Course Details
Course Outline
5 days course
Maths & Statistics Primer
- Introduction to probability theory.
- Basics of probability & statistics Probability models.
- Bayes’ rule and conditional probability.
- Total probability.
- Bayes’ rule application.
- Probability distribution.
- Binomial distribution.
- Central limit theorem.
- Manipulating normal variables.
Python Primer
- Operating systems overview.
- Variables in python.
- Creating and managing lists.
- Numerical lists Tuples.
- Dictionaries in python.
- Boolean variables.
- Conditional variables.
- About functions.
- Python demonstration and code manipulation.
Descriptive Analytics
- What is data?
- Data and decision making.
- Estimate statistics of a data set.
- Maximum likelihood estimation.
- Detection and quantification of correlation.
- Outliers Linear regression.
- Real-life applications.
Predictive Analytics
- Introduction to machine learning.
- Machine learning process.
- Supervised learning Forecasting vs inference.
- Using nearest neighbours for classification problems.
- Predict outcomes in a business context using regression trees.
- Classify data using support vector machines.
- Measure similarity of data clusters.
- Predict outcomes for different clusters.
- Machine learning in the real world.
Foundations of linear programming
- Optimisation problems.
- Production planning problem.
- Capital budgeting problem Identifying the constraints.
- The optimal solution.
- Solving the problem in Excel.
- Model business problems as linear programmes Integer programming.
- Optimisation models.
- Tricks-of-the-trade for business decisions.
- Real-life applications.
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