These days almost anything can be a valuable source of information. The primary challenge lies in extracting the insights from the said information and make sense of it, which is the point of Big Data. However, you also need to prep the data at first, and that is Data Wrangling in a nutshell.
The nature of the information is that it requires a certain kind of organization to be adequately assessed. This process requires a crystal clear understanding of which operations need what sort of data.
Let’s look closer at data wrangling and explain why it is so important.
Data Wrangling (also known as Data Munging) is the process of transforming data from its original “raw” form into a more digestible format and organizing sets from various sources into a singular coherent whole for further processing.
What is “raw data”? It is any repository data (texts, images, database records) that is documented but yet to be processed and fully integrated into the system.
The process of wrangling can be described as “digesting” data (often referred as “munging” thus the alternative term “data munging”) and making it useful (aka usable) for the system. It can be described as a preparation stage for every other data-related operation.
Data Wrangling is usually accompanied by Mapping. The term “Data Mapping” refers to the element of the wrangling process that involves identifying source data fields to their respective target data fields. While Wrangling is dedicated to transforming data, Mapping is about connecting the dots between different elements.
The primary purpose of data wrangling can be described as getting data in coherent shape. In other words, it is making raw data usable. It provides substance for further proceedings.
As such, Data Wrangling acts as a preparation stage for the data mining operation. Process-wise these two operations are coupled together as you can’t do one without another.
Overall, data wrangling covers the following processes:
- Getting data from the various source into one place
- Piecing the data together according to the determined setting
- Cleaning the data from the noise or erroneous, missing elements
It should be noted that Data Wrangling is somewhat demanding and time-consuming operation both from computational capacities and human resources. Data wrangling takes over half of what data scientist does.
On the upside, the direct result of this profound — data wrangling that's done right makes a solid foundation for further data processing.
Data Wrangling is one of those technical terms that are more or less self-descriptive. The term “wrangling” refers to rounding up information in a certain way.
This operation includes a sequence of the following processes:
- Preprocessing — the initial state that occurs right after the acquiring of data.
- Standardizing data into an understandable format. For example, you have user profile events record, and you need to sort it by types of events and time stamps;
- Cleaning data from noise, missing or erroneous elements.
- Consolidating data from various sources or data sets into a coherent whole. For example, you have an affiliate advertising network, and you need to gather performance statistics for the current stage of the marketing campaign;
- Matching data with the existing data sets. For example, you already have user data for a certain period and unite these sets into a more expansive one;
- Filtering data through determined settings for the processing.
Overall, there are the following types of machine learning algorithms at play:
- Supervised ML algorithms are used for standardizing and consolidating disparate data sources:
- Classification is used to identify known patterns;
- Normalization is used to flatten the independent variables of data sets and restructure data into a more cohesive form.
- Unsupervised ML algorithms are used for exploration of unlabeled data:
- Clustering is used to detect distinct patterns
The most fundamental result of data mapping in the data processing operation is exploratory. It allows you to understand what kind of data you have and what you can do with it.
While it seems rather apparent — more often than not this stage is skewed for the sake of seemingly more efficient manual approaches.
Unfortunately, these approaches often leave out and miss a lot of valuable insights into the nature and the structure of data. In the end, you will be forced to redo the thing properly to make possible further data processing operations.
Automated Data Wrangling goes through data in more ways and presents much more insights that can be worthwhile for business operation.
It is fair to say that data always comes in as a glorious mess in different shapes and forms. While you may have a semblance of comprehension of “what it is” and “what it is for” — data, as it is in its original form, raw data is mostly useless if it is not organized correctly beforehand.
Data Wrangling and subsequent Mapping segments and frames data sets in a way that would best serve its purpose of use. This makes datasets freely available for extracting any insights for any emerging task.
On the other hand, clearly-structured data allows combining multiple data sets and gradually evolve the system into more effective.
Noise, errors and missing values are common things in any data set. There are numerous reasons for that:
- Human error (so-called soapy eye);
- Accidental Mislabeling;
- Technical glitches;
Its impact on the quality of the data processing operation is well-known — it leads to poorer quality of results and subsequently less effective business operation. For the machine learning algorithms noisy, inconsistent data is even worse. If the algorithm is trained is such datasets — it can be rendered useless for its purposes.
This is why data wrangling is there to the right the wrongs and make everything the way it was supposed to be.
In the context of data cleaning, wrangling is doing the following operations:
- Data audit — anomaly and error/contradiction detection through statistical and database approaches.
- Workflow specification and execution — the causes of anomalies and errors are analyzed. After specifying their origin and effect in the context of the specific workflow — the element is then corrected or removed from the data set.
- Post-processing control — after implementing the clean-up — the results of the cleaned workflow are reassessed. In case if there are further complications — a new cycle of cleaning may occur.
Data Leakage is often considered one of the biggest challenges of Machine Learning. And since ML algorithms are used for data processing — the threat grows exponentially. The thing is — prediction relies on the accurateness of data. And if the calculated prediction is based on uncertain data — this prediction is as good as a wild guesstimation.
What is Data Leakage? The term refers to instances when the training of the predictive model uses data outside of the training data set. So-called “outside data” can be anything unverified or unlabeled for the model training.
The direct result of this is an inaccurate algorithm that provides you with incorrect predictions that can seriously affect your business operation.
Why does it happen? The usual cause is a messy structure of the data with no clear border signifiers where is what and what is for what. The most common type of data leakage is when data from the test set bleeds into the training data set.
Extended Data Wrangling and Data Mapping practices can help to minimize its possibility and subsequently neuter its impact.
- Excel Power Query / Spreadsheets — the most basic structuring tool for manual wrangling.
- OpenRefine — more sophisticated solutions, requires programming skills
- Google DataPrep - for exploration, cleaning, and preparation.
- Tabula — swiss army knife solutions — suitable for all types of data
- DataWrangler — for data cleaning and transformation.
- CSVKit — for data converting
- Numpy (aka Numerical Python) — the most basic package. Lots of features for operations on n-arrays and matrices in Python. The library provides vectorization of mathematical operations on the NumPy array type, which improves performance and accordingly speeds up the execution.
- Pandas — designed for fast and easy data analysis operations. Useful for data structures with labeled axes. Explicit data alignment prevents common errors that result from misaligned data coming in from different sources.
- Matplotlib — Python visualization module. Good for line graphs, pie charts, histograms, and other professional grade figures.
- Plotly — for interactive, publication-quality graphs. Excellent for line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axis, polar graphs, and bubble charts.
- Theano — library for numerical computation similar to Numpy. This library is designed to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
- Dplyr - essential data-munging R package. Supreme data framing tool. Especially useful for operating on data by categories.
- Purrr - good for list function operations and error-checking.
- Splitstackshape - an oldie but goldie. Good for shaping complex data sets and simplifying the visualization.
- JSOnline - nice and easy parsing tool.
- Magrittr - good for wrangling scattered sets and putting them into a more coherent form.
Staying on your path in the forest of information requires a lot of concentration and effort. However, with the help of machine learning algorithms, the process becomes a lot simpler and manageable.
When you gain insights and make your business decisions based on them, you gain a competitive advantage over other businesses in your industry. Yet, it doesn't work without doing the homework first and that's why you need data wrangling processes in place.
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