EDA is one of the most crucial skills in Data Science and Analytics. If you are a person who wishes to build a flourishing career using data, then taking an introductory course such as a Data Analyst Course Noida can help you learn about EDA right from the very beginning.
What is Exploratory Data Analysis?
Exploratory Data Analysis (EDA), often abbreviated as EDA, refers to the preliminary examination and interpretation of a data set before any further analysis or modeling. It can be considered as knowing your data – the way you would get to know someone by spending time around him or her.
EDA was coined by John Tukey, a renowned mathematician and statistician, in 1977. His main contention was that one needed to investigate the data before making any assumptions or constructing any models because the data would tell its own story.
Why is EDA Important?
EDA is much more than a technical approach; it is a critical thinking procedure. Below are the reasons why EDA is so important:
- Helps you to know the structure and size of the data set
- Identifies any missing data, duplicates, or even data errors
- Provides insights into the spread of the data variables
- It reveals connections between various columns
- It allows you to detect any outlier which might impact your analysis
- It helps you decide which algorithm to use for the analysis
- It prevents you from wasting time on troubleshooting further down the road
Key Steps to Perform EDA
Now let us understand how to actually do Exploratory Data Analysis. Here is a simple step-by-step process that every data professional follows:
Step 1 – Understand the Dataset
The first thing that you should do is to know your data by getting an initial overview. In other words, how big is the dataset in terms of the number of rows and columns? What are the names of the columns, and what types of data do they contain?
Step 2 – Check for Missing Values
In practice, data is never error-free. Missing values, which are entries that have no value whatsoever, are bound to exist. It is essential in EDA to pinpoint these holes and determine what should be done about them. They can be replaced with average values, deleted, or investigated further.
Step 3 – Understand Data Distribution
In this case, it is important to examine how data points are distributed across all columns. Is there a concentration around a central value, with some extremely high or low values on either side? Histograms, box-plots, and kernel density plots provide useful ways to represent the distribution of values.
Step 4 – Find and Handle Outliers
Outliers refer to those observations that are much farther from the other observations in your data set. For instance, suppose the expenditure of most clients lies between 500 and 5000 rupees; however, there exists one client whose expenditure is 50 lakhs rupees. This observation would be considered an outlier.
Step 5 – Explore Relationships Between Variables
That’s when things become very exciting. Relationships among columns begin to emerge; does increased age result in increased spending? Does rainfall influence crop production? These are just two examples of questions that could be answered using scatterplots, correlation tables, and heat maps.
Step 6 – Visualize the Data
Data visualization forms one of the strongest components of EDA. Transforming data into visual representations helps detect patterns, trends, and anomalies easily. Python packages such as Matplotlib, Seaborn, and Plotly are commonly used for data visualization.
Step 7 – Draw Initial Insights
Having explored and plotted the data, you will now begin to make some early observations based on what you see. What trends did you observe in your data? What were some unexpected things you learned from your data? Do you still have unanswered questions?
Tools Used for EDA
Here are some of the most commonly used tools and libraries for performing EDA:
- Python
- Pandas
- NumPy
- Matplotlib and Seaborn
- Plotly
- Jupyter Notebook
EDA in Real Life – A Simple Example
Consider the case where you are working for some e-commerce firm, and you have been provided with a dataset of the orders placed in the past year. Before starting the development of your recommendation system and even forecasting. This is all about EDA.
Conclusion
Once you have decided that you are ready to develop yourself in this regard and move forward in your career, consider taking up the complete Data Analyst Course in Jaipur, which includes EDA, data visualization, Python, SQL, and many other aspects essential for being a competent data analyst. Your adventure with data begins with just a single step.
