📚 All Lessons
Browse all 108 lessons in the curriculum. Use the search box and tag filters below to find specific topics.
Day 1: Python for Business Analytics - First Steps
Welcome, future business leader! You're about to take your first step into a larger world of
Day 2: Storing and Analyzing Business Data
In Day 1, we performed basic calculations. Now, we'll learn how to store data in **variables** and
Day 3: Operators - The Tools for Business Calculation and Logic
An **operator** is a symbol that tells the computer to perform a specific mathematical or logical
Day 4: Working with Text Data - Strings
In business analytics, text data is everywhere—customer names, product reviews, addresses, and
Day 5: Managing Collections of Business Data with Lists
In business, you often work with collections of data: lists of customers, quarterly sales figures,
Day 6: Tuples - Storing Immutable Business Data
While lists are great for data that changes, sometimes you need to store data that *shouldn't*
Day 7: Sets - Managing Unique Business Data
We've seen lists for ordered data and tuples for immutable data. Now we'll learn about **sets**,
Day 8: Dictionaries - Structuring Complex Business Data
Real-world business data is structured. A customer has a name, an email, and a location. A product
Day 9: Conditionals - Implementing Business Logic
Business is full of rules and decisions. "If a customer spends over $100, they get a 10% discount."
Day 10: Loops - Automating Repetitive Business Tasks
What if you have a list of 10,000 sales transactions? You won't write code for each one. This is
Day 11: Functions - Creating Reusable Business Tools
As you perform more complex analysis, you'll write the same code repeatedly. **Functions** are
Day 12: List Comprehension - Elegant Data Manipulation
In data analysis, you constantly create new lists by transforming or filtering existing ones. While
Day 13: Higher-Order Functions & Lambda
A **higher-order function** is a function that takes another function as an argument or returns a
Day 14: Modules - Organizing Your Business Logic
As your projects grow, you need a way to organize your code. In Python, we do this with **modules**.
Day 15: Exception Handling - Building Robust Business Logic
In the real world, data is messy and operations can fail. A file might be missing, a user might
Day 16: File Handling for Business Analytics
A huge part of data analysis involves reading data from files and writing results to them. Whether
Day 17: Regular Expressions for Text Pattern Matching
Often, the text data you need to analyze isn't perfectly structured. You might need to find all the
Day 18: Classes and Objects - Modeling Business Concepts
So far, we've organized our code with functions. But what if you want to model a real-world concept,
Day 19: Working with Dates and Times
Time-series analysis is at the heart of business analytics. Whether you're tracking daily sales,
Day 20: Python Package Manager (pip) & Third-Party Libraries
The real power of Python for data analysis comes from its vast ecosystem of third-party libraries.
Day 21: Virtual Environments - Professional Project Management
As you work on more complex projects, you'll find they have different requirements. Project A might
Day 22: NumPy - The Foundation of Numerical Computing
While Python lists are flexible, they aren't efficient for large-scale numerical calculations. For
Day 23: Pandas - Your Data Analysis Superpower
If NumPy is the foundation, **Pandas** is the tool you will use every single day for practical data
Day 24: Advanced Pandas - Working with Real Data
You'll rarely create data from scratch. The most common workflow is to load data from external
Day 25: Data Cleaning - The Most Important Skill in Analytics
It's often said that data analysts spend about 80% of their time cleaning and preparing data. Messy,
Day 26: Practical Statistics for Business Analysis
On Day 26 you expand beyond data wrangling and apply core statistical tools to business datasets.
Day 27: Data Visualization - Communicating Insights
Visualising key business metrics makes it easier to communicate findings and uncover patterns. Day
Day 28: Advanced Visualization & Customization
Creating a basic chart is just the first step. To effectively communicate your story, you need to
Day 29: Interactive Visualization with Plotly
Static charts are good for reports, but in the modern era of business intelligence, users expect to
Day 30: Web Scraping - Extracting Data from the Web
Sometimes, the data you need isn't available in a clean CSV file or through an API. It's simply
Day 31: Working with Databases in Python
While CSV files are great for smaller datasets, most real-world business data is stored in
Day 32: Connecting to Other Databases (MySQL & MongoDB)
In the previous lesson, we used `sqlite3`, which is fantastic for learning and small projects
Day 33: Accessing Web APIs with `requests`
This lesson introduces a lightweight wrapper around the
Day 34: Building a Simple API with Flask
Consuming data from APIs is a core skill. But what if you need to provide data from your analysis to
Day 35: Flask Web Framework
Welcome to Day 35! This lesson contains a small Flask project that analyses submitted text and
Day 36 – Capstone Case Study
Day 36 ties together the full analytics workflow. You will load the `case_study_sales.csv` dataset,
Day 37: Conclusion & Your Journey Forward
You did it! You have successfully completed the core analytics track of the 50-Day Python for
Day 38: Math Foundations - Linear Algebra
Linear algebra underpins much of machine learning. This lesson revisits the building blocks—vectors,
Day 39: Math Foundations - Calculus
Calculus powers the optimisation routines that train machine learning models. Derivatives,
Day 40: Introduction to Machine Learning & Core Concepts
This lesson introduces the machine learning workflow and highlights how evaluation techniques ensure
Day 41 · Supervised Learning – Regression
- `solutions.py` – modular helpers for generating synthetic regression data, training a linear
Day 42 · Supervised Learning – Classification (Part 1)
- `solutions.py` – reusable helpers for loading the Iris dataset, scaling features, and training
Day 43 · Supervised Learning – Classification (Part 2)
- `solutions.py` – modular helpers for preparing the Iris dataset, fitting SVM and decision tree
Day 44: Unsupervised Learning
Day 44 introduces two foundational unsupervised learning workflows:
Day 45: Feature Engineering & Model Evaluation
Day 45 demonstrates how thoughtful preprocessing and rigorous evaluation combine to build
Day 46: Introduction to Neural Networks & Frameworks
Welcome to Day 46! Today, we begin our exploration of **Deep Learning** by introducing the
Day 47: Convolutional Neural Networks (CNNs) for Computer Vision
Welcome to Day 47! Today, we dive into **Convolutional Neural Networks (CNNs)**, a specialized type
Day 48: Recurrent Neural Networks (RNNs) for Sequence Data
Welcome to Day 48! Today, we explore **Recurrent Neural Networks (RNNs)**, a class of neural
Day 49: Natural Language Processing (NLP)
Day 49 introduces the classic feature-extraction techniques that turn raw text into numeric
Day 50: MLOps - Model Deployment
Welcome to our final day, Day 50! Today, we touch upon **MLOps (Machine Learning Operations)**,
Day 51 – Regularised Models
This lesson extends the regression toolkit with L2 (ridge), L1 (lasso), and elastic net penalties
Day 52 – Ensemble Methods
Day 52 highlights how bagging, boosting, and stacking unlock better accuracy than single estimators.
Day 53 – Model Tuning and Feature Selection
Optimisation is the bridge between baseline models and production-grade performance. Day 53
Day 54 – Probabilistic Modeling
Gaussian mixtures, Bayesian classifiers, expectation-maximisation, and hidden Markov models power
Day 55 – Advanced Unsupervised Learning
Density-based clustering, hierarchical approaches, and modern embeddings unlock structure within
Day 56 – Time Series and Forecasting
Seasonality-aware forecasting keeps plans grounded in data rather than gut feel. The lesson
Day 57 – Recommender Systems
Recommender systems pair users with relevant products when catalogues explode. This lesson covers
Day 58 – Transformers and Attention
Transformers dominate modern sequence modelling. This lesson demonstrates how to:
Day 59 – Generative Models
Generative models synthesise data, compress signals, and enable controllable creativity. In this
Day 60 – Graph and Geometric Learning
Graph neural networks capture relational structure beyond Euclidean grids. This lesson focuses on:
Day 61 – Reinforcement and Offline Learning
Reinforcement learning (RL) balances exploration and exploitation while offline evaluation keeps
Day 62 – Model Interpretability and Fairness
Explainable and responsible AI practices underpin trustworthy analytics. After this lesson you will:
Day 63 – Causal Inference and Uplift Modeling
Understand how experimentation and counterfactual reasoning quantify impact. After this lesson you
Day 64 – Modern NLP Pipelines
Connect discrete NLP components into a reproducible workflow. After this lesson you will:
Day 65 – MLOps Pipelines and CI/CD Automation
Day 50 introduced model persistence. Day 65 expands that foundation into production-grade automation
Day 66 – Model Deployment and Serving Patterns
Production machine learning systems expose predictions through a variety of runtime patterns.
Day 67 – Model Monitoring and Reliability Engineering
The final instalment of the MLOps arc closes the loop from deployment to operations. After mastering
Day 68 – BI Analyst Foundations
> This lesson is part of the Phase 5 Business Intelligence specialization. Use the
Day 69 – BI Strategy and Stakeholders
Day 69 extends the BI Analyst roadmap by pairing strategic constructs with the humans who bring them
Day 70 – BI Metrics and Data Literacy
Day 70 focuses on translating the BI roadmap's metrics and statistics nodes into a cohesive
Day 71 – BI Data Landscape Fundamentals
> This lesson expands the BI roadmap by grounding each data classification and source channel in
Day 72 – BI Data Formats and Ingestion
Business intelligence analysts encounter a wide mix of raw data files. This day focuses on
Day 73 – BI SQL and Databases
Day 73 rebuilds the SQL and database depth outlined in the BI roadmap so the track moves beyond the
Day 74 – BI Data Preparation and Tools
Day 74 focuses on the hands-on mechanics of preparing business intelligence data. We reinforce data
Day 75 – BI Visualization and Dashboard Principles
Day 75 codifies the BI visualization fundamentals that analysts rely on to communicate credibly, and
Day 76 – BI Platforms and Automation Tools
Day 76 explores the major BI delivery platforms alongside the scripting and standardisation
Day 77 – BI Domain Analytics and Value Drivers
Day 77 bridges the roadmap's domain-specific BI topics with tangible stakeholder value stories. The
Day 78 – BI Experimentation and Predictive Insights
This lesson connects experimentation design, forecasting, and machine learning so BI teams can run
Day 79 – BI Storytelling and Stakeholder Influence
Day 79 teaches analysts how to weave together storytelling craft and change leadership so executive
Day 80 – BI Data Quality and Governance
Day 80 fills the roadmap gap on enterprise-grade data quality programmes and governance frameworks.
Day 81 – BI Architecture and Data Modeling
Business intelligence teams translate raw data into governed insights. A clear architecture keeps
Day 82 – BI ETL and Pipeline Automation
Day 82 extends the roadmap by transforming the ETL and automation nodes into a workshop on
Day 83 – BI Cloud and Modern Data Stack
> This lesson is part of the Phase 5 Business Intelligence specialization. Use the
Day 84 – BI Career Development and Capstone
> This lesson closes Phase 5 by converting the roadmap insights into a polished career narrative.
Day 85 – Advanced SQL and Performance Tuning
This lesson delves into advanced SQL techniques and performance tuning, essential skills for any
Day 86 – BI in the Cloud
This lesson explores the landscape of Business Intelligence in the cloud. We will discuss the
Day 87 – Data Governance and Security
This lesson focuses on the critical importance of data governance and security in a BI environment.
Day 88 – Capstone Project - Part 1
This lesson marks the beginning of the capstone project. In this two-part project, you will apply
Day 89 – Capstone Project - Part 2
In the second part of the capstone project, you will focus on data analysis, visualization, and
Day 90 – Career Workshop and Next Steps
Welcome to your final lesson! This career workshop is designed to help you translate the technical
Day 91: Relational Databases
Welcome to Day 91! Today, we'll dive deep into the foundational concepts of **Relational
Day 92: Data Definition Language (DDL)
Welcome to Day 92! Today, we'll learn about **Data Definition Language (DDL)**, a subset of SQL used
Day 93: Data Manipulation Language (DML)
Welcome to Day 93! Today, we'll learn about **Data Manipulation Language (DML)**, a subset of SQL
Day 94: Data Query Language (DQL)
Welcome to Day 94! Today, we'll focus on the **Data Query Language (DQL)**, which is used to
Day 95: Joins
Welcome to Day 95! Today, we'll learn about **Joins**, a fundamental concept in SQL for combining
Day 96: Subqueries
Welcome to Day 96! Today, we'll explore **Subqueries**, also known as nested queries or inner
Day 97: Views
Welcome to Day 97! Today, we'll learn about **Views**, which are virtual tables based on the
Day 98: Indexes
Welcome to Day 98! Today, we'll learn about **Indexes**, a crucial performance-tuning feature in
Day 99: Transactions
Welcome to Day 99! Today, we'll learn about **Transactions**, a sequence of operations performed as
Day 100: Stored Procedures
Welcome to Day 100! Today, we'll learn about **Stored Procedures**, which are prepared SQL code that
Day 101: Triggers
Welcome to Day 101! Today, we'll learn about **Triggers**, which are special stored procedures that
Day 102: Common Table Expressions (CTEs)
Welcome to Day 102! Today, we'll learn about **Common Table Expressions (CTEs)**, a temporary result
Day 103: Pivoting Data
Welcome to Day 103! Today, we'll learn about **Pivoting Data**, a technique used to transform data
Day 104: Database Design and Normalization
Welcome to Day 104! Today, we'll cover the important topics of **Database Design** and
Day 105: JSON in SQL
Welcome to Day 105! Today, we'll explore how to work with **JSON (JavaScript Object Notation)** data
Day 106: XML in SQL
Welcome to Day 106! Today, we'll explore how to work with **XML (eXtensible Markup Language)** data
Day 107: SQL Security
Welcome to Day 107! Today, we'll discuss **SQL Security**, a critical topic for protecting your
Day 108: SQL Performance Tuning
Welcome to Day 108! Today, we'll discuss **SQL Performance Tuning**, the process of optimizing your