Python for Business Analysts – From Data to Insights

About Course

This hands-on program is designed to help business analysts at Campus Shoes unlock the power of Python for data handling, cleaning, and analysis. Over two focused sessions, participants will move from basic Python concepts to real-world business applications — learning how to clean messy Excel files, merge multiple data sources, and uncover insights through data visualization.

The course blends practical coding exercises, case studies, and assignments tailored to retail and business data. By the end of this course, participants will be able to confidently handle raw data, automate routine reporting tasks, and create insightful visuals for decision-making.

Key Learning Outcomes:

Understand Python fundamentals with real business use cases
Import, clean, and merge data from multiple sources (Excel, CSV, SQL)
Apply Pandas and NumPy for efficient data handling
Perform exploratory data analysis (EDA) to identify key trends
Build visualizations using Matplotlib and Seaborn
Automate repetitive Excel-style tasks using Python scripts
Create an end-to-end data analysis report

Course Content

Get going with Python
In this section we will learn what is programming, python installation using anaconda and various IDEs where you can code in Python.

Getting started with Python

09:04
Python Installation and IDEs

06:33
Using Spyder IDE from Anconda Installation

06:02
Guest Post
Quiz-Get Going With Python

Data types used in Python
1. Integers 2. Float 3. Complex numbers 4. Boolean 5. Strings

Operators in Python
Operators are used to perform various operations on different variables or values. This lesson talks about all those operators along with their types.

Data structures used in Python

Conditions and Loops

Functions

Pandas Library

Introduction to pandas library (article)
Basics of Pandas, Series and Dataframes

11:52
Import csv file with pandas read_csv()

16:26
Dataframes (article)
Indexing and Selection (article)
Pandas DataFrame Concatenate

09:24
Pandas DataFrame Join

14:45
Pandas DataFrame Merge

05:56
Working with DataFrames

13:32
Quiz-Pandas Library and DataFrames

Exceptional Handling
Errors and bugs are part and parcel of any programming language. So we need to learn a way to handle them and exceptional handling is one such tool.

SQL within Python
Execute SQL queries within Python for analysis.

Mini Case Study: Merge & Clean Datasets
Combine multiple data sources and handle data quality issues

Assignment: Clean & Summarize Multi-File Dataset
Consolidate multiple data sources and prepare a report

Real-World Business Applications
Apply Python to solve real-world business problems

Exploratory Data Analysis & Visualization
Perform EDA and create insightful charts

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About Course

Key Learning Outcomes:

Course Content

Get going with Python In this section we will learn what is programming, python installation using anaconda and various IDEs where you can code in Python.

Getting started with Python

Python Installation and IDEs

Using Spyder IDE from Anconda Installation

Guest Post

Quiz-Get Going With Python

Data types used in Python 1. Integers 2. Float 3. Complex numbers 4. Boolean 5. Strings

Data Types

Strings – Part 1

Strings – Part 2

Quiz-Data Types

Operators in Python Operators are used to perform various operations on different variables or values. This lesson talks about all those operators along with their types.

Arithmetic Operator

Assignment operators

Comparison operators

Logical operators

Identity operators

Membership operators

Quiz-Operators

Data structures used in Python

List

Quiz – Python Lists

Tuples

Dictionaries

Sets

List and Dictionary Comprehension

Conditions and Loops

If Else conditional statement

Loops

Quiz: Loops and Conditional Statements

Functions

How to write, call and other basics of functions (article)

Creating functions in python

Quiz- Functions

Pandas Library

Introduction to pandas library (article)

Basics of Pandas, Series and Dataframes

Import csv file with pandas read_csv()

Dataframes (article)

Indexing and Selection (article)

Pandas DataFrame Concatenate

Pandas DataFrame Join

Pandas DataFrame Merge

Working with DataFrames

Quiz-Pandas Library and DataFrames

Exceptional Handling Errors and bugs are part and parcel of any programming language. So we need to learn a way to handle them and exceptional handling is one such tool.

Exceptional Handling- Try, Except, Raise

Quiz-Exception Handling

SQL within Python Execute SQL queries within Python for analysis.

Introduction to SQLite

SELECT, WHERE, ORDER BY

Import Query Result into Pandas

Mini Exercise: Query Orders

Mini Case Study: Merge & Clean Datasets Combine multiple data sources and handle data quality issues

Merge Datasets

Clean Column Names

Handle Missing Values

Perform Joins

Group Activity

Assignment: Clean & Summarize Multi-File Dataset Consolidate multiple data sources and prepare a report

Merge 3–4 CSV/Excel Files

Handle Missing Values

Generate Summary

Automate Excel Cleanup

Real-World Business Applications Apply Python to solve real-world business problems

Cleaning Messy Sales Data

Combining Data Sources

Automating Reports

Identifying Top Customers

Forecasting Demand

Exploratory Data Analysis & Visualization Perform EDA and create insightful charts

Handle Missing Values & Duplicates

groupby & Descriptive Stats

Create Visualizations

Visualization Hands-On

Final Assignment

Earn a certificate