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Data Science —
Python & Analytics

Turn raw data into actionable insights. Master Python data tools — Pandas, NumPy, Matplotlib, and Seaborn — then bridge into machine learning with real datasets.

📘 36 Lessons
⏱ ~24 hrs
🎯 Intermediate
🌐 English
⭐ 4.8 (1.4k reviews)
👥 8,600 enrolled
Free
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This course includes

✓ 36 structured lessons
✓ Pandas & NumPy mastery
✓ Data visualization
✓ 3 real dataset projects
✓ ML intro module
✓ Certificate on completion
Overview
Curriculum
Requirements
Instructor

What you'll learn

✓ NumPy arrays and vectorized operations
✓ Pandas: DataFrames, groupby, merging
✓ Data cleaning and preprocessing
✓ Exploratory Data Analysis (EDA)
✓ Matplotlib & Seaborn visualizations
✓ Statistical thinking & distributions
✓ Correlation, regression, and hypothesis testing
✓ Intro to ML with real datasets

Course Curriculum

Module 1 — Python for Data
6 lessons · ~3 hrs
⌄
▶ Jupyter Notebook Setup 10 min
▶ NumPy Arrays & Operations 24 min
▶ Broadcasting & Indexing 18 min
▶ List Comprehensions for Data 14 min
▶ Lambda & Functional Tools 16 min
📝 Module Quiz 10 min
Module 2 — Pandas Mastery
10 lessons · ~5 hrs
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▶ Series & DataFrames 22 min
▶ Reading CSV, JSON, Excel 16 min
▶ Filtering & Selection 18 min
▶ GroupBy & Aggregation 22 min
▶ Merging & Joining DataFrames 20 min
▶ Handling Missing Data 18 min
▶ Pivot Tables 16 min
▶ Time Series Data 20 min
▶ apply() & map() 14 min
🛠 Project: Netflix Dataset EDA —
Module 3 — Visualization
8 lessons · ~4 hrs
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▶ Matplotlib Basics 18 min
▶ Line, Bar & Scatter Plots 20 min
▶ Subplots & Layouts 16 min
▶ Seaborn Overview 18 min
▶ Heatmaps & Pair Plots 14 min
▶ Histograms & Distributions 16 min
▶ Styling & Themes 12 min
🛠 Project: Covid-19 Visualization Dashboard —

Requirements

Instructor

📊
Sneha Pillai
Data Scientist · Analytics Lead

Sneha is a data scientist with 7 years of experience in analytics, business intelligence, and ML. She has led data teams at e-commerce and healthcare companies and is passionate about making data science understandable to everyone.