Amassing and organizing data is a critical function of many businesses, organizations, and governmental agencies. Additionally, professionals, researchers, academics, and individuals have a need in the modern world to gather and organize huge amounts of data.
What’s more, these entities need to analyze, draw conclusions about, and report on more data than ever before.
Don't get left behind as the data science industry evolves. In as few as 18 months, earn your MS in Applied Data Science from Syracuse University, ranked #19 in Best Online Graduate Computer Information Technology Programs. Apply today and join Syracuse iSchool’s growing alumni network of over 10,000 data professionals. No GRE required.Visit Site
No GRE is required for the University of Denver's online MS in Data Science. In as few as 18 months, you'll gain knowledge and skills in critical competencies such as programming, data mining, machine learning, database management, and data visualization. The program is available to students from all academic and professional backgrounds.Visit Site
The online Master of Information and Data Science (MIDS) program is preparing the next generation of experts and leaders in the data science field and providing students with a UC Berkeley education without having to relocate. Students graduate with connections to UC Berkeley’s extensive alumni network in the Bay Area and across the world. All international applicants will be required to submit official Test of English as a Foreign Language (TOEFL) scores.Visit Site
Gain technical skills in SQL, Python, and R and learn to drive successful business outcomes with your master's in business analytics online from UD. In 12 months, you will be prepared to pursue professional analytics roles. No GRE required.Visit Site
Related resource: TOP 15 ONLINE MASTER’S IN MARKETING ANALYTICS
The process of gathering, organizing, analyzing, and reporting on data is referred to as data analysis. And a key component of taking a comprehensive approach to data analysis is data exploration.
In short, data exploration can be defined as the process in which data analysts explore data using statistical techniques and data visualization. The goal is to describe the data using features like quantity or size to develop an understanding of what the data is saying.
But as with anything in the field of Big Data, there is much more to the story.
Let’s examine data exploration in more detail.
Data Exploration Basics
In the grand scheme of data analysis, data exploration is the first step. Typically, data exploration involves the identification of the primary characteristics of a dataset. Once those characteristics have been identified, data analysts can then summarize the data in the set.
When speaking of a dataset, we’re talking about a collection of information that is related, yet composed of separate data elements. Those elements can be better understood if they are organized or manipulated into some sort of usable informational unit.
In the past, this involved manual organization of the dataset. Today, however, this is frequently done using specialized computer software to speed up the process (though manual manipulation of data is still common).
For example, data analysts might use data visualization techniques to get a broad view of the data and get an initial understanding of what the data says. Then, manual manipulation might take place, such as using a drill-down method to find anomalies or patterns in the data.
Data exploration isn’t just for finding anomalies, though. Data exploration techniques can be used to identify the structure of a dataset, the relationships between different variables, and the presence of outliers.
Additionally, these techniques can help bring the distribution of data values into focus, thereby revealing points of interest in the data and patterns that give analysts a deeper understanding of what’s contained in the dataset.
Sometimes manual scripts can be used in conjunction with automated techniques as well. Scripts can be written in programs like SQL or even a spreadsheet program like Microsoft Excel to parcel out and view particular pieces of data. This is a valuable undertaking because data is usually gathered in huge quantities. Not only that, data is typically gathered from multiple sources and is in an unstructured format.
Because of the use of organizational and visualization tools, the process of data exploration makes analyzing the data a far easier process. Not only does this save time, but it also saves money, streamlines the process of data analysis, and helps analysts build an improved familiarity with the information. This, in turn, means that analysts can search for and find the information they need in a dataset with much greater speed.
What is Exploratory Data Analysis?
Let’s take this a step further and examine exploratory data analysis.
This is a specific technique used in data exploration to develop an understanding of multiple facets of the data. Think of it as the process of summarizing the data. Data scientists use it to explore data, break it down, and summarize its essential qualities.
Additionally, exploratory data analysis enables data scientists to identify inconsistencies, test speculations, and check suppositions. Likewise, exploratory data analysis gives data scientists the ability to observe what’s in a dataset, acquire information on the factors in the datasets, and explore their connections as well.
This process can be used to find exceptions in the data, find significant elements, gain new knowledge, and recognize mistakes.
Why is Automated Data Exploration so Important?
The evolution of technology, particularly with the internet, smartphones, and other web-enabled devices, means that there is more information available more quickly than ever before. But it’s too much data to explore manually.
Thus, automated data exploration gives organizations a much quicker means of analyzing all that data.
For example, the automated component of data exploration can include profiling data. This process aids in placing raw data into a more structured form as a precursor to a manual examination and consideration of that information. In other words, the automated portion takes care of a lot of menial and time-consuming tasks, such as:
- Cleaning the data
- Handling outliers and missing values
- Handling imbalanced data sets
Python libraries like pandas profiling enable these processes to be automated. By automating these processes, you can save an enormous amount of time and put your attention elsewhere in the data exploration process.
Importance of “Data Drilldown”
The automated element of data exploration typically is followed by what is called “data drilldown.”
Data drilldown is a manual process that is utilized to identify any patterns or anomalies identified through the automated component of data exploration.
Data drill down involves viewing the raw data after the conclusion of the automated process. Data drill down may also necessitate the use of spreadsheets to consider raw data in something of a more organized manner. In addition, as discussed earlier, manual scripting and specific queries into the data may be necessary as part of the effort to identify patterns or anomalies.
The final element of comprehensive data exploration is called data refinement. This involves what people involved in the process commonly called “pruning” or “refining” data.
Unusable elements of collected data are removed from the aggregate, an endeavor called “data cleansing.” The poorly formatted data is refashioned and relevant relationships across datasets are defined.
What Tools are Used in Data Exploration?
As noted a moment ago, there are manual data exploration tools as well as computer software programs that can be utilized for data exploration.
From the perspective of manual data exploration, popular tools include scripts that analyze raw data or using spreadsheets to manually filter data into an organized form.
For example, you can use Microsoft Excel to create simple charts. You can also use Excel to view raw data and identify correlations between two variables. Chi-squares, stacked column charts, and two-way tables are additional features in Excel that enable you to undertake data exploration.
Automated data exploration tools are much greater in number.
Data exploration software runs the gamut from complete platforms to data visualization software to business intelligence suites. These types of programs provide data analysts with scatter plots, bar charts, and various types of graphs (among many other visualizations) that facilitate more organized and efficient exploration of data.
Some popular software programs for data exploration include:
- Matplotlib – This program essentially recreates the graphics from MATLAB, but in a much simpler form. Matplotlib also includes robust support for visualizations. It has the advantage of being very fast, efficient, and is open-source with cross-platform support. A disadvantage of Matplotlib is that plots are only static – there are no interactive plots. Likewise, to make a custom plot you have to do a lot of repetitive coding.
- Pandas – this is one of the most popular Python libraries for data exploration. It began as a tool for quantitative analyses of financial data. Today, it’s most often used with table format data, like .xlsx or .csv. Pandas supports series and dataframe data structure types and gives you an easily readable representation of data. It has robust file format capability and is good at handling large datasets. It doesn’t, however, work well with 3D data, and indexing can be slow in series objects.
- Seaborn – This tool is based on Matplotlib. It allows you to create beautiful, informative charts with very little effort. Seaborn integrates well with pandas and allows you to use categorized plots directly. This type of plot is self-contained and provides the opportunity for alteration, such as adding legends or axis labels automatically. The advantage of this program is that plot customization is extremely easy. However, plots are not interactive.
- Scikit Learn – What began as a simple summer code project is today one of the most popular robust libraries for data exploration. Scikit Learn is built on Python and uses NumPy, SciPy, and Matplotlib. Because of this, it has a large collection of tools that includes data classification, clustering, preprocessing, and regression, just to name a few.
In addition to these software options, there are two programming languages that are most frequently used for data exploration: Python and R.
In the case of Python, it integrates well with common tools, has a great deal of support from a large community of users, and it offers a large library. When used with Pandas, Python makes data exploration even easier by providing:
- Time series functionality
- Reading and writing tools for data
- Aggregating data
- Subsetting of large data sets
- Merging and joining of datasets
- Pivoting and reshaping of datasets
- Hierarchical axis indexing
- Intelligent data alignment
These are just a few examples of the many functionalities Python and Pandas offer.
Who Uses Data Exploration?
Historically, what is known as data exploration today was a prime focus for statisticians. In this day and age, data exploration is more widely undertaken. Data exploration is the work of such professionals as data analysts and data scientists.
The data scientist represents a relatively new professional designation. Data scientists tend to be found in larger companies and other types of organizations, including governmental agencies and some nonprofit entities.
Whoever is conducting data exploration is taking part in an important process – converting mountains of numerical data into something that’s easier to understand.
The challenge, of course, is to give meaning to thousands, tens of thousands, or hundreds of thousands of data points. Data exploration helps narrow the focus while data visualization techniques help bring the most important data to life through:
- Lines and points
- Dimensions and angles
Doing so allows data scientists and data analysts to visualize the data more effectively, to derive meaning from it, to cleanse it, and present streamlined information.
How are Data Mining and Data Exploration Related?
When analyzing large amounts of data, you can mine it, which is an automatic process, or you can explore it, which is the manual equivalent.
Data mining refers to finding and extracting patterns in the data by using various algorithms. Data exploration, on the other hand, offers direction for making additional statistical treatments to the data.
Essentially, both processes are highly similar, and the terms are sometimes used interchangeably.
Also highly similar are the processes of data exploration and data examination. In undertaking data examination, you look at the internal consistency of the dataset. Doing so allows you to confirm the quality of the data prior to further analyses.
Another process that’s related to data exploration is data discovery.
Once data exploration has been done and the data is refined, data discovery can be undertaken. This process is used by businesses and organizations to answer extremely specific questions.
In order to provide the end-user with the level of specificity that’s needed, data discovery looks for patterns, sequences, and trends. It seeks out clusters of data, uses time-series analysis, and offers a means of visualizing data.
Data Exploration is a Critical Part of Data Analysis
In the final analysis, the various elements of data exploration are designed to create a meaningful, understandable, usable mental model.
It is also meant to achieve a suitable definition of basic metadata, which includes structure, relationships, and statistics.
In layperson’s terms, the ultimate objective of data exploration, and the application of its component parts, is to make once disparate datasets truly usable.