Business Analytics: Data and Decisions

Course Info

Length: 1 Week

Type: In Classroom

Available Dates

Venue

  • Dec-12-2022

    Kuala Lumpur

  • Dec-12-2022

    Singapore

  • Dec-12-2022

    Amsterdam

  • Dec-12-2022

    Zurich

  • Dec-19-2022

    Barcelona

  • Dec-19-2022

    Paris

  • Dec-26-2022

    London

  • Dec-26-2022

    Kuala Lumpur

  • Dec-26-2022

    Dubai

  • Dec-26-2022

    Singapore

  • Dec-26-2022

    Istanbul

  • Dec-26-2022

    Amsterdam

  • Dec-26-2022

    Zurich

  • Dec-26-2022

    Madrid

  • Jan-09-2023

    Dubai

  • Jan-16-2023

    London

  • Jan-16-2023

    Paris

  • Jan-16-2023

    Singapore

  • Jan-23-2023

    Amsterdam

  • Jan-30-2023

    Istanbul

  • Jan-30-2023

    Singapore

  • Jan-30-2023

    Barcelona

  • Feb-06-2023

    Madrid

  • Feb-06-2023

    London

  • Feb-13-2023

    Dubai

  • Feb-13-2023

    Zurich

  • Feb-20-2023

    Istanbul

  • Feb-20-2023

    Kuala Lumpur

  • Mar-06-2023

    Paris

  • Mar-06-2023

    London

  • Mar-06-2023

    Singapore

  • Mar-13-2023

    Dubai

  • Mar-13-2023

    Amsterdam

  • Mar-20-2023

    Singapore

  • Mar-20-2023

    Istanbul

  • Mar-20-2023

    Barcelona

  • Apr-03-2023

    London

  • Apr-03-2023

    Madrid

  • Apr-10-2023

    Dubai

  • Apr-10-2023

    Zurich

  • Apr-17-2023

    Kuala Lumpur

  • Apr-17-2023

    Istanbul

  • May-08-2023

    Paris

  • May-08-2023

    London

  • May-15-2023

    Dubai

  • May-15-2023

    Amsterdam

  • May-22-2023

    Istanbul

  • May-22-2023

    Singapore

  • May-22-2023

    Barcelona

  • June-05-2023

    London

  • June-05-2023

    Madrid

  • June-12-2023

    Zurich

  • June-12-2023

    Dubai

  • June-19-2023

    Istanbul

  • June-19-2023

    Kuala Lumpur

  • July-03-2023

    Paris

  • July-03-2023

    London

  • July-10-2023

    Dubai

  • July-10-2023

    Amsterdam

  • July-17-2023

    Istanbul

  • July-17-2023

    Singapore

  • July-17-2023

    Barcelona

  • Aug-07-2023

    Madrid

  • Aug-07-2023

    London

  • Aug-14-2023

    Zurich

  • Aug-14-2023

    Dubai

  • Aug-21-2023

    Istanbul

  • Aug-21-2023

    Kuala Lumpur

  • Sep-04-2023

    Paris

  • Sep-04-2023

    London

  • Sep-11-2023

    Amsterdam

  • Sep-11-2023

    Dubai

  • Sep-18-2023

    Barcelona

  • Sep-18-2023

    Istanbul

  • Sep-18-2023

    Singapore

  • Oct-09-2023

    Madrid

  • Oct-09-2023

    London

  • Oct-16-2023

    Zurich

  • Oct-16-2023

    Dubai

  • Oct-23-2023

    Kuala Lumpur

  • Oct-23-2023

    Istanbul

  • Nov-06-2023

    Paris

  • Nov-06-2023

    London

  • Nov-13-2023

    Amsterdam

  • Nov-13-2023

    Dubai

  • Nov-20-2023

    Singapore

  • Nov-20-2023

    Barcelona

  • Nov-20-2023

    Istanbul

  • Dec-04-2023

    London

  • Dec-04-2023

    Madrid

  • Dec-11-2023

    Zurich

  • Dec-11-2023

    Dubai

  • Dec-18-2023

    Istanbul

  • Dec-18-2023

    Kuala Lumpur

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.