Python for Data Science

About Course

Turn raw data into real insight using Python. This course covers everything from core programming concepts to data wrangling with Pandas and building visualizations with Matplotlib and Seaborn. It’s built for people who want practical, job-relevant skills — not just theory.

Write Python code for data manipulation and analysis
Load, clean, and explore datasets with Pandas
Build charts and visualizations with Matplotlib and Seaborn
Identify trends and patterns in real-world data
Structure a basic data analysis workflow
Use NumPy arrays and interpret axes and missing values
Audit cleaning rules and validate joins
Evaluate a prediction baseline without data leakage
Package a reproducible analysis with honest limitations

Course Content

Topic 1: Python Basics
Build a solid programming foundation before diving into data.

Lesson 1: Data Types, Variables & Operators
Lesson 2: Loops, Functions & File Handling
Python & Pandas Essentials
Analyze a Public Dataset

Python for Data Science

About Course

What Will You Learn?

Course Content

Topic 1: Python Basics
Build a solid programming foundation before diving into data.

Lesson 1: Data Types, Variables & Operators

Lesson 2: Loops, Functions & File Handling

Python & Pandas Essentials

Analyze a Public Dataset

Topic 2: Data Analysis with Pandas
Wrangle real-world datasets like a pro.

Lesson 1: Loading & Exploring Datasets

Python for Data Science – Knowledge Check

Lesson 2: Filtering, Grouping & Aggregating Data

Topic 3: Numerical Computing with NumPy
Work with arrays, vectorized calculations, shapes, and reproducible numerical summaries.

Lesson 1: Arrays, Shapes & Vectorized Calculations

Lesson 2: Axes, Missing Values & Reproducible Samples

NumPy Reasoning Check

Topic 4: Data Quality, Cleaning & Joins
Create explicit cleaning rules, preserve raw data, and combine tables without silently changing their meaning.

Lesson 1: Build a Transparent Cleaning Workflow

Lesson 2: Combine Tables Without Duplicating Results

Audit & Combine a Messy Orders Dataset

Topic 5: Visualization & Statistical Reasoning
Choose informative charts, describe distributions, and separate evidence from unsupported conclusions.

Lesson 1: Turn Analysis Questions into Clear Charts

Lesson 2: Describe Variation & Avoid Causal Overclaims

Charts & Evidence Check

Topic 6: Introductory Models & Reproducible Projects
Build a baseline, prevent data leakage, evaluate errors, and package a clear analysis.

Lesson 1: Baselines, Train/Test Splits & Leakage

Lesson 2: Evaluate Errors & Package a Reproducible Notebook

Capstone: Reproducible Python Analysis & Baseline

About Course

What Will You Learn?

Course Content

Topic 1: Python Basics Build a solid programming foundation before diving into data.

Lesson 1: Data Types, Variables & Operators

Lesson 2: Loops, Functions & File Handling

Python & Pandas Essentials

Analyze a Public Dataset

Topic 2: Data Analysis with Pandas Wrangle real-world datasets like a pro.

Lesson 1: Loading & Exploring Datasets

Python for Data Science – Knowledge Check

Lesson 2: Filtering, Grouping & Aggregating Data

Topic 3: Numerical Computing with NumPy Work with arrays, vectorized calculations, shapes, and reproducible numerical summaries.

Lesson 1: Arrays, Shapes & Vectorized Calculations

Lesson 2: Axes, Missing Values & Reproducible Samples

NumPy Reasoning Check

Topic 4: Data Quality, Cleaning & Joins Create explicit cleaning rules, preserve raw data, and combine tables without silently changing their meaning.

Lesson 1: Build a Transparent Cleaning Workflow

Lesson 2: Combine Tables Without Duplicating Results

Audit & Combine a Messy Orders Dataset

Topic 5: Visualization & Statistical Reasoning Choose informative charts, describe distributions, and separate evidence from unsupported conclusions.

Lesson 1: Turn Analysis Questions into Clear Charts

Lesson 2: Describe Variation & Avoid Causal Overclaims

Charts & Evidence Check

Topic 6: Introductory Models & Reproducible Projects Build a baseline, prevent data leakage, evaluate errors, and package a clear analysis.

Lesson 1: Baselines, Train/Test Splits & Leakage

Lesson 2: Evaluate Errors & Package a Reproducible Notebook

Capstone: Reproducible Python Analysis & Baseline

Topic 1: Python Basics
Build a solid programming foundation before diving into data.

Topic 2: Data Analysis with Pandas
Wrangle real-world datasets like a pro.

Topic 3: Numerical Computing with NumPy
Work with arrays, vectorized calculations, shapes, and reproducible numerical summaries.

Topic 4: Data Quality, Cleaning & Joins
Create explicit cleaning rules, preserve raw data, and combine tables without silently changing their meaning.

Topic 5: Visualization & Statistical Reasoning
Choose informative charts, describe distributions, and separate evidence from unsupported conclusions.

Topic 6: Introductory Models & Reproducible Projects
Build a baseline, prevent data leakage, evaluate errors, and package a clear analysis.