Data Science

Analysis, visualization, and actionable insights

31 chapters across 7 modules

1

Statistics And Probability Foundations

6 chapters

1

Descriptive Statistics

6 min read

2

Probability Fundamentals

7 min read

3

Probability Distributions

7 min read

4

Sampling & the Central Limit Theorem

8 min read

5

Correlation & Covariance

7 min read

6

Hypothesis Testing Fundamentals

8 min read

2

Data Acquisition And Wrangling

5 chapters

1

Data Sources & Formats

5 min read

2

SQL for Data Science

7 min read

3

Data Cleaning Techniques

7 min read

4

Handling Missing & Duplicate Data

7 min read

5

Feature Engineering Basics

7 min read

3

Exploratory Data Analysis

5 chapters

1

The EDA Workflow

6 min read

2

Univariate Analysis

6 min read

3

Bivariate & Multivariate Analysis

6 min read

4

Outlier Detection

7 min read

5

EDA Case Study: Retail Customer Data

7 min read

4

Data Visualization

5 chapters

1

Visualization Principles

7 min read

2

Matplotlib Fundamentals

6 min read

3

Seaborn for Statistical Plots

7 min read

4

Interactive Visualization with Plotly

6 min read

5

Building a Dashboard with Streamlit

7 min read

5

Experimentation And Applied Statistics

5 chapters

1

A/B Testing Fundamentals

8 min read

2

Statistical Significance & p-values in Practice

8 min read

3

Common Experiment Design Pitfalls

8 min read

4

Time Series Analysis Basics

7 min read

5

Storytelling with Data

8 min read

6

Data Science In Practice

3 chapters

1

The Data Science Workflow

7 min read

2

Working with Data at Scale

7 min read

3

Capstone Project: E-Commerce Marketing Analysis

7 min read

7

Bridge To Machine Learning

2 chapters

1

From EDA to Modeling: What Changes

7 min read

2

Mapping This Curriculum to Machine Learning

5 min read