Data Analysis with Python: From Fundamentals to Insights

Learn to clean, manipulate, visualize, and analyze data using Python libraries like Pandas, NumPy, and Matplotlib—no prior experience required.

Course Objectives

• Set up a Python environment for data analysis with Jupyter Notebook.
• Load, clean, and transform datasets using Pandas and NumPy.
• Create visualizations with Matplotlib and Seaborn to identify trends.
• Perform statistical summaries and exploratory data analysis (EDA).
• Apply basic machine learning models for predictive insights.

Course Overview

This hands-on course introduces the core tools and workflows used in data analysis with Python. Whether you’re working with spreadsheets, databases, or raw files, you’ll learn how to extract insights from data using efficient, readable Python code. Real-world datasets and step-by-step projects guide learners from foundational techniques to applied data exploration and modeling.

Sample Module: Working with Tabular Data in Pandas

This module introduces Pandas, Python’s most powerful data manipulation library. Participants will learn how to load CSV files, inspect dataframes, filter rows, and perform basic aggregations and joins.

Lesson: Cleaning and Preparing Your Data

In this lesson, learners will use Pandas to identify missing values, convert data types, handle outliers, and standardize column names—essential steps before any meaningful analysis can take place.