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Learn how to develop and model business problems quantitatively.
|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.
|This Specialization covers both the dynamics and the global aspects of strategic management.
|You learn how to translate data into models to make forecasts and to support decision making.
|Optimization is a common form of decision making, and is ubiquitous in our society.
|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.
|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.
|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.
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
|Credit Card Fraud
|Looking at a play example for credit card fraud, using publicly available data
|Predicting asset price in time series.
|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.
|A quick look at a budget
|Graphing the budget.
|General Ledger Analysis
|A look at the GL in python
|A-B Test Result
|Initial A-B Results
|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
|KNN technique to segment customers.
|Investigating and employee survey
|A telco company’s churn analysis.
|Constraint programming analysis
|CP Insurance analysis.
|Machine Learning + CP
|Machine Learning + Optimisation.
|Post Office optimisation
|Soda - CP
|Constraint Programming + ML
|Soda - Knapsack
|Knapsack algorithm + ML
|Soda - MLP
|MLP analysis + ML
|Attrition distribution statistics
|Neural Network Attrition
|Attrition using neural networks.
|Expacted Value Model
|Using Expected Value to Evaluate Model Performance
|RFM Marketing Analysis
|Panama Papers 1
|Deep Panama Network
|Panama Papers 2
|Second Panama Analysis
|Game of Thrones
|RFM Marketing Analysis
|Counting Disclosure for companies
|Age analysis over debtors
|Ads LTSM analysis
Freely editable notes for machine learning tasks.
|These notes go over the initial process of importing data and getting it ready for the machine learning model
|This notebook explores the different types of data frame analyses used in the data science process.
|Exploring the different type of graphs and charts. Including plolty and seaborn plots.
|Feature engineering is an especially important technique to improve model performance on tabular data. This notebook explores different feature engineering techniques.
|This notebook identifies a few default models that can be used for fast prediction tasks.
|This notebook focuses on the different measures available to interpret feature or predictor variable importances.
|The time series notebook includes code and theory related to long and short term forecasts.
|This notebook identifies the different deep learning approaches for various tasks.
|This notebook explores the different type of cross-validation and validation techniques.
|All other type of code are aggregated in this notebook.