Estimated reading time: 7 minutes

Please see the GitHub repository for more up to date material.

Course labs

Learn how to develop and model business problems quantitatively.

Topic Description
Financial Modelling Wharton’s Business and Financial Modelling Specialization is designed to help you make informed business and financial decisions.
Data Structure Algorithms This specialization is a mix of theory and practice: you will learn algorithmic techniques for solving various computational problem.
Business Strategy This Specialization covers both the dynamics and the global aspects of strategic management.
Econometrics You learn how to translate data into models to make forecasts and to support decision making.
Advanced Modelling Optimization is a common form of decision making, and is ubiquitous in our society.
Business Analytics This Specialization provides an introduction to big data analytics for all business professionals, including those with no prior analytics experience.
Complexity and Uncertainty This course will teach you the first principles of complexity, uncertainty and how to make decisions in a complex world.
Digital Manufacturing Commons The Digital Manufacturing Commons (DMC) is an open, online space for companies of all sizes to collaborate and transform how they design and manufacture their products.
Managerial Economics The capstone project involves an in-depth analysis of an actual business situation in which you will examine the global economic environment of a business.
Managerial Economics Business In order to effectively manage and operate a business, managers and leaders need to understand the market characteristics and economic environment they operate in.
Accounting Decision Making In order for a manager to effectively perform their role they must have an understanding of accounting information, as accounting systems generate information that is used by both internal and external stakeholders.
Computer Science for Business Individuals This is CS50’s introduction to computer science for business professionals, designed for managers, product managers, founders, and decision-makers more generally.
Business Model Metrics and Advanced Tools Learn advanced business model tools and metrics to help you achieve an agile business model.
Marketing Analytics Learn how to use price and promotion analytics to effectively allocate your marketing budget to maximize profits.
Financial Decision Making Learn how corporate leaders make effective decisions to maximize profitability and achieve strategic organizational goals.
Process Mining First Learn how to use the free, open source process mining framework (ProM) to analyse, visualise, and improve processes based on data.
Process Mining Second Process mining is the missing link between model-based process analysis and data-oriented analysis techniques.


Exploratory Notebooks

The following analytical notebooks take a deeper dive into consequential business areas. These notebooks are rough and experimental in nature, but a lot can be learned from them.


Playground Prediction Analysis

Sample Description
Credit Card Fraud Looking at a play example for credit card fraud, using publicly available data
Financial Prediction Predicting asset price in time series.
Insurance Model Identify the steps involved in an insurance prediction model.
Red Hat Customer Value Learning on historically valuable customers to predict the current customer value.
Textual Stock Prediction Using news articles to predict stock price changes.



Sample Description
Budget Analysis A quick look at a budget
Bullet Graph Graphing the budget.
General Ledger Analysis A look at the GL in python


Causal Inference

Sample Description
A-B Test Result Initial A-B Results
Causal Regression Regression technique for causal estimate.
Frequentist vs Bayesian A-B Test Comparison between frequentist and bayesian A-B testing
A-B Test Power Analysis Sample size estimation to match testing power.
Variance Reduction A-B test Techniques to reduce variance in A-B tests



Sample Description
Aggregator Aggregating Frames
Customer Segmentation KNN technique to segment customers.
Customer Survey Investigating and employee survey
Customer Churn A telco company’s churn analysis.


Decision Optimisation

Sample Description
Covering Set Constraint programming analysis
Insurance CP Insurance analysis.
Machine Learning + CP Machine Learning + Optimisation.
Post Office Post Office optimisation
Soda - CP Constraint Programming + ML
Soda - Knapsack Knapsack algorithm + ML
Soda - MLP MLP analysis + ML



Sample Description
Attrition Stats Attrition distribution statistics
Cost Attrition Attrition analysis.
Neural Network Attrition Attrition using neural networks.
Termination Analysis Analysing Termination


Game Theory

Sample Description
Single Game Single GT
Tournament Tournament GT



Sample Description
Bikeshare Bikeshare Analysis
Backorder Backorder Prediction
Expacted Value Model Using Expected Value to Evaluate Model Performance



Sample Description
RFM RFM Marketing Analysis



Sample Description
Panama Papers 1 Deep Panama Network
Panama Papers 2 Second Panama Analysis
Game of Thrones RFM Marketing Analysis



Sample Description
Disclosure Counts Counting Disclosure for companies



Sample Description
Aged Debtors Age analysis over debtors
Amortization Schedule Amortisation Analysis



Sample Description
Commission Commission Calculation
Sales Performance Performance analysis
Sales Waterfall Waterfall Analysis


Time Series

Sample Description
LTSM RNN Ads LTSM analysis


Community Notes

Freely editable notes for machine learning tasks.

Topic Description
Data Processing These notes go over the initial process of importing data and getting it ready for the machine learning model
Table Exploration This notebook explores the different types of data frame analyses used in the data science process.
Visual Exploration Exploring the different type of graphs and charts. Including plolty and seaborn plots.
Feature Engineering Feature engineering is an especially important technique to improve model performance on tabular data.  This notebook explores different feature engineering techniques.
Model Building This notebook identifies a few default models that can be used for fast prediction tasks.
Feature Importance This notebook focuses on the different measures available to interpret feature or predictor variable importances.
Time Series The time series notebook includes code and theory related to long and short term forecasts.
Deep Learning This notebook identifies the different deep learning approaches for various tasks.
Cross Validation This notebook explores the different type of cross-validation and validation techniques.
Other All other type of code are aggregated in this notebook.