PYTHON FOR DATA ANALYSIS

Turn raw data into business insights using the powerful tools of python.

Use pandas & numpy to analyse datasets. visualize data with matplotlib & seaborn.

Pandas Seaborn Numpy Matplotlib

10 Weeks • Live Online / Offline • 15+ Commercial Datasets

python_data_lab.ipynb

sales_data.csv (12.4K rows)

In [3]: # Group by region and aggregate revenue

import pandas as pd
df = pd.read_csv("sales_data.csv")
df.groupby("region")["revenue"].sum().sort_values(ascending=False)

OUT [3]: PANDAS SERIES (GROUPED REVENUE) ● 28ms Execution

region	revenue	share
East	₹7.1M	28.7%
North	₹6.2M	24.9%
South	₹5.8M	23.5%
West	₹5.7M	22.9%

Analytical Finding: East Territory (₹7.1M) drives highest regional revenue velocity.

ANALYSIS READY

STEP 01

1. Work with Data Using Python

Use Python to import, read, and work with data from Excel, CSV, databases, and other sources.

STEP 02

2. Clean & Manipulate Data

Use Pandas and NumPy to handle missing data, remove duplicates, filter, sort, merge, transform, and manipulate datasets.

STEP 03

3. Perform Exploratory Data Analysis

Explore datasets, identify patterns and trends, calculate statistics, detect outliers, and discover meaningful relationships within the data.

STEP 04

4. Create Data Visualizations

Use Matplotlib and Seaborn to create charts and visualizations that make complex data easier to understand and communicate.

STEP 05

5. Build Real-World Data Analysis Projects

Apply Python skills to practical datasets, perform complete data analysis projects, and develop a portfolio that demonstrates your data analytics skills.