Complete Data Science & AI Professional Program

From Everyday Data to Useful Decisions

Reading Records, Fields, and Data Sources

Asking Measurable Questions

The Analyst Workflow: Check, Explore, Explain

Analytics, Data Science, and Team Roles

Artificial Intelligence and Machine Learning

Generative AI and Large Language Models

Writing Clear Prompts and Checking Replies

Responsible Data and AI Use

Mini Project: An Evidence-Based Attendance Brief

Setting Up Python and Running Your First Script

Variables and Core Data Types

Working with Text: Strings and Formatting

Lists: Storing Ordered Data

Tuples and Sets: Fixed Records and Unique Values

Dictionaries: Key-Value Records

Making Decisions: if, elif and else

Repeating Work: for and while Loops

List and Dictionary Comprehensions

Functions: Packaging Reusable Analysis Steps

Handling Errors with try and except

Modules and the Python Standard Library

Reading and Writing Files, Including CSV

NumPy Arrays: Fast Numeric Computation

pandas Series and DataFrames: Loading Your Data

Selecting, Filtering and Sorting in pandas

Cleaning Data: Missing Values, Types and Duplicates

Grouping and Aggregating with groupby

Combining Datasets with merge and concat

Visualising Results with Matplotlib

Numbers, Expressions, and Functions

Vectors: Ordered Values and Movement

Vector Length, Distance, and Dot Products

Matrices: Shape, Entries, and Transpose

Matrix Multiplication and Linear Transformations

Solving Linear Systems and Checking Inverses

Span, Independence, Basis, and Rank

Eigenvalues and Eigenvectors: Stable Directions

Change, Limits, and Continuity

Derivatives: Slope, Rules, and the Chain Rule

Partial Derivatives, Gradients, and a Descent Step

Integrals: Accumulation and the Fundamental Theorem

Introduction to Statistics: Data Types, Measurement Scales & Sampling

Measures of Central Tendency: Mean, Median & Mode

Measures of Dispersion: Range, Variance, Standard Deviation & IQR

Distribution Shape: Skewness, Kurtosis, Percentiles & Quartiles

Normal Distribution, Z-Scores & Standardization

Probability & Probability Distributions

Binomial & Poisson Distributions

Central Limit Theorem & Sampling Distributions

Confidence Intervals & Hypothesis Testing

P-Values, T-Tests, ANOVA & Chi-Square Test

Correlation & Covariance

Simple & Multiple Linear Regression

Outliers, Anomaly Detection & Introduction to Bayesian Statistics

Project 1: Exploratory Statistical Analysis of a Real Dataset

Project 2: Hypothesis Testing & Regression Analysis Project

Introduction to NumPy

Indexing and Slicing Arrays

Multi-Dimensional Arrays

Shape and Reshape

Data Types in NumPy

NumPy Copy and View

Iteration in NumPy

NumPy Joins

NumPy Splits

NumPy Search

NumPy Sorting

NumPy Filtering

Introduction to Pandas and Setup

Creating and Accessing a Pandas Series

Series Attributes and First Look

Missing Values and Dropping Items in a Series

Series Statistics and Unique Values

Sorting, Membership and Modifying a Series

String Methods with the .str Accessor

Creating a Pandas DataFrame

Loading a DataFrame from CSV with read_csv

Selecting Rows with loc, iloc, at and iat

DataFrame Attributes and Shape

Adding, Modifying, Renaming and Deleting Rows and Columns

Indexing and Filtering a DataFrame

Iterating Over a DataFrame

Arithmetic Operations Between DataFrames

Summary Statistics for a DataFrame

Measuring Spread: Std, Variance, Quartiles and Skew

Cumulative Functions, Correlation and Covariance

Reshaping Data with pivot and pivot_table

Handling Missing Data in a DataFrame

Combining DataFrames and Grouping

Audit a Dataset and Define Its Contract

Standardize Text and Categories

Parse Numbers and Dates Without Losing Evidence

Choose a Missing-Value Policy

Resolve Duplicate Records and Conflicting Keys

Validate Ranges, Units, and Outliers

Join Reference Data Without Multiplying Rows

Protect Time Meaning in Cleaning

Build a Reusable Cleaning Function and Quality Checks

Cleaning Capstone: Reconcile a Messy Orders Batch

SQL for Business Analytics: Foundations

SELECT, Filter and Sort Business Data

Aggregations and Business KPIs

CASE, NULLs and Business Calculations

Date and Time Analysis

JOINs for Multi-Table Business Analysis

Subqueries for Business Questions

CTEs for Readable Analytics Queries

Window Functions for Advanced Analytics

Sales and Customer Analytics with SQL

Marketing and Operations Analytics with SQL

Data Quality Checks for Analysts

End-to-End SQL Business Analytics Case Study

Start an EDA: Business Questions and Data Grain

Audit Data Before Trusting It

Clean Missing Values, Labels and Numeric Types

Understand One Variable at a Time

Compare Segments with the Right Denominator

Investigate Relationships and Unusual Values

Join Tables Without Inflating Results

Case Study: What Explains Retail Performance Differences?

Parse Dates and Build Calendar Features

Handle Time Zones, Durations and Date Boundaries

Build a Regular Time Series from Event Data

Explore Trends, Seasonality and Period Changes

Case Study: Are Support Response Times Getting Worse?

Case Study: Explore Energy Demand and Collection Gaps

Capstone: Explain a Retail Revenue Dip with Evidence

Introduction to Matplotlib

Core Figure and Axes Concepts

Basic Plot Types in Matplotlib

Customization and Styling

Statistical and Scientific Plots

Advanced Layout and Composition

Working with Images and Patches

Animation

Interactive Features and Widgets

Saving, Exporting, and Output Formats

Matplotlib with Pandas and NumPy

Custom Artists and Rendering

Custom Backends and Renderers

Integration with Frameworks and Tools

Publication-Quality and Professional Visualization

Introduction to Seaborn

Python Data Visualization Fundamentals

Setting Up Seaborn Environment

Understanding Datasets

Distribution Plots

Relational-Plots

Categorical Data Visualization

Matrix and Heatmap Visualization

Pairwise and Multivariate Visualization

Styling and Customization

Statistical Analysis With Seaborn

Advanced Visualization Techniques

Real-World Applications

Expert-Level Topics

Tools, Libraries, and Ecosystem

Capstone Projects and Portfolio Building

Define Features, Targets, and an Honest Evaluation Split

Create Domain Features With Safe Arithmetic

Scale Numeric Features Using Training Statistics

Transform Skewed Features and Create Bins

Encode Categories and Handle Unseen Values

Engineer Date and Text Features Without Future Information

Filter Constant and Redundant Features

Use Target-Aware Filters Carefully

Train a Small Selection Benchmark and Measure Error

Compare Feature Subsets With Wrapper Selection

Read Model-Based and Permutation Importance

Assemble a Leakage-Safe Feature Pipeline

Frame a Learnable Business Question

Build Features and Targets From Trustworthy Records

Separate Training, Validation, and Final Test Data

Establish Baselines and Measure Prediction Error

Fit a First Learned Model and Inspect Its Predictions

Recognize Underfitting and Overfitting

Evaluate Classification Decisions and Tradeoffs

Keep Learned Preprocessing Inside the Training Workflow

Use Cross-Validation to Check Stability

Choose a Model With a Small Documented Experiment

Check Limits, Subgroups, and Monitoring Needs

Capstone: Deliver a Reproducible First ML Experiment

Frame a Regression Experiment

Interpret a Simple Linear Regression Equation

Understand Least Squares and Prediction Residuals

Trace Gradient Descent With a Small Numeric Example

Fit and Interpret Multiple Linear Regression

Choose Regression Metrics and Compare a Baseline

Read Residual Diagnostics and Check Assumptions

Encode Categories Inside a Regression Pipeline

Add Polynomial Features Without Leaking Validation Data

Control Coefficients With Ridge and Lasso

Select a Regression Workflow With Cross-Validation

Capstone: Deliver a Reviewed Price Prediction Model