The first step before building models is to understand your dataset, which is known as Exploratory Data Analysis (EDA). This will help you identify patterns, identify errors, and ask the correct questions.
When looking for the Best Institute for Data Science in Kolkata, you will find that EDA is one of the first skills taught in any good course. Let's take a look at an example case study to see how this can be applied.
The Business Problem
Suppose that you work at an online retail company. Sales have been decreasing in the last 3 months, and your manager wants to understand why. You have a dataset containing information about customer orders, product types, price, delivery times, and customer ratings. Explore this dataset to extract some useful information.
Step 1: Understand the Data
This is basic information, so it's good to know how many rows and columns there are, what each column represents, and what type of data we're dealing with. In Python, this can be done easily using functions such as info() and describe(). This will give us an overview of our data.
Step 2: Clean the Data
Real data is always messy. In this case, you may find:
Missing values in the rating column
Duplicate orders
Wrong dates or unusual prices
To correct these issues, you can fill or drop missing values, remove duplicates, and look at outliers. Once the data has been cleaned, you will be able to trust your results.
Step 3: Find Patterns
Now the real exploration begins. You create charts and ask questions:
Which product categories sell the most, and which have dropped?
Do sales change by month or by city?
Are late deliveries linked to low ratings?
Bar graphs, line graphs, and heat maps can be created using tools such as Matplotlib, Seaborn, or Power BI. This gives a clear view of any patterns in the data.
Step 4: Share the Insights
Suppose we find that 1 category of products has had large declines in sales and most people buying products from this category have long delivery times. Customers with longer delivery times leave worse feedback and don’t buy as much. This can help the company to know what to improve.
Conclusion
EDA is the process of transforming raw data into meaningful insights. Thus, it is an essential part of any decent data science project. Enroll in a program that includes hands-on projects and guidance from experts.
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