Category: Reviews

The COVID-19 pandemic disrupted the landscape of several industries, particularly the healthcare sector. Besides the strain on supply chains to keep vaccines in stock, the combined effects of soaring inflation, looming recession, and severe understaffing create very turbulent weather for the healthcare system.

Although still reeling under the effects of the pandemic, the healthcare system has braced up for new modes of operation powered by technology. 2023 is expected to usher in new trends for the healthcare industry, all aimed at optimizing the quality and cost of healthcare. Healthcare is a critical industry, and the market is expanding rapidly. The value of the national healthcare industry is expected to reach 6 trillion USD by 2026. This vast market has a lot of healthcare opportunities waiting to be maximized. 

We have curated a list of healthcare trends that providers and consumers can expect in the coming year.

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Trends to Watch for Healthcare Providers in 2023

The foreseeable future of healthcare and, indeed, all sectors of the economy is inexorably linked to technology. Healthcare is becoming more and more value-based, and the emphasis is shifting from the health organization to patient care and health outcomes. As such, the major technological advancements in the health industry are in line with value-based care methods.

Technology provides more efficient ways of healthcare delivery, therapy care, optimizing healthcare policies, and improving the delivery of healthcare services, minimizing the need for physical health consultations with medical professionals. 

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By highlighting leading trends in the healthcare industry, physicians and users are better prepared to take advantage of the industry dynamics for optimal benefit on both ends.

Here are trends to be looked out for in the healthcare industry in 2023. 

The Use of Artificial Intelligence

AI has significantly improved every field of the economy, and healthcare is not exempted. This trend is expected to rise and evolve in 2023. Many healthcare organizations already use chatbots on their websites to support customers. But beyond that, more specialized uses of artificial intelligence, such as image and pattern recognition, natural language processing, and machine learning, will be used to diagnose and treat health problems.

AI solutions help healthcare facilities cope with staff shortage problems. With powerful algorithms, data from patient scans can be processed at breakneck speed. AI enables the automated detection of diseases through computed tomography scans. This is a huge leap for the healthcare sector. AI will be used in drug discovery and analysis, conducting and assessing clinical trials, and analyzing radiographs.

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Accuracy improvements for AI technologies are ongoing; thus, AI is mainly used as a medical assistant rather than as a major diagnostic in delicate situations. The use of AI in drug manufacturing began in 2020 but will become an even bigger trend in 2023. AI and machine learning algorithms will be used for drug testing and iterations, shortening the time and cost of scientific research and discovering groundbreaking medical solutions.

Apart from the healthcare sector, AI is employed in health management operations, such as claims processing for health insurance and maintaining and updating patient and personnel information in medical facilities. In 2023, artificial intelligence in healthcare delivery will become more mainstream. The healthcare AI market is projected to reach nearly $188 billion by 2030, with an annual compound growth rate of 37% from 2022 to 2030.

Personalized Healthcare and SDOH Considerations

The demand for value-based healthcare has increased the use of personalized services for healthcare delivery. Take, for example, the concept of precision medicine, where medications and treatment routines are specially designed to suit a certain demography of patients. These groups of patients are selected based on some social determinants of health (SDOH), like age, genetics, lifestyle, locality, or risk factors.

It is understood that environmental conditions have an undeniable impact on quality of life and health. SDOH, such as food and job insecurity, housing, childhood development, social structure, discrimination, access to education and prompt healthcare, etc., lead to health disparities. Precision medicine incorporates all these factors for individualized diagnosis and quality care delivery.

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The use of precise informatics technologies is vital for effectively collecting and processing patient data and prescribing proper treatment. These technologies will be secure — not compromising data privacy, and interoperable — usable by different care providers.

The market for informatics technologies is expected to reach $5 billion in 2023, no small feat for a virgin field of innovation.

One such solution is PULSE (Patient Unified Lookup System for Emergencies), an online store where patient health information can be accessed to enable efficient treatment. PULSE first arrived on the scene in 2018 but has greater applications in 2023 and the coming years.

As an advancement, personalized healthcare will incorporate genetic information from the patient, including medical history. This will assist care providers in determining the efficacy of specialty drugs and their potential side effects. This application may include the use of artificial intelligence.

The trend will also have applications in traditional healthcare services. Patient preferences will be considered in curating treatment plans, paying attention to and adjusting to the peculiar situation of the patient as much as possible.

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Retail Healthcare

The number of health services traded off the counter in retail shops is expected to soar in the coming year. Hospitals are already severely understaffed and so may be under enormous strain to provide quality services for a swarming number of health cases. 

The move towards retail health care is a more convenient and cost-effective method of health care delivery and a more viable option for both the patients and caregivers, considering the current economic terrain. It can complement primary healthcare, is more convenient, and lower the costs of care delivery.

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Some major retailers like Walmart and Amazon already sell medical supplies like blood and pregnancy test kits, vaccines, and injections. Still, there are specialized retail stores for nearly all kinds of regular health solutions like laboratory testing, pediatrics, first aid, weight management, eye and ear care, immunizations, mental well-being, etc. 

Retail health systems supply instant, off-the-shelf services already packaged. Thus, they are more timely, dispensing with the need for physical health appointments, more pocket friendly, and more easily accessed.

Internet of Medical Things (IoMT)

The IoMT is not about to be invented in 2023; it already exists. However, it will grow dramatically in the coming years because it complies with the trending value-based care models, not to mention its efficiency and cost savings.

IoMT includes wearable medical devices that monitor patient health conditions in real-time, eliminating the stress of continuous physical testing, thus improving the patient experience. In addition, these devices are reliable, easy to use and provide timely information.

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In 2023, the use of wearable medical devices is predicted to increase. Test kits like Accu-Chek, for example, used by diabetic patients at home are basic but have laid the foundation for remote patient monitoring. More advanced wearables that will storm the health market include smartwatches that can conduct highly specialized scans such as ECGs, smart textiles for monitoring blood pressure and cardiovascular health, and smart gloves to assist patients with disease-related tremors.

These wearables incorporate sensors and processors to obtain and analyze the data within themselves, dispensing with the need for a journey between cloud and data source. This is known as edge computing technology and has several benefits for the patient, including speed and data protection.

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With these wearable devices, patients can constantly be updated on their health condition, and healthcare providers can monitor them remotely using the internet of medical things (IoMT). Indeed, these sophisticated software and hardware technologies provide individualized real-time data and enable disease prevention, patient engagement, and improved clinical outcomes.

Research is currently underway to develop wearable devices suited to identify and track mental health illnesses.

The market for patient engagement technologies such as medical wearables, telehealth content, and applications – is projected to stretch to more than four times its 2021 size, from $13.42 billion to $74.28 billion by 2030.

Telehealth

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Communication technology has come a long way, from the use of landlines to more diverse digital and universal options. In fact, communication now successfully powers an aspect of health. Telehealth or digital health uses the features of modern communication devices to provide medical services. Calls, video calling, and streaming services are ways communication can be used to provide remote monitoring and other healthcare services.

Telehealth practice gained prominence in 2021, and with the advent of 5G technology, it has soared much higher. Many traditional health organizations have incorporated it into their service delivery through online consultations, diagnoses, and prescriptions. Virtual healthcare organizations also use the possibilities of telehealth to sponsor their services. 

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Telehealth is forecast to continue its upward journey in 2023 to global acceptance and gradually displace traditional in-person consultations as the years go by. China, a leading global economy, is working to incorporate telehealth in 70% of public hospitals by the end of 2023. Also, the US, a global leader in telemedicine, plans to make more off-site healthcare services available to patients. This will alleviate the financial strain healthcare providers face in running medical facilities and thus lower the cost of primary care. Other countries are also embracing the tide of telehealth and developing customized policies to deliver prompt, virtual care services as the needs arise. It is predicted that the 5G wireless technology will accelerate the universal adoption of digital health in 2023 and beyond. 

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Robotics

Robots are gradually altering the dynamics of healthcare. When accurately programmed, they can efficiently replace human efforts. As a result, medical personnel is relieved from routine tasks to focus on research and other non-delegable activities. 

Robotics have been used over the last few years in healthcare facilities for assisted treatments and handling specific tasks like monitoring vital signs, computing patient health information, transportation of food and medical supplies within the facility, and patient care.

Where there is a risk of contracting a disease, e.g., from isolation rooms or operating theaters, ultraviolet “C” light (UV-C) autonomous disinfection robots are the safest options for disinfecting such places.

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Robots have near-perfect precision in laboratory testing, from locating the needed blood vessel to transporting, analyzing, and storing test samples. In recent years, there has been a creation of surgery-performing robots. They assist in surgery, helping surgeons to make more precise decisions. Some advanced robots even perform accurate surgery on delicate organs of the body. Certain robotic assistants help in administering physiotherapy to people with paraplegia.

Nano-medicine also involves microscopically tiny, self-replicating robots, called xenobiotics or nanorobots, injected into the bloodstream to detect and fight against genetic, oncologic, and auto-immune diseases in human cells such as tumors.

2023 will usher in greater research and advancement in the application of robotics and healthcare, for example, using drones for health supplies. The market size for medical robotics is predicted to reach $14.4 billion in 2023 and $44 billion by the decade’s end.

Interoperability and Data Sharing Among Healthcare Entities

Healthcare organizations store the health information of patients in a database. The process of obtaining such information can be lengthy and tedious, and if there is ever a need to change healthcare providers, the patients might need to repeat the process as often as possible.

However, with the interoperability and data sharing trend, medical care facilities can access a single universal database for all patient health information. This information will be stored as a detailed medical image of the human body, which various specialist hospitals can then interpret according to their specialties. This imaging method is more accurate than symptom reporting, which may exclude the core reasons for the disease.

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This idea of data-sharing accords with the shift towards a value-based care delivery model, altering the approach of medical facilities from an organization-centered to a patient-centered one. 2023 will see an increase in healthcare interoperability projects with heavy government and investor support, and non-compliant facilities will find themselves swimming against the tide.

Innovative technologies like platform integration, multi-cloud computing solutions, data fabric architecture, and APIs will be deployed to enhance the interoperability of healthcare systems. Blockchain technology will also be increasingly adopted for storing health information between now and 2030.

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Smart Implants

Implants have been a very helpful solution in regenerative and rehabilitative medicine. The limitation, however, often exists in the problem of compatibility of the implant with the patient’s body system. 3D bioprinting technology is the future of creating biocompatible implants. More healthcare manufacturers will engage this technology in 2023 to provide solutions for different cases, e.g., new materials and more sophisticated prosthetics. This portends greater efficiency and even a cure for many otherwise incurable disabilities. 

The 3D bioprinting technology offers prospects for reduction of time and cost using traditional prosthetics. It provides implants and bionic prostheses, customized, functional, and seamlessly integrated with the body system of each patient. The bioprinting market is expected to rise to an excess of $6 billion by 2027.

Neural implants, too, will be used more widely in the company year, even as Healthcare companies like Neuralink have developed brain chips to revive or accelerate static brain functions. These implants are expected to enter the market in 2023. Many other health manufacturers are working to develop and improve medical implants for various medical needs, including paralysis and blindness. In 2021, a smart microelectrode array was implanted into the visual cortex of a visually impaired patient, and there was a significant improvement in vision. Thus, the technology of intelligent implants offers hope for medicine in 2023.

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Blockchain Technologies For Data Protection

These technological advancements mean one thing for patients — more personal health information will be given away. As a result, new trends for data privacy and security measures will become a top priority for healthcare leaders to prevent the breach of patient and personnel data.

2023 will see an investigation into the use of data protection trends like blockchain technology. The blockchain is a peer-to-peer decentralized distributed ledger technology that records digital transactions in transparent, secure, and non-duplicable cryptograms, with the key given to the initiator of the transaction.

Blockchain technology allows patients to share and sell their health information without compromising data privacy. Overall, blockchain can improve the efficiency of workflow processes in many health systems. Every new technological advancement poses peculiar concerns for data protection. To avoid the risk of breaches, companies adopt tighter measures to ensure data privacy and security plans while handling patient data.

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Apart from blockchain technology, some other methods of data protection for health companies:

• Regular cybersecurity checks according to the rules of HIPAA or other applicable health laws
• Outsourcing to trusted and law-compliant third parties
• Conducting training to educate workers on data and cybersecurity principles, risks, best practices, and consequences of non-compliance
• Maintaining standard access controls, with scheduled checks for spyware malware
• Hiring a qualified in-house IT manager

VR, Augmented, and Mixed Reality in Healthcare

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This list of trends will be lacking if we fail to mention the use of Metaverse technologies in healthcare. Augmented reality (AR) and virtual reality (VR) are up-and-coming technologies with huge potential for the healthcare industry. The market value of AR and VR technology for healthcare in North America is forecast to reach $4.64 billion by 2025.

Virtual reality technologies are paving the way for unprecedented improvements in medical diagnosis and education. By placing students or practitioners in simulated environments with peculiar use cases, they can demonstrate analytical thinking and skills in practice, gathering experience for real-life situations.

Augmented reality (AR) incorporates virtual objects into the real world. Thus care teams can interact with digital information simultaneously while attending to work in the real world with navigation tools such as voice command.

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Mixed reality affords real-time association of computer-generated objects with the physical environment. With digital avatars, exact virtual representations of real-life places and objects can be made. Thus, it is possible to move across both worlds seamlessly.

MR technology is a trailblazer in that it offers prototypes for medical design and engineering. The digital avatars replace the need to have physical samples and prototypes for laboratory work. Instead, the MR technology is used to simulate an accurate virtual care setting where testing can be done.

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Another application of the MR technology that has yet to be fully explored but is expected to become more mainstream in 2023 is remote surgical operations. With the use of digital twins or avatars, healthcare professionals can carry out operations across different locations. This technology is time and cost-efficient. And help in the development and iteration of healthcare products and services. 

Conclusion

The healthcare ecosystem keeps evolving, powered by new technologies such as AI, robotics, wearables, and telehealth systems. The shift towards value-based care will greatly influence industry trends in 2023 as health providers and the government seek to improve healthcare quality at reduced rates. Trends like virtual health systems, Robots in health, AI in healthcare, and blockchain technology will redefine care settings but also bridge the current health disparities.

Patients and the care team should know current trends to make the most of them. This article has provided actionable insights for industry leaders. With these tools, doctors and clinicians can promptly identify a patient’s health challenges and deliver care centered on these challenges. Also, patients seeking care can know the extent of options available to them without expending all their resources on unnecessary healthcare procedures. 

Proper record keeping of Electronic Health Records (EHR) and Electronic Medical Records (EMR) has emerged as a crucial bedrock of healthcare delivery. Healthcare facilities are increasing their spending in this area. A survey suggests that between $15,000 and $70,000 is spent per provider to implement these systems. 

To underline how big it is, the sector was valued at $29 billion in 2021 and is projected to reach $47 billion by 2027. About 35 years ago, before the Internet became more mainstream, exchanging medical data had to be done manually. A patient had to take hard copies of laboratory tests to doctors to interpret and make prescriptions. 

The prescriptions were then handed to a pharmacist to supply the drugs. The time and efforts expended resulted in slow medical care. Medical researchers had to wait days and even weeks to get research results from others in their collaborative work. 

With the incursion of technology in the health management sector, there arose a need to facilitate data sharing from these records for proper healthcare administration. How do they ensure this data is secure, reliable, and available? The answer was interoperability. 

Picture this: prescription records, blood work, lab tests, billing system, doctor’s appointment schedules, etc., working in sync in a rapid and all-in-one suite. Yes, that’s what interoperability in medical records means. Rather than the cumbersome and mechanical process of moving paper-based reports from one medical institution to another, this information is gathered digitally and available to doctors and patients. 

The need to ensure a seamless and secure transfer of health records between different interfaces prompted the Non-governmental Organization, HL7, to develop standards to package, transfer, retrieve, and interpret health data. With more innovation, HL7 has produced other standards, chiefly HL7 V2 and First Healthcare Interoperability Resources (FHIR).  

This article discusses HL7 vs. FHIR, bringing out their similarities and differences. Ultimately, you should be able to choose which is suitable for your needs. 

And if you’re ready to optimize your healthcare interoperability with the right software development? Contact us today to discuss your needs and how we can help.

Is FHIR a Component of HL7?

HL7 introduced FHIR as part of a new variant of health information standards embedded in the HL7 V2 and V3 and CDA. Its introduction was hailed by many key stakeholders and was implemented on health apps such as Apple’s health app. 

FHIR presents healthcare systems with greater interoperability opportunities using RESTful web services. In addition, mobile apps and Electronic Health Records can utilize FHIR in their operations. 

Before FHIR was designed, other versions of HL7 were known as V2, V3, and HL7 CDA. 

The HL7 V3 was designed to improve the interoperability of V2, using a context-rich format to ensure a smooth information interchange. It also uses the shared model and Reference Information Model. Unfortunately, the V3 is too complex to understand, which has hindered most healthcare institutions’ adoption. It is also not backward compatible and is only mentioned when Clinical Documentation Architecture (CDA) is implemented. 

FHIR, as the latest in the HL7 family, combines the best features of V2 and V3. FHIR is composed of modules known as ‘resources.’ These resources make resolving administrative problems in data transfer seamless. 

Is the HL7 Standard Being Phased out?

The short answer is no. We will provide some context to this inquiry. 

HL7, or Health Level 7, is a body of rules which controls how health management systems relate with each other regarding data sharing. 

HL7 operates based on protocols. Some of these include an event-driven protocol, which creates the link for data exchange upon the happening of an event. Such an event can be when a prescription is made to a patient or when a piece of billing information is sent to the patient.

The application-to-application protocol happens when two separate apps need to share information. For instance, a patient portal and a wearable device. Other protocols include OSI layer 7 protocol which enables software based on HTTP and SMTP to function seamlessly. There are also Standard and Exchange Protocols. 

Before its introduction, health institutions had to build different interfaces for different data types. For instance, there was an interface for clinical documentation, prescriptions, laboratory results, etc. However, there were no means to share these data simultaneously. Also, there were no standards to regulate the kind of data and the formats of these data when shared. 

Since its introduction, health institutions have adopted it, making healthcare administration easier and more flexible.  Other reasons why HL7 integration is key for health institutions include the following. 

A hospital usually has many departments, from radiology, gynecology, laboratory, and clinical records. HL7 ensures that patients’ information concerning any department is collected in one place. 

Also, this information is available to any of the other departments at the tap of a button. Doctors can quickly check on the patient’s care history to diagnose quickly. The ease of transferring data and readability of these data makes HL7 a cornerstone of the modern health sector.

Errors in collecting medical information are a leading cause of death. In a survey of 22,889 patients, 21% reported errors in their medical records. These errors contribute to misdiagnosis, often leading to serious harm and, in some cases, fatality. 

Using HL7 standards in collecting medical data reduces the incidence of errors due to the information being collected and available at one point. There is no need to enter the same information manually every time. Thus, medical professionals can easily detect any error. 

Due to the ease of data sharing and integration with other relevant information, HL7 is a platform for developing other solutions. For instance, mHealth apps and wearable devices help to collect up-to-date information about a patient’s health status. 

These digital solutions monitor heart rate, sleep patterns, and prescriptions. HL7 allows doctors to access this information and assess it on the spot. With HL7 in play, the possibilities in digital health technologies are limitless. Patients can monitor their health while doctors have less workload with the help of technology. 

HL7 standards are not limited to one country. Instead, it is a global standard for medical institutions. Through HL7, institutions in different countries can collaborate on medical research by sharing medical records. For instance, at the height of the Covid-19 pandemic, doctors worldwide shared ideas and information about getting a cure for the disease. 

HL7 was possible, as the lockdowns ensured that paper-based data sharing would have been complicated. 

Are you interested in exploring the benefits of HL7 and FHIR for your healthcare organization? Let’s discuss how our software development solutions can help you achieve your goals and improve patient care.

One demerit is confusion and misunderstanding of abbreviations. Because the system reads various data from multiple sources, there may be a need for better communication about what a particular abbreviation stands for. Different clients define some words differently, which HL7 may not know. This may lead to errors harmful to the patient. 

Another drawback is the time draining involved in implementing it. As a highly complex process of integrating many interfaces, it takes more time in deployment, as you might hope. 

Despite the innovative features of FHIR, HL7 V2 remains relevant and is still widely used in the EHR sector. FHIR is regarded as an emerging standard by most healthcare institutions; hence HL7 is still dominant. 

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To gain a deeper understanding of HL7 FHIR integration, tune in to our CareMinds podcast featuring Redox specialist Nick McKenzie. In the episode, Nick draws an analogy between EHR integration architecture and building a house, where a team of professionals work together to create a cohesive system. As a translator between clinicians and technical teams, Nick shares his fascination with product management and helping to design and execute solutions.

Watch other episodes as well!

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How Do the Two Versions of HL7 V2 and FHIR Differ?

Before 1987, the seamless flow of information and patient data between healthcare institutions, laboratories, pharmacies, and payment systems was nonexistent. At that time, these organizations had to rely on semi-automatic communication systems such as fax machines to send key data between them. 

Enter HL7, and the first standards for health records sharing were birthed. With time, HL7 has released the HL7 V1, V2, and V3. However, V2 is still widely used by a lot of health practitioners. 

Both share key similarities. After all, they are products of the same organization. Chief among the similarities is that they are built around reusable data packs. This means that developers already have help with existing data packs contained in the protocol. They simply modify and build on it instead of writing it from scratch. 

Another similarity is the emphasis on complying with forward and backward compatibility rules. This implies both can parse data on older and newer versions without glitches. With this, there is reduced cost of infrastructure and implementation.

Deciding between HL7 Vs. FHIR: Which One Should You Select?

Choosing between FHIR and HL7 to standardize your health management records depends majorly on the kind of existing system you have. 

FHIR is more modern and an improvement on the HL7 V2 and V3 that came before it. FHIR also infuses more contemporary technological advancements in making health information seamless. 

One key feature of FHIR is its RESTful API, which, instead of the slow and cumbersome point-to-point interaction style of HL7 V2, uses a one-to-many point style. This approach shortens the time needed to integrate other data partners such as pharmacies, Electronic Health Records, and billing systems. 

To help you understand better, we will look at the advantages of FHIR over HL7. 

Developers who use the FHIR standards praise it for facilitating the creation of systems to suit the client’s needs. RESTful API also means that FHIR can work with mobile apps, critical to rapid information sharing. In addition, FHIR displays its flexibility by supporting all the data formats used in standardization.

All the resources needed to implement the FHIR standards are freely available. The implication is a low cost of implementation for clients. 

Different from the previous standards, FHIR is less complex. Interoperability is more achievable using FHIR due to covering more areas. Most developers are familiar with open Web technologies such as JSON and RDF data formats used by FHIR. It means they spend less time getting to grips with the system, thereby lessening the learning curve.

There is no fear of patients’ data getting into the wrong hands. In addition to making data sharing easier, FHIR provides adequate security to the shared data. The patient and the administrators have greater control over the information they give out. 

Ready to choose the right standard for your healthcare interoperability needs? Contact us to learn more about HL7 and FHIR and how our software development services can help you implement the right solution.

FHIR vs. HL7: Core Differences Compared & Reviewed

Features	HL7	FHIR
Architecture	Based on messages	Uses RESTful API
Extensibility	Doesn’t support interoperable extension	Supports extensions to suit the system
Security	Transport layer	Transport layer and SSL
Platforms	EMR, EHR	EHR, EMR Mobile apps, wearables
Use cases	Medical Records management	Interconnectivity with Medical IOT devices and mobile health apps
Reliability	Unreliable due to multiple options columns	Specific resources make it reliable
Human readability	Doesn’t support readability	Has support for human-readability
Accessibility	Restricted access	Free access

Let’s see how FHIR and HL7 compare to each other. 

HL7 infrastructure is based on messages, fields, and records. FHIR uses RESTful API as its base. The RESTful API ensures that it can handle large amounts of data from disparate interfaces without any bottleneck in implementation. 

Healthcare data systems are dynamic and interchanging. Constant changes to medical data and technological advances make the ability to add extensions crucial to any information standards. Unfortunately, the extensibility of HL7 is not interoperable. In contrast, FHIR is interoperable and supports adding extensions to suit the system. 

Securing patient data using HL7 is through transport layer security, while FHIR is through transport layer security and SSL. In addition, the security architecture of FHIR has an authorization protocol in which a secured data exchange between healthcare professionals and patients can occur. 

HL7 was not created with current technological advancement in mind. Hence its platforms are limited to EMR, EHR, and HIS (Health Information System). A much more modern FHIR supports all the above platforms and adds mobile apps and wearable IOT-enabled devices as part of its platforms. 

FHIR is more advanced and isn’t only used for Electronic Health Records and Electronic Medical Records alone. FHIR allows the free flow of information from all sources and partners. 

Wearable devices and mobile health apps also have access to relevant health records using FHIR. A great example is the Apple watch, to monitor heart rate and blood pressure. The data collected is shared with the doctor for effective health monitoring. HL7, on the other hand, only supports the exchange of health records among health institutions. 

The multiple option columns impact HL7’s reliability. FHIR is more reliable as it has specific resources to handle specific data. 

HL7 V2 does not support human-readable versions of the shared data. For FHIR, it is a mandatory requirement that all data shared must be human-readable. 

Unlike HL7, FHIR is free to use for developers. You don’t need a login, nor are there any exchange protocol restrictions. In addition, due to the use of RESTful API and other open web technologies, developers find it easier to use FHIR, unlike HL7. 

Wrapping up 

Implementing the right standards in Medicaid is hugely important in quality and rapid healthcare delivery. Deciding between HL7 and FHIR may be complex due to their similarities. HL7 V2 is still commonly used among practitioners in the healthcare industry. 

However, FHIR promises to make data exchange more seamless and easily accessible to patients and doctors, especially with its ability to sync with mobile devices and mobile apps. Moreover, its more modern outlook and incorporation of contemporary web technologies bring limitless possibilities. 

The good thing is you don’t have to make this decision alone. You need a trusted partner like The APP Solutions to help you develop an action plan to implement the standardization of your health records.

When I’m writing these words, I have a flashback to gloomy November days in 2008. Several months earlier I graduated from the National Technical University of Ukraine with a bachelor’s degree in Power Engineering. 

For me, the future was uncertain, and I didn’t even dream of becoming a data scientist, nor knowing much about such a profession. I think you feel the same. But, unlike me in 2008, you know that you want to learn data science and have numerous opportunities to choose. 

In those days, I wished to find a trustworthy guide on data science courses that would tell me how to start my career and what subjects I should study during my learning journey. Over ten years ago, there was little information about data science courses on the Internet. In contrast, in 2020, we have too many online programs to enroll. 

Like many other data scientists, I took online courses to dig deeper into the subject. And it took me a while to realize which program is worth my attention or that is just a waste of time. 

Now that I have years of experience in data science, I see what courses are worth taking. I also see that the demand for data science specialists outstrips supply.

So, I decided to write this article about online courses for data scientists to help beginners in data science. I hope it will help you to overcome the struggle of finding decent online programs. 

To help you become a job-ready specialist, I gathered certification programs offered by private companies, such as Google and IBM. I also listed online programs on data science from Coursera, a learning marketplace. 

But before digging into the details, let’s answer a simple question. Do you need to look for a data science online course or a certification program?

Data Scientist Certificate Programs vs. Certification: What is best for you? 

Words “Certificate” and “certification” sound almost similar. But there are differences in their meaning, as well as learning outcomes. 

So, let’s set the record straight. 

A certificate program includes one course or series of lectures from education institutions. Such programs will give you specific knowledge in a particular field. After you finish a learning program, you receive a certificate that demonstrates your understanding of content from a learning institution. 

You’ll need to take a certificate program if you have little to no experience in the subject. But owning a certificate doesn’t mean your knowledge and skills meet industry standards. 

A certification is a more valuable achievement because it includes an experience and education component and requires passing an exam. In plain terms, a certification determines whether you are qualified enough for the certification standards, determined by independent certification bodies.

To get a certification, you need to have a solid background on the subject and meet standard-setting organization requirements. In some cases, to meet changing environments, you need to renovate your certification every year. 

In a nutshell, if you are looking for knowledge and skills training, you need a certificate program from a university. In contrast, certification evaluates your current knowledge and skills against predetermined standards set by independent certification bodies.

Best Data Science learning path: where to start 

Before discovering online data science courses, I want to give you a picture of what your working routine as a data scientist will look like.  

The work of a data scientist includes several stages. First, you’ll need to develop a statistical model that will analyze the dataset. Then, you’ll apply statistics and mathematical techniques to interpret your result, and finally, visualize it in an understandable format. I described a small part of what data scientists do, but it is enough for you to get a basic understanding.  

To prevent you from meaningless hours of searching for where you should start your learning path, I offer my own. The provided pathway shows how I learned data science foundations and moved from one milestone to another to become a data scientist. 

Programming: Python and R 

Both Python and R are programming languages I use most often. I recommend starting from Python, a high-level programming language because it is beginner-friendly. Python fundamentals, such as data structures and data analysis, will help you develop data analysis models. 

You can then start learning R. This programming language for static computing will help you develop statistical software and data analysis. Moreover, R is also an interactive environment for data clearance, transformation, wrangling, and visualization. 

Statistics and mathematics

Both statistics and mathematics form the backbone of data science. You’ll need to learn about the main techniques to interpret the results you generated through analytic data models. Such methods as confidence intervals, hypothesis tests, probability theory, likelihood, and common distributions will help you with this task. 

You’ll need to learn about such concepts as inferential statistics and exploratory data analysis (EDA). 

Data visualization

One of the data scientist’s responsibilities is to extract business insights from raw data. But in most cases, such insights look like a large table of numbers no one except you can read nor understand. To make data analytics results more user-friendly and communicate them to a general audience, you must learn about different data visualization methods.

The next level for a data scientist is creating more sophisticated data analytics models for artificial intelligence and machine learning applications. If you are courageous enough to try it, here is what you’ll need to learn. 

Linear algebra 

Linear algebra is a part of mathematics, applied across disciplines, including data science. With a solid background in linear algebra, you’ll better understand machine learning and deep learning algorithms. Linear algebra fundamentals include such concepts as matrices and vectors, matrix operations, matrix inverse, and others. 

As for linear algebra applications by data scientists, we often use Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) for image decompression and reducing the dimensionality of the data sets. 

Machine learning 

Machine learning is a more narrow field of data science that deals with programs that learn from experience. To create such a program, you must be aware of the main types of machine learning algorithms. By learning style, ML algorithms could be supervised, unsupervised, and semi-supervised. Then, you’ll need to know about methods to create one or another algorithm. 

Deep learning

Deep learning, also known as deep structured learning, is a part of a broader family of machine learning methods based on artificial neural networks. The adjective “deep” in deep learning means that such algorithms leverage multiple layers in the network. 

Knowing how to develop deep learning algorithms can create sophisticated systems like image recognition software, deep fakes, and text-to-voice tools.  

I have just given a quick overview of an approximate learning path for data scientists. But consider that milestones are due to change depending on your preferences and your employee requirements. 

Now that you know how you can begin learning data science, let’s see where you can receive the necessary knowledge and master your skills.  

Training in data science: What are the best courses for data science?

Learning from industry leaders in data science is a more straightforward way to receive theoretical and practical knowledge. For this purpose, I gathered data science courses from IBM, Google Cloud Platform, and Microsoft. I also added data science learning materials from KDNuggets, a data scientists learning community. 

So, let’s review the best data science learning programs one by one. 

IBM Data Science professional certificate

You might know IBM for creating the first electronic data processing machine in 1957 and SQL, a programming language, in 1974. However, IBM does not limit itself to that.  

In 2010 IBM launched Big Data University, a community initiative for providing comprehensive learning paths in data science, big data, and analytics. The mission of this initiative is to nurture a community of skilled data professionals.

Big Data University offers the Data Science Fundamentals Learning Path, free online courses for all applicants. The learning path will prepare you for IBM Data Science Foundations Level 1 and Level 2. 

These courses are short by design and follow the “5 x 5” rule. 

• The learning path includes 5 courses 
• Each course is about 5 hours
• The course consists of 5 modules
• Each module has 5 videos that last for 5 minutes 

Data Science Fundamentals Level 1 and Level 2 includes four main courses: 

• Data Science 101 (3 hours) 
• Data Science Methodology (5 hours)
• Data Science Hands-on with Open Source Tools (5 hours)
• R 101 (5 hours) 

As a result, you’ll earn a Science Foundations Level 1 badge or Data Science Foundations Level 2 badge. You’ll also get solid knowledge in concepts and tools to prepare them for first job and entrepreneur opportunities.

Data Engineer and Data Science courses by Google Cloud Platform

Consider Data Engineering on the Google Cloud learning path if you want to learn about big data, AI, and ML. 

All courses are available on Coursera, an online learning platform. The platform offers a 1-month free trial for this course. After the trial ends, you’ll pay a $49 monthly subscription fee to continue learning.  

The learning path consists of five courses:

• Google Cloud Big Data and Machine Learning Fundamentals
• Modernizing Data Lakes and Data Warehouse with Google Cloud
• Building Batch Data Pipelines on Google Cloud
• Building Resilient Streaming Analytics Systems on Google Cloud
• Smart Analytics, Machine Learning, and AI on Google Cloud

Each course takes about 12 hours to complete. And at the end of each course, you will earn a Certificate upon completion. 

Google Cloud Platform also offers an on-demand introductory course Data Engineering on Google Cloud. The course length is 4 days and covers such topics as:

• Building data processing systems on Google Cloud Platform
• Leveraging unstructured data with Spark and ML APIs on Cloud Dataproc
• Implementing autoscaling data pipelines on Cloud Dataflow for process batch and streaming data

The course includes presentations, demonstrations, and hands-on labs.

Google also hosts online courses focused on AI and its application in data science. To find out more about Google AI courses for data scientists, visit Google AI Learning section. 

If you want to get a Google certification, check out the Professional Data Engineer Certification page. In short, the requirements are the following: 

• 3+ years of industry experience 
• 1+ years designing and managing solutions using GCP

The certification exam takes 2 hours to complete and costs $200.  

Azure Data Scientist Associate Certification by Microsoft

Microsoft doesn’t need any introduction. The company’s Azure platform is a top-rated tool among data scientists and data engineers. To train professional data scientists, Microsoft offers a Data Scientist certification path that includes three levels of complexity: 

• Fundamentals
• Associate
• Expert 

If you are new to Azure, you can begin your path with Microsoft Certified: Azure Fundamentals certification. If you are already familiar with Azure, try yourself with Microsoft Certified: Azure Data Scientist Associate certification. 

To pass the exam, you have two options. You can learn theory online (free of charge) or get an instructor who will prepare you for the certification. The costs of guided courses and certification exams are calculated depending on your location. 

Online courses, boot camps, and certifications from KDNuggets

KDNuggets is one of the most influential web-based communities of data scientists, created by Gregory Piatetsky-Shapiro. Gregory is one of the top authorities in Big Data and Data Science. 

The website offers various online degree programs, boot camps, certificates, and certifications. Let’s check them out. 

Best  Data Science Online courses 

KDNuggets online data science courses have a wide range of topics. They range from basics in Data Science for working professionals looking for a career change to topic-narrow classes, such as machine learning, data and text mining, and the semantic web. 

Thus, the KDNuggets list of online courses is the legit starting point of your career as a Data engineer. It includes various programs approved by KDNuggets to skill-up Big Data Analytics, big data integration, and visualization skills. 

You got it right. The platform offers just one Data Mining Course, created by Dr. Gregory Piatetsky-Shapiro, the platform’s founder, and Prof. Gary Parker from Connecticut College. 

However, there are courses provided by top-rated education institutions. Berkley, Boston University, and Illinois Institute of Technology, to name a few.

KDNuggets also lists online programs from online data communities, such as DataCamp and Guru99.  

Best Data Science Degree programs 

Data Science Bachelor degree: 

• Colorado Technical University BS in CS/Data Science
• Ottawa University BS in Data Science and Technology
• Thomas Edison State University (TESU) BS in Data Science Analytics 
• ASU Business Data Analytics (BS)

Data Science Master degree:

• Capella U. MS in Analytics  
• Colorado State University Global AI and Machine Learning Master’s degree
• Colorado State University-Global Campus Masters of Science in Data Analytics 
• Colorado Technical University Master of Science in Computer Science
• Moravian Graduate Online MS in Predictive Analytics 

Best Online Data Science training, courses,  and classes,

• 360DigiTMG Practical Data Science program 
• Big Data University 
• Booz Allen Hamilton “Explore Data Science”
• Business Toys Full Stack Data Science Professional Program
• Class Central Data Science and Big Data courses 
• CMU Open Learning Initiative 
• CourseBuffe 
• Coursera: Machine Learning for Everyone with Eric Siegel
• DataCamp
• EMC Data Science and Big Data Analytics 
• Galvanize 13-week online program 
• Guru99
• InventaTeq Data Science Certification Course
• Jigsaw Academy
• JMP Statistical Thinking and Problem Solving – free online course
• LearnAnalytics India
• MIT OpenCourseWare : Data Mining
• OpenClassroom Machine Learning Course
• Oxford Machine Learning (and Deep Learning)
• Springboard Intro to Data Science 

Best Data Science Boot camps 

Data science boot camps are immersive programs that take from 4 to 12 weeks to complete. The main benefit of boot camps is an excellent range of activities. The studying process includes lectures, workshops, and even work on a real-life project. To participate in a boot camp, you need to send your application and pay for the program beforehand. Thus, carefully check out the submission dates. Besides this, you also should meet the organizers’ requirements. 

Basic requirements for participating in Data Analytics boot camps are 

• Advanced English skills (at least C1 level) 
• Good abstract thinking 

So, technical skills and formal education are rather “nice to have” than “mandatory” requirements. Some boot camps are accredited by educational institutions, include in-person instruction, and even provide employment support.

Here are some data science boot camps KDNuggets recommends:

• BaseCamp
• Data Science Dojo
• DataScientest
• General Assembly 
• K2 Data Science Bootcamp
• Metis Data Science Bootcamps
• NYC Data Science Academy
• Peaks Academy Data Science boot camp
• Principal Analytics Prep 
• The Data Incubator 
• The Dev Masters
• Thinkful Data Science boot camps

Best Data Science Certificate and Certifications

Data science certificate programs give fundamental knowledge and suit beginners the most. To graduate from a certificate program, you need to learn the material, answer test questions, and receive a certificate of passing the program. 

You can choose between instructor-led or self-paced certificate programs. The difference between them is that instructor-led programs are taught by an instructor via live videos, while self-paced include only learning materials and an exam at the end. 

To receive a certificate in Data Science, chose among the following programs: 

• Machine Learning for Everyone with Eric Siegel 
• Data Science Dojo and the University of New Mexico Data Science Certificate 
• Data Science Certificate

Certifications include training sessions followed by an exam. Passing a certification means you meet high industry-related standards and creates more significant opportunities to enrich your career. 

Before passing the certification exam, the education provider offers learning about data science both offline and online. 

For starting your data science career, consider the following data science certification online: 

• Artificial Intelligence Engineer Certification
• Data Science Training and Certification  
• Certified Analytics Professional
• Data Scientist Certifications 
• Artificial Intelligence Certification

 Which is the best data science course in Coursera?

Coursera, an online education marketplace, offers numerous options for your data science learning path. You can choose from short guided projects that length several hours to courses, professional certificates, specializations, and master degrees that take years to learn. 

Coursera offers free weekly access to online courses and learning materials. In this way, you can evaluate whether the program is worth your attention before paying for it. 

But the main challenge is to find the best program among 1888 courses on data science. To save you time, I sorted data science online courses on Coursera by length. Then, I searched for the top 3 most popular online data science courses from each section, and here is what I got:   

3 Best Data Science guided projects 

Guided projects are the best way to learn job-relevant skills within several hours. During a guided project, you receive support from a subject matter expert. One will guide you through the process of completing the task with step by step instructions. As for November 2020, the most popular guided projects on Coursera are: 

Spreadsheets for Beginners using Google Sheets

Enrolled students: 34,709 

Reviews: 435 

Cost: $9.99

During 2 hours you will learn the basics of working with Google Sheets.  

But you can apply these skills to other spreadsheet programs, such as Microsoft Excel.

For this online course on data science, your instructor is Alex Margos. He will guide you through the following concepts: 

• Working with basic data entry, formatting, and calculations 
• Leveraging relative and absolute cell references 
• Using concatenate, stout, sum, and other essential functions 
• Working with advanced functions such as vlookup, if, and, or
• Dealing with error messages 
• Applying conditional formatting, filtering, and sorting
• Visualizing data with basic charts 

By the end of the project, you can apply learned concepts for visualizing household expenses.

Introduction to Python

Enrolled students: 24,223 

Reviews: 260

Cost: $9.99

Introduction to Python is the second most popular guided project on Coursera. It suits beginners in data science and takes 55 minutes to complete. The course instructor is David Dalsveen, a Software Engineer skilled in C, JavaScript, and Python. With David, you will walk through the following steps: 

• Introducing to Python applications 
• Learning Python Variables and Input
• Working with Python Decision Constructs
• Using Python Looping
• Leveraging Python Functions

You’ll learn how to apply new knowledge in Python to create a guessing game app that puts the computer against the user.

COVID19 Data Analysis Using Python 

Enrolled students: 23,348

Reviews: 315

Cost: $9.99

COVID19 Data Analysis Using Python takes 100 minutes to complete. With this program, you’ll learn how to prepare your data courses for analysis, find measures to establish for your analytics, and visualize your results. 

Ahmad Varasteh, Data Mining and Machine Learning instructor will explain how to work with the COVID19 dataset. The dataset includes the cumulative number of confirmed cases per day. You will merge it with another dataset with various life factors, scored by the people living in each country worldwide. Then, you’ll find any relationship between them and visualize results. This guided program consists of the following steps: 

• Importing COVID19 dataset and preparing it for the analysis 
• Deciding on and calculating a measure for an analysis
• Merging two datasets and finding correlations among our data 
• Visualizing our analysis results using Seaborn

3 Best online course for Data Science

To get more in-depth knowledge in a subject, try data science courses. They include recorded auto-graded and peer-reviewed assignments, video lectures, and community discussion forums. So, what data science courses do Coursera users take more often? 

Machine Learning

Enrolled students: 3,722,388

Reviews: 38,354

Cost: $79 per month

Machine Learning is one of those tech trends that is changing the world around us. If you want to learn how to create ML algorithms, take a Machine Learning course. The online course in data science includes an 11 week program and takes 60 hours to complete.   

Machine Learning program is an introduction to machine learning, data mining, and statistical pattern recognition. It covers Supervised learning and Unsupervised learning algorithms. You’ll become familiar with building smart robots, text understanding, computer vision, and other areas.

Andrew Ng, founder of Landing AI and founding lead of Google Brain, will instruct you on machine learning during the whole program which includes: 

• Week 1 Introduction, Linear Regression with One Variable, Linear Algebra Review
• Week 2 Linear Regression with Multiple Variables, Octave/Matlab Tutorial
• Week 3 Logistic Regression, Regularization, 
• Week 4 Neural Networks: Representation, 
• Week 5 Neural Networks: Learning
• Week 6 Advice for Applying Machine Learning, Machine Learning System Design
• Week 7 Support Vector Machines
• Week 8 Unsupervised Learning, Dimensionality Reduction
• Week 9 Anomaly Detection, Recommender Systems
• Week 10 Large Scale Machine Learning, 
• Week 11 Application Example: Photo OCR

Neural Networks and Deep Learning

Enrolled students: 864,559

Reviews: 19,625

Cost: $49 per month

Neural Networks and Deep Learning are the most complex and, at the same time, the most exciting subfields of Artificial Intelligence. This course is a part of Deep Learning Specialization offered by Deeplearning.ia. 

The Neural Networks and Deep Learning course takes 20 hours to complete. 

Top Course instructors from Stanford University will support you on your road to Deep Learning proficiency. They are Andrew Ng, Kian Katanforoosh, and Younes Bensouda Mourri. So, what you’ll learn during this 4-week course? 

• 1 Week Introduction to deep learning
• 2 Week Neural Networks Basics
• 3 Week Shallow neural networks 
• 4 Week Deep Neural Networks

Python Data Structures

Enrolled students: 677,472

Reviews: 13,757 

Cost: $49 per month

The Python Data Structures course is a part of Python for Everybody Specialization, provided by the University of Michigan. The course will introduce you to the core data structures of the Python programming language. In 19 hours, you’ll learn the basics of procedural programming and how to leverage Python for built-in data structures and perform sophisticated data analysis. 

At the end of the course, you’ll know the principles of data structures and how to apply them. You’ll be able to write code for apps and use Python dictionaries for storing data. 

Charles Russell Severance, a Clinical Professor from the School of Information, will be your course instructor. The course includes a 7-week program, based on Chapters 6-10 of the textbook “Python for Everybody”: 

• 1 Week Chapter Six: Strings
• 2 Week Unit: Installing and Using Python
• 3 Week Chapter Seven: Files
• 4 Week Chapter Eight: Lists
• 5 Week Chapter Nine: Dictionaries
• 6 Week Chapter Ten: Tuples
• 7 Week Graduation

3 Best Data Science specializations 

In the Coursera Specialization section, you will find programs to master specific skills for your career. Each specialization includes a series of video courses and hands-on projects. At the end of every specialization program, you’ll earn a Specialization Certificate. 

The top 3 most popular specialization programs are: 

Data Science Specialization

Enrolled students: 407,031 

Reviews: 15,786 

Cost: $49 per month

Data Science specialization, offered by Johns Hopkins University, includes 10 courses for beginners with previous Python experience. If you study for 7 hours each week, you can complete this specialization in 11 months. 

With Data Science Specialization, you’ll learn how to build a data science pipeline from the ground-up. 

For the final Capstone Project, you’ll apply new skills and knowledge, creating a data product using real-world data. 

Four instructors from the Bloomberg School of Public Health will guide you through the specialization. They are Jeff Leek, Ph.D., Roger D. Peng, Ph.D., and Brian Caffo, Ph.D. Now, let’s take a look at the courses this specialization includes. 

• Course 1 The Data Scientist’s Toolbox
• Course 2 R Programming
• Course 3 Getting and Cleaning Data
• Course 4 Exploratory Data Analysis
• Course 5 Reproducible Research
• Course 6 Statistical Inference
• Course 7 Regression Models
• Course 8 Practical Machine Learning
• Course 9 Developing Data Products
• Course 10 Data Science Capstone Project Class

To get a certificate, you’ll have a portfolio demonstrating their mastery of the material.

Data Science: Foundations using R Specialization

Enrolled students: 66,419

Reviews: 13,189

Cost: $49 per month

Data Science: Foundations using R Specialization, the second most popular specialization in data science, takes 5 months to complete. 

You’ll learn about core data science tools and techniques, how to get clean, and explore data, and R programming. Apart from that, this specialization includes peer review assignments. At the end of each course, you need to complete a project to show your new skills. 

Your instructions are Jeff Leek, Ph.D., Roger D. Peng, Ph.D., and Brian Caffo, Ph.D. from Bloomberg School of Public Health. As for the learning program, it includes 5 courses from Data Science Specialization we previously mentioned. 

• Course 1 The Data Scientist’s Toolbox
• Course 2 R Programming
• Course 3 Getting and Cleaning Data
• Course 4 Exploratory Data Analysis
• Course 5 Reproducible Research

After you finish this program, you can continue your learning path with the Statistics and Machine Learning specialization. 

Deep Learning Specialization

Enrolled students: 540,049 

Reviews: 38,851

Cost: $49 per month

Deep Learning specialization gives in-depth knowledge about deep learning algorithms and their applications. 

During 4 months, you will pass 5 courses and learn about building neural networks, starting successful machine learning projects, and anything in between. You will master your theoretical knowledge in Python and TensorFlow with real projects to become a market-ready AI specialist. 

The Specialization instructors are – Andrew Ng, Kian Katanforoosh, and Younes Bensouda. 

They will guide you through 5 courses of this specialization: 

• Course 1 Neural Networks and Deep Learning
• Course 2 Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization
• Course 3 Structuring Machine Learning Projects
• Course 4 Convolutional Neural Networks
• Course 5 Sequence Models

To earn your certificate, you need to finish hands-on projects at the end of each course successfully. 

Top 3 Data Science Master Degree 

Master degree programs available on Coursera are your perfect choice if you want to study at top universities online. The main benefit of master’s degree programs is lower costs compared to on-campus learning. While paying less, you receive the same credential as students who attend class on campus. Let’s look at the top 3 master’s degree programs in data science. 

Online Master of Computer Science from the University of Illinois

Length: 12 – 36 months

Courses: 32 credit hours (8 courses)

Cost: $21,440

The Master of Computer Science in Data Science (MCS-DS) program includes mastering skills for starting your data science career. With MCS-DS, you’ll learn how to apply machine learning, data mining, data visualization, and cloud computing. The program also includes interdisciplinary data science courses from the Department of Statistics and the School of Information Sciences.

You’ll learn how to apply mathematical foundations, algorithmic principles, and computer science theory to real-world problems. You’ll also study how to identify the computing requirements appropriate for solving those problems. 

The degree program includes mastering skills for designing, implementing, and evaluating computer-based systems, processes, components, and programs. 

Master of Data Science from National Research University Higher School of Economics

Length: 20 – 24 months

Courses: 18 Courses + 3 Projects

Cost: 675,000 – 1,350,000 RUB

The Master of Data Science offered by National Research University Higher School of Economics is an English-taught program, though 100% online. All meetings and live class sessions are organized in Slack and Zoom. The course includes leveraging Python programming language for practical assignments, shared via Github. 

The curriculum includes mastering analysts, data and computer science, software engineering, and machine learning skills. You can select the degree specialization among three options: 

• Data Scientist
• Machine Learning Engineer
• Researcher in Data Science

Online Master of Computer Science from Arizona State University

Length: 18-36 months

Courses: 10 courses

Cost: $15,000

The Master of Computer Science (MCS) degree program from Arizona State University offers a well-balanced combination of theoretical and practical assignments. The degree program covers such topics as artificial intelligence, blockchain, big data, and cybersecurity. By taking this master’s degree program, you’ll receive the same knowledge as students who study the program on-campus. 

With this program, you’ll learn about performing computer analysis, information transfer systems, and computational processes. The curriculum also covers cloud platforms’ leverage for increasing productivity, solving problems in automated planning and scheduling. After graduating from the degree program, you’ll be able to create search engines, systems of biomedical information, and package distribution systems. 

Data science certifications online: the final word

The learning path of data science may look like an endless highway for a beginner. But remember that all things worth attention take time. Thus, don’t try to learn everything in a day. 

You can start by learning data science fundamentals on specialization programs and online data science courses. Then, you can take certification programs to find and fill in the knowledge gap. For polishing your practical skills, participate in boot camps. You can also take guided programs. 

But promise me, you’ll never give up. 

If you find this article with the best data science programs useful, I’ll be glad if you’ll share it with your friends on Facebook and LinkedIn. 

Implementing the latest big data solutions for your business can be vital to staying ahead of the competition. At The APP Solutions, we build user-friendly products that incorporate our vast experience with AI, machine learning, and cloud-based computing. We’re proud to announce that our hard work has paid off—Clutch has named us a top B2B company in Ukraine in a recent press release!

Clutch is a B2B research firm that helps buyers looking to make critical partnerships for their organizations with outsourced developers and other agencies. After collecting customer feedback through client interviews, Clutch analyzes the best companies to produce a trustworthy B2B database for buyers. 

In one of our latest projects, we built a custom data analytics platform for a venture capital firm. They kindly gave us five stars across the board. 

Here’s what they had to say about our services:

“The system is complete and now gives us a fuller understanding of how our target audiences for our products intersect. We can optimize internal and external processes as a result… The APP Solutions has developed a complex system from scratch and integrated it seamlessly into our pipeline.” — Investment Associate, Venture Capital Firm

We are also featured on The Manifest, Clutch’s sister site. A how-to guide and research source for prospective buyers and entrepreneurs, The Manifest ranks us as one of the top app developers in Kyiv. 

Buyers can discover more research on Ukraine’s B2B sector on Visual Objects, a digital portfolio site for companies like us can showcase work.

We would like to thank our satisfied customers for leaving us so much positive feedback and helping us earn this prestigious honor. If you are looking to join their ranks, feel free to visit our website and contact us today!

PHP is a programming language that provides users with a basic structure to build applications. In 2018, PHP powered around 80% of all websites. Using a broad spectrum of PHP web framework ecosystems, developers can create a safe, user-friendly, and sophisticated mobile app in the short term. 

Let us imagine that you need a mobile application for your business and you want to build it with the PHP framework. Here, the question arises “How to decide which PHP framework is best for you?” Since there are too many options, to find the best framework becomes challenging.

But don’t panic. We are here to help! In this article, we have gathered:

• Tips on how to choose the framework that is best suited for your project ·         
• The top four PHP solutions and their main features
• The top four PHP frameworks comparison table 

to make it easy for you to choose the best PHP framework for your business. 

Read on!

BEST COURSES FOR DATA SCIENCE RECOMMENDED BY A SKILLED DATA SCIENTIST

Tips on how to choose a PHP framework

To select your future framework in PHP, you need to consider the following parameters:

The goal

The aim of your project is the main decision-making factor for the future framework. 

If you need to develop a simple web app, there is no need to use an enterprise-level framework that requires many resources.    

Community

If your PHP framework is popular, you will receive support from its publishers and service companies while getting more external packages and libraries. Also, consider that some frameworks are more preferred in some particular countries.  While Yii is more liked among Russian-speaking developers, Laravel has a big community that mostly consists of English-speaking users.   

Security

Security is essential for the best web development in PHP. Since websites are potentially vulnerable, you need to select the framework that ensures advanced security functions like Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), SQL Injection.

Documentation

Proper documentation will make it easier for programmers to understand the source code and develop applications. Since different framework varies greatly depending on the complexity. While Yii is simple to learn by developers, Symfony is more complex. For this, consider the volume, nature, and quality of the framework documentation.

When you know about the most important factors for future PHP solutions, let’s move further. Below you will find the list of top PHP frameworks and their advantages.

BACK-END TECH STACK FOR CUSTOM ELEARNING SOLUTIONS

The 4 best PHP frameworks

As we have said, there are numerous PHP solutions, currently presented on the market.

The image below shows the popular PHP frameworks and the percentage of their users.

Among other PHP frameworks, Laravel, Code Igniter, and Symfony are definitely the leaders.

Still, we chose quality over quantity. According to our experience, the best PHP frameworks are:

•     Symfony
•     Laravel
•     Zend
•     Yii 2

These frameworks allow creating, not only a high-end web app but also enterprise-level software. Find out more about their main advantages below.

ESSENTIAL FEATURES FOR A CUSTOM TRANSPORTATION MANAGEMENT SYSTEM

1. Symfony

Image source: Symfony

Since its launch in 2005, the Symfony framework provides web developers with built-in functionality for testing, as well as reusable code and components. They are Cache, ClassLoader, Form, Mailer, Messenger, suitable for any project requirements, even the development of enterprise-level software. This framework also has other benefits, including:

Fast loading speed. The app’s performance is essential. Since it determines whether users will use it or not. Symfony 4.2 loads its REST API during 2 ms. In this way, Symfony is the fastest PHP framework. 

Flexibility. Symfony adapts to any project requirements. The framework includes Event Dispatcher, making Symfony completely configurable. Besides, each Symfony component is operated independently.

Thorough documentation. This framework has comprehensive and detailed documentation useful for both experienced developers and newbies. All framework components are well explained and simplified with examples. 

Advanced stability. SensioLabs company, the Symfony developers, have supported this framework for 13 years. So, you can contact them concerning any issues. 

We used this PHP framework to transform the existing CPA product into a white-label mobile marketing platform.

The client required us to build a mobile app that handles planned traffic growth.

HOW TO LEVERAGE A MOBILE DEVICE MANAGEMENT SYSTEM TO ENSURE EMR SECURITY IN MOBILE HEALTHCARE APPS

• Our team expanded a data processing toolset to make it more scalable.
• We have added new parameters and features
• Besides, we ensured the smooth Android app operation using Symfony 4.

As a result, the client received a fully-featured mobile marketing platform with over 50 parameters, 20 metrics, and an intuitive interface. Find out more about this project by reading the case study. 

2. Laravel

Image source: Webxmedia

From the beginning, the main goal of Laravel is to create feature-rich web applications. Laravel has a well-built templating engine that performs a large variety of regular tasks. This framework has such features as RESTful routing, caching, user management, and authentication. This way, Laravel speed up the development process. Why do we use Laravel?

Advanced security. As we have said, high-level security is one of the essential PHP framework features. Laravel has advanced security that eliminates any malware activity. With Laravel, you receive a risk-free framework, secure from unintended and hidden SQL injections.

Mail services integration. With a built-in mailing service, your web application can send notifications to users via local or cloud-based services across various delivery channels, such as SMS and Slack. Laravel has a simple API and provides drivers for Mailgun, SMTP, Mandrill, and Amazon SES.

Numerous features. The framework allows developers to integrate numerous ready-made features using packages, modules, plug-ins, and even components to your web app and speed up the development process. The full list of the most popular Laravel packages is available on the link.  

Better authentication. Why use Laravel Framework? Because it helps developers implementing certain functionality in a better way. Therefore, developers can easily create even the logic behind the authentication, which always was an extremely challenging task. 

We used Laravel for Conectric LLC, a start-up from the real estate sector. The main goal was to provide commercial building owners with end-to-end solutions that reduce the power consumption of HVAC (heating, ventilation, air conditioning) and lighting. 

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For this project, we

• Developed the whole software ecosystem
• Integrated the back-end with two mobile applications.
• Incorporated the software with hardware, high-loaded back-end, sensors, and control boxes.
• Created a high-performing and secure mobile app using Laravel.

You can find the full case study by following this link.

3. Yii2

Image source: Yiiframework

Yii2 is famous for advanced security and fast loading time. This framework applies a non-repetitive code principle. Such a principle allows for avoiding the repeated use of similar software patterns. Using Ajax and JQuery features, Yii2 simplifies the development of large-scale applications. Other Yii advantages are:

Advanced security. Yii 2 includes numerous security measures to prevent your website or a web app from attacks.  So you should not worry about cookie tampering, SQL injection, cross-site request forgery (CSRF), and cross-site scripting (CSS) anymore. 

Faster development time. Since Yii generates the basic CRUD (create, read, update, and delete) operations, the team of developers created the application faster, which reduces the project costs.

Easy installation. During the development process, every minute is valuable. Yii helps developers to improve their use of time by providing the installation template. Thus, developers need less time to install and configure the framework.

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4. Zend Framework 

Image source: Medium

Built with agile methodology in mind, Zend Framework is aimed at enterprise-level application. By using interfaces and inheritance features, Zend allows adding any project-specific functions to your app. Zend has 5 years of support

allowing you to develop stable business applications with a longer usable lifespan.

This framework is also known for its great performance and security. Besides, this framework has other advantages:

Collection of authentication components. As we have said, authentication makes PHP development quite a challenging task. Zend toolset for feeds and form validations eases the development process because there is no need to create this functionality from scratch.

Zend Studio IDE. Zend Studio is an integrated development environment that allows developers to code faster with improved indexing, validation, and searching of PHP code. Now, developers can debug more comfortably with Xdebug, Zend Debugger, Z-Ray integrations, and deploy PHP applications on any server including cloud support for Amazon AWS and Microsoft Azure.

Enhanced documentation. With The Zend Framework Reference Guide that includes over 1,000 pages and over 500 examples, your development team might find the necessary solution for your app. Moreover, Zendcasts includes numerous tutorial videos that highlight the framework functionality. 

Zend Expressive. Zend Expressive PSR-7 is one of the core framework components, which improves the implementation of microservices in the applications. This component allows your team to develop complex web applications with a set of easy PHP interfaces that enable the working with URIs, HTTP requests & responses, and uploaded file representations. 

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The top 4 PHP framework comparison

Now, let us look at the most popular PHP frameworks comparison, their usage, and main advantages.

  Symfony vs. Laravel vs. Yii 2 vs. Zend

Framework	Current version	Industry	Size	Main advantages
Symfony	4.3.2.	Mobile web applications	Large Scale Portals	Free of repetitive code writing Fastest framework for large-scale applications Provides faster performance Protects sensitive data against threats Handles a large number of requests per seconds
Laravel	5.8	eCommerce	Medium Scale Portals	Streamlined authentication integration Faster web applications development Supports popular cache back-ends Easily handle errors Reliable Routing
Yii 2	2.0.22	eCommerce	Medium Scale Portals	Free of repetitive code writing Fastest framework for large-scale applications Faster performance Protects sensitive data against threats Handles a large number of requests per seconds
Zend	3.0.1	web applications	Large Scale Portals, Enterprise-level web applications	Well-documented Flexible Architecture High productivity The extensible and well-tested codebase

 

If you did not select your PHP framework yet, don’t despair. As a bonus section, we share other popular PHP frameworks and their description. 

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Other 6 popular PHP frameworks for web development

Our popular PHP frameworks list includes the following solutions:

• CakePHP
• CodeIgniter
• FuelPHP
• Phalcon
• Slim
• PHPixie

Now, let us look at each of them in more detail.

CakePHP

Powered with Ruby on Rails, this framework allows the receiving of a better website so the development process becomes flexible, faster, and, as a result, cost-effective. CakePHP uses auto-detection for all settings and does not require a previous system configuration. Developers spend the most time on writing codes and checking their structures synchronization. With CakePHP pre-written code, developers spend less time on such tasks. They pick the desired code and add it to the app.

CodeIgniter

CodeIgniter is the best option for those who are looking for a simple yet reliable solution. This PHP framework has detailed documentation. Still, if any issue arises, you can receive support from the developer community that counts 57,000 users.  

FuelPHP

This flexible PHP framework uses URI filtering for advanced security. FuelPHP is great for developing web applications of diverse sizes and complexities.

Phalcon

Unlike other frameworks, Phalcon built on C and C++ coding. In its feature list, Phalcon has a universal autoloader, caching, security, asset management, translation, and much more. This framework uses low levels of architecture, which reduces overheating, typical of MVC-based apps. 

Slim

Developers use Slim in case they need to create RESTful APIs for their projects. Apart from minimalistic design, this framework includes URL routing, session, and cookie routing.  If you want to build a small web application, this is your framework of choice. 

PHPixie

Developed for read-only websites, PHPixie uses 100% unit tested components that require minimum dependencies. In its feature list, PHPixie has authorization capabilities, input validation, caching, and object-relational mapping.

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Conclusion

PHP frameworks allow developing highly secure, feature-rich applications in the short term.  Still, with such a great diversity of PHP frameworks in 2019, it is difficult to choose the best one for your project.

When choosing the PHP framework, base your choice on the project’s needs, security options, extensive documentation, and the developer’s community. 

To create a feature-rich mobile app, with advanced security and numerous integrations, we recommend Symfony and Lavarel. We used these frameworks to facilitate the development process in our previous projects. 

However, for a simple web application, you can use Yii or CakePHP.

If you have an idea for a future project, need help in choosing the PHP framework, or want to hire PHP developers, experienced in creating applications, we are here to help.

 

The emergence of the Beacon system, especially Bluetooth-based, has dramatically simplified the way businesses improve engagement with customers. And as a result, it brought customer experience to an entirely new level, and with that, increased growth prospects for the companies.

In this article, we will explain what beacon technology is, how it works, and how beacons are used in different business operations.

What is beacon technology?

Geolocation beacons look like small devices empowered by a CPU, radio, and batteries. They use Wi-Fi or Bluetooth Low Energy technology to repeatedly sending identifiers to mobile devices nearby. The identifier includes a unique ID number, recognized by mobile devices. When the device user walks near a beacon device, one receives a unique beacon identifier that may contain ads, special offers, links, or perform whatever function it has been programmed. 

[Beacon technology explained]

Still, to make Bluetooth beacon tracking and sending notifications, you need user permission. 

Beacon technology for business success

Beacons are cost-efficient and straightforward solutions to deliver relevant information to customers. Still, you can use beacons to gather information about your customers and analyze it. 

With the help of beacons, you can gather: 

• How much time a particular customer has spent in a specific area of the store; 
• Which product section was particularly interesting for him;

Later on, this information is figured into advertising targeting that further perpetuates customer experience and improves the precision of product suggestions.

Types of beacons

The market is flooded with beacon devices that vary by size, use case, battery performance, and the ability to resist exogenous factors. The most common beacon types are:

• Standard beacon looks like a small Wi-Fi and works the best for indoor tracking. 
• A Portable/small beacon is no bigger than a credit card and suits proximity solutions and asset tracking. 
• USB beacon is portable, small, and convenient to deploy and looks like a flash drive. 
• Video beacon Bluetooth device, usually plugged to the back of the screen, is a small device aimed at delivering contextual videos and digital signage. 
• AI beacon is empowered by machine learning algorithms to detect gestures and movements. 
• Sticker beacon, the smallest type of beacons, are the perfect solution for asset tracking.
• Parent beacon that looks like a big Wi-Fi router that manages Bluetooth beacon network gathers their data from other beacons, sent it to the cloud storage, and so on. 
• A dedicated beacon is the best solution for tracking indoor activities and working in harsh environments since it is resistant to antistatic, UV, water, dust, and other exogenous factors. 

At the same time, beacons also vary on specification, including: 

Apple iBeacon, a dominant player of beacon technology. Introduced in 2013, it is one of the most commonly used types of beacons out there. They are compatible with both iOS and Android and are easy to implement in any environment. iBeacon is perfect for the straightforward UUID broadcasting of notifications.

AltBeacon is the original alternative to iBeacon introduced in 2014. From a technical viewpoint, AltBeacon and iBeacon are very similar, but AltBeacon takes a more streamlined approach to API, which makes it more flexible in terms of operational customization. But at the same time, it is a bit harder to handle because of that. 

Eddystone is Google’s take on beacon technology, introduced in 2015. In comparison with iBeacon and AltBeacon, it offers more features. In addition to standard UUID, Eddystone beacons can send URLs (which let you cover those who haven’t installed an application). This feature makes it a better instrument for engaging a new audience. 

Top 5 beacon technology uses

Nowadays, a beacon is a solution of choice for such giants as Wallmart, New York Times, and Wall Street Journal that integrated this technology into their mobile apps. 

To give you a clue on how you can implement beacon technology, below we have gathered these and other successful beacon use cases.  

Proximity Marketing

Proximity marketing in retail is the first field of beacon technology use. Retail commerce lives and breathes on, giving the customer a reason to buy something. Since the customer is already in the store, beacons can drop a nudge right on the phone and simply suggest something worthwhile. That makes them very convenient tools for delivering a personalized customer experience with minimal resources involved. 

An excellent example of proximity marketing in retail done right is Walmart. They use Wi-Fi beacons to perpetuate personalized customer experience with reasonable offers, timely reminders, and attractive discounts based on external user data:  

• Customer’s location in the store;
• The route sequence; 
• Features of the area. 

This information is combined with an in-app input (most commonly – product searches). Each notification has a set of requirements, such as time spent on location, and the priority of an offer based on an in-app input. If the conditions of certain notifications are met – it is sent to the customer. 

Payment 

Payments are the next logical step of implementing beacons into the customer experience. The most common two ways of implementing beacon-based technology to the payment experience are by adding it to the payment terminal and enabling payments via smartphone (currently in use by terminals compatible with Apple Pay and Google Pay services).

In essence, the beacon-based payment experience is an excellent showcase of the advantages of cashless purchases widely adopted by major retailers. Here’s why:

• Payment proceedings are more comfortable to track and analyze (that’s good for optimization of the supply chain and the analysis of the demand);
• Fraudulent activity is more natural to detect and neutralize (especially, in the case of account hijacking).

At this point, major digital wallet platforms, like Google Pay and Apple Pay, are compatible with beacon payments both via Bluetooth and Wi-Fi.

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Event Management

Event management is the other big field where Beacon location technology may come in handy and make a huge difference. Managing events on any scale is a considerable challenge.

You need to: 

• Know what is going on all the time 
• Inform everybody about everything as soon as possible 
• Navigate people through the event and remind them of scheduled events and changes to the program.

Beacons can also be used to deliver complementary content like information about the companies in a specific area or details about present and future events.

Let’s see how the SXWS festival uses beacons to improve event management. 

In their case, beacons and event app handle:

• Registering and identifying the attendees;
• Proximity marketing for Event Promotion (for example, “Big product unveil at Hall H in 30 minutes”);
• Providing valuable information (event reschedule or merchandise giveaway)
• Location Navigation (map with all relevant events marked down);
• Social Networking (like contact exchange, etc.);
• Exit Surveys

In this way, beacon functions allow managing events and gathering valuable insights about the audience.

Content Delivery

The perfect way of implementing content delivery to the customer experience is with various hangout platforms like cafes, coffee shops and restaurants, and the likes. Airport or railway waiting rooms are also suitable spaces for this kind of activity. 

For example, major newspapers like the New York Times and Wall Street Journal have provided certain popular hangout spots in Manhattan and JFK airport with beacon-based content delivery. Beacons sent the latest news and the most popular articles to promote the newspaper’s applications.

Transportation

Transportation and public transportation, in particular, can benefit from beacon app notifications in the following ways: 

• Inform passengers about the route schedule and current location of the vehicle (Stop AAA, some significant object nearby, etc.),
• Manage ticket deployment in a more streamlined and effortless manner. This approach is now widely used in the New York City public transportation system.
• Provide information such as train/plane changes or delays, and also specify weather conditions on the destination point. This approach is currently in use in the international airports of Frankfurt, London’s Heathrow, and New York’s JFK. 

The APP Solutions experience with beacon technology 

Apart from outdoor, beacons are also handy in indoor navigation. Such a beacon use case mainly concerns exhibitions, where people have a limited amount of time and massive space with numerous booths. 

Our recent client, an organizer of a bicycle exhibition, hired us to develop a mobile app with beacon technology to help visitors navigate in spans over 11,000 square meters. 

To bring the client’s idea into reality, we build a mobile app with integrated beacon technology and various filters to help exhibition visitors find the direction to a particular bike. 

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In Conclusion

Beacon technology is a powerful tool to engage the audience and deliver them relevant information on short notice. It has many successful use cases across different industries. Thus, if you are looking for a solution to increase engagement with your office audience or make your event management app even more convenient, this solution is right for you. 

Today we’re going to discuss a whole range of apps for cars. Probably you’ve read a summary from the Mobile World Congress last year where there was discussed how many cool cars were there and how few apps for them. Two years ago Apple released Apple CarPlay that was later followed by Android Auto launched by Google. 

So, let’s explore what’s happened since that moment and what’s that supercar app is. Here are what people in the industry shared with us:

1. Cars are getting connected

Today many car manufacturers include WiFis and SIMs by companies such as Novero in their premium-class models. This is awesome as the mobile signal and as a result data connections are much better with an external antenna that provides better coverage. That also means that your car is permanently connected and can deliver information to the driver and owner even when there is nobody in the car. On the other side, your car will need an extra mobile connection, that’s why many car buyers prefer not to activate it.r

2. Navigation and Control

Manufacturers know that the number one priority in cars and driving is safety. That is the main reason why they always try to include voice control along with touch screens to make it easier to navigate and receive information. But that’s a fact that the car voice control systems are mostly bad as well as navigation and touch screens lack the quality of the Samsung Galaxy or iPhone devices. This is certainly a barrier to uptake.

2018 Update There are already augmented reality navigation maps, user cases, and solutions. You can check our articles and cases.

3. Proprietary Car Apps & Android Auto + Apple Carplay

Speaking about the barriers to uptake, it is also worthy to note the very little amount of proprietary smart car systems. The majority of app developers just can’t afford to invest in one more platform like QNX. This is the main reason why car manufacturers preferred to launch Android Auto and Apple CarPlay although with common restrictions. Android and iOS developers don’t have any access to the information from the car but can only use the audio and navigation systems of the car. Moreover, Google and Apple ask for additional approval for car apps.

Well, if you need access to speed, fuel consumption, last service, and other items, the only way you can use to do it is through proprietary platforms designed by manufacturers. It’s even more interesting to observe that there was almost nothing said about the QNX car application development platform in the previous year.

4. Security

Another huge barrier for a car app after safety is security. Just imagine what will happen if for example someone will hack let’s say 15,000 cars and will do an override of the autopilot to deactivate or accelerate the breaks. This is the biggest reason why access to car features is limited for extra apps.

5. What Apps are Available Today?

Currently, Apple offers Phone, Maps, iMessage along with different music apps like iHeartRadio, Podcasts, and Spotify. Although the portfolio is a little bit limited now, it’s still growing. On the other hand, the Android portfolio is almost the same with similar functionality (media) enabled but with more applications, as they’ve been less restrictive with who they approve.

Besides that, you can enjoy the proprietary car platform apps that have specific features like weather, navigation, and media, and that are pretty much it.

Probably the most useful and sophisticated apps can be found in such car information start-up manufacturers as Mojio, Automatic, Truvulo, and Dash. The connection is performed through an open standard plug, which is located underneath the wheel with the help of their own or 3rd party hardware and offers car information about speed, consumption, the health of the car, driving behavior, and more. This hardware can then be used to provide applications that track safe driving (usually use by teenagers), car location, and fuel consumption.

6. What Will Be the Killer App of the Future?

There were big debates among the industry players and the biggest challenge discussed was the huge difference between what want consumers and what want manufacturers. It is obvious that manufacturers ask for providing more data from the cars, such as driving behavior, utilization, fuel consumption, safety, collecting road information, location tracking, and other data that can be used to improve the service, cars and sell data to third parties.

On the other hand, the consumers have to become incentivized to provide information about them and no one of the existing car applications is good for that. The type of apps the consumers are seeking today involve music, navigation, traffic information (including police controls), car safety for their driving teen-children, service and maintenance (1 time per year), weather broadcast, and of course keeping the car secure.

Bonus: Top 8 Free Car Information Apps

As it was told earlier, car applications represent virtual information hubs, which offer details on new models of cars, cars for sale, parking opportunities, and possible ways to save on car use.

We’ve decided to list the most popular and absolutely FREE car applications you may download and install right now.

Parking Panda

Parking Panda is extremely helpful for anyone seeking parking lots in the largest metro areas, where parking usually becomes a real challenge.

AutoGlance

This app will help drivers to find used cars within 200 miles of their location

Parkopedia

Parkopedia is another great app that provides you with detailed information about parking areas all around the world.

True Car

This app collects information that can be useful for those users interested in buying a new car for the best price.

ParkWhiz

Visit ParkWhiz.com each time you need to search and reserve a guaranteed parking lot before you reach the destination.

DriverSide

This is just an awesome online resource for any driver

DentBetty

Here you can get detailed information about auto body repair estimated by the photo.

CarWoo!

Looking for a new car for the best price? Be sure to get exactly what you want with CarWoo.

Well, what’s the killer car app for your Android or iPhone? Perhaps these are Google self-driving cars, after all, so we can spend our time blogging and not be bothered by the driving process at all?

See also: How to Make a Taxi App like Uber?