AI-Powered Python for Data Science & Data Engineering

About Course

Most Python courses were designed before AI changed how data work gets done. Today, AI can write code in seconds. But someone still has to know what to ask for, read what the AI produced, and catch the mistakes before they reach a report or a manager’s inbox.

This course trains you to be that person.

You’ll learn Python, NumPy, pandas, visualization and statistics properly, so you understand what every line of code does to your data. Then you’ll move into real data engineering: Git and GitHub, APIs, SQL, PySpark, Delta Lake, and scheduled ETL pipelines on Databricks. Finally, you’ll deliver results to the business through interactive Dash dashboards, automated Excel, Word and PDF reports, and delivery by Outlook email and SharePoint.

Every lesson follows a four-step method:

Understand the concept: a clear explanation and a small hands-on example, without AI.
Read the code: learn to explain AI-generated code line by line.
Build with AI: write a clear specification and let Claude Code or the Databricks Assistant build it.
Verify the result: check row counts, totals and edge cases, and find the bugs AI introduces.

You’ll work in Databricks, the cloud platform used by data teams worldwide, and in VS Code with Claude Code for larger projects. All exercises use one realistic business dataset from a fictional airline’s customer-experience operation, so every module builds on the last.

The course ends with a capstone project that you publish on GitHub as a portfolio piece. You’ll build a complete, scheduled pipeline that ingests raw data, transforms it through bronze, silver and gold layers, runs statistical analysis, powers a live dashboard, and emails automated reports to managers.

Write and read Python with confidence, from basic syntax to functions, generators and classes
Clean, reshape, join and analyze data in depth with pandas (40 lessons)
Create clear charts with Matplotlib, Seaborn and Plotly
Run hypothesis tests, A/B tests and regression with SciPy and statsmodels
Manage projects professionally with Git, GitHub, uv and Ruff
Ingest data from files, REST APIs and SQL databases
Process large datasets with PySpark and Spark SQL
Build a lakehouse with Delta Lake and the medallion architecture
Build ETL pipelines with data quality checks, and schedule them with Databricks Jobs
Build and deploy interactive web dashboards with Dash
Generate Excel, Word and PDF reports automatically
Deliver reports through Outlook and SharePoint with Microsoft Graph
Use Claude Code and the Databricks Assistant productively, and verify everything they produce

Course Content

Module 0: Setup and the AI Workflow

0.1 Course roadmap and the SkyCare dataset
0.2 Databricks Free Edition tour
0.3 Loading the course data
0.4 Desktop setup
0.5 How to work with an AI coding assistant
0.6 AI risks in data work

Module 1: Python Essentials with AI Pair Programming

1.1 How Python code runs
1.2 Variables and naming
1.3 Numbers and arithmetic
1.4 Booleans and comparisons
1.5 Type conversion
1.6 Strings basics
1.7 String methods and f-strings
1.8 Lists
1.9 Tuples and unpacking
1.10 Dictionaries
1.11 Sets
1.12 Nested data and mutability
1.13 Conditions
1.14 for loops
1.15 while loops and loop control
1.16 Comprehensions
1.17 Functions basics
1.18 Functions in depth
1.19 Iterators and generators
1.20 Modules and the standard library
1.21 Errors and exceptions
1.22 Working with files
1.23 Classes 1: Classes and objects
1.24 Classes 2: Methods
1.25 Classes 3: Inheritance
1.26 Classes 4: Special methods and dataclasses
1.27 Reading and debugging AI code

Module 2: NumPy for Fast Numerical Computing

Module 3: Data Analysis with pandas

3.1 Why pandas and the Series
3.2 The DataFrame
3.3 Creating DataFrames
3.4 Reading CSV files
3.5 Reading Excel files
3.6 Reading JSON and Parquet
3.7 Writing data
3.8 Inspecting a dataset
3.9 Counting and profiling
3.10 Selecting columns and rows
3.11 Boolean filtering
3.12 Filtering with query()
3.13 Sorting and ranking
3.14 Finding missing values
3.15 Handling missing values
3.16 Duplicates
3.17 Data types and conversion
3.18 Columns and index management
3.19 Cleaning text with .str
3.20 Regex with pandas
3.21 Working with dates
3.22 Time series basics
3.23 Creating new columns
3.24 apply, map and replace
3.25 Binning values
3.26 GroupBy basics
3.27 GroupBy advanced
3.28 Pivot tables
3.29 Crosstab and pivot
3.30 Melt: wide to long
3.31 Stack, unstack and MultiIndex
3.32 Merging DataFrames
3.33 Merge pitfalls
3.34 Combining with concat
3.35 Period comparisons
3.36 Rolling and cumulative metrics
3.37 Method chaining and pipe
3.38 Performance and memory
3.39 Styling tables
3.40 Verifying AI-written pandas code

Module 4: Data Visualization (Matplotlib, Seaborn, Plotly)

Module 5: Statistics with SciPy and statsmodels

Module 6: Professional Python Workflow: Git, GitHub, uv and Ruff

6.1 Introduction to Git and GitHub
6.2 Git fundamentals
6.3 Installing Git
6.4 Basic Git workflow
6.5 Undoing changes
6.6 GitHub account setup and authentication
6.7 Cloning, push and pull
6.8 Creating repositories and .gitignore
6.9 Branches and merging
6.10 Pull requests and code review
6.11 Merge conflicts
6.12 Using Git in the VS Code UI
6.13 Git folders in Databricks
6.14 Environment variables and secrets (.env)
6.15 Using the python-dotenv package
6.16 Introduction to Ruff
6.17 Setting up Ruff in VS Code
6.18 Ruff in action
6.19 Introduction to uv
6.20 Installing uv
6.21 Using uv (init, add, sync, run)
6.22 Git with Claude Code
6.23 Complete Python project workflow exercise

Module 7: Data Sources: Files, APIs and SQL

Module 8: PySpark and Spark SQL

8.1 Why Spark
8.2 Spark DataFrames
8.3 Defining schemas
8.4 Transformations
8.5 Aggregations
8.6 Joins in Spark
8.7 Window functions
8.8 Spark SQL
8.9 pandas vs PySpark
8.10 Performance basics

Module 9: Delta Lake and the Lakehouse

Module 10: ETL Pipelines, Data Quality and Scheduling

Module 11: Web Dashboards with Dash

Module 12: Automated Reporting: Excel, Word, PDF

12.1 Report automation design
12.2 Excel with XlsxWriter
12.3 Excel with openpyxl
12.4 Word reports with python-docx
12.5 Templates with Jinja2
12.6 Word templates with docxtpl
12.7 PDF reports with ReportLab
12.8 Working with PDFs using pypdf
12.9 One report per team
12.10 Scheduling report generation

Module 13: Microsoft 365 Automation

Module 14: Capstone: End-to-End AI-Built Pipeline

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AI-Powered Python for Data Science & Data Engineering

About Course

What Will You Learn?

Course Content

Module 0: Setup and the AI Workflow

0.1 Course roadmap and the SkyCare dataset

0.2 Databricks Free Edition tour

0.3 Loading the course data

0.4 Desktop setup

0.5 How to work with an AI coding assistant

0.6 AI risks in data work

Module 1: Python Essentials with AI Pair Programming

1.1 How Python code runs

1.2 Variables and naming

1.3 Numbers and arithmetic

1.4 Booleans and comparisons

1.5 Type conversion

1.6 Strings basics

1.7 String methods and f-strings

1.8 Lists

1.9 Tuples and unpacking

1.10 Dictionaries

1.11 Sets

1.12 Nested data and mutability

1.13 Conditions

1.14 for loops

1.15 while loops and loop control

1.16 Comprehensions

1.17 Functions basics

1.18 Functions in depth

1.19 Iterators and generators

1.20 Modules and the standard library

1.21 Errors and exceptions

1.22 Working with files

1.23 Classes 1: Classes and objects

1.24 Classes 2: Methods

1.25 Classes 3: Inheritance

1.26 Classes 4: Special methods and dataclasses

1.27 Reading and debugging AI code

Module 2: NumPy for Fast Numerical Computing

2.1 Why NumPy

2.2 Creating arrays

2.3 Indexing and slicing

2.4 Vectorized operations and broadcasting

2.5 Aggregations and statistics

2.6 NumPy inside pandas

Module 3: Data Analysis with pandas

3.1 Why pandas and the Series

3.2 The DataFrame

3.3 Creating DataFrames

3.4 Reading CSV files

3.5 Reading Excel files

3.6 Reading JSON and Parquet

3.7 Writing data

3.8 Inspecting a dataset

3.9 Counting and profiling

3.10 Selecting columns and rows

3.11 Boolean filtering

3.12 Filtering with query()

3.13 Sorting and ranking

3.14 Finding missing values

3.15 Handling missing values

3.16 Duplicates

3.17 Data types and conversion

3.18 Columns and index management

3.19 Cleaning text with .str

3.20 Regex with pandas

3.21 Working with dates

3.22 Time series basics

3.23 Creating new columns

3.24 apply, map and replace

3.25 Binning values

3.26 GroupBy basics

3.27 GroupBy advanced

3.28 Pivot tables

3.29 Crosstab and pivot

3.30 Melt: wide to long

3.31 Stack, unstack and MultiIndex

3.32 Merging DataFrames

3.33 Merge pitfalls