In today's business landscape, every organization is actively leveraging business analytics to enhance their decision-making processes. Business analytics encompasses various quantitative and statistical analysis techniques, predictive modeling, data mining, and multivariate testing. By dissecting past performance, organizations can formulate strategies for the future.

The Business Analytics: Data and Decisions training course aims to broaden your understanding of business analytics. It provides you with the knowledge and skills to effectively utilize descriptive, predictive, and prescriptive analytics in order to identify, analyze, and solve critical business challenges.

Through this course, you will gain a comprehensive understanding of fundamental methods, frameworks, and techniques in business analytics. You will learn how to derive meaningful insights from your data and use it to make informed business decisions. Additionally, you will have the opportunity to explore practical applications of the analytical frameworks covered in the course.

Course Objectives:

By the end of the Business Analytics: Data and Decisions training course, participants will be able to:

Take you through the fundamentals of the programming language Python to help you expand your understanding of business analytics.
It will teach you how to use descriptive, predictive and prescriptive analytics to identify, analyze and solve critical business problems.
It will help you understand and explore fundamental methods, frameworks and techniques of business analytics to make sense of your data and use it to make informed business decisions.

Who Should Attend?

Business Analytics: Data and Decisions training course, is ideal for:

Technical managers implementing analytics in their function or organization.
Professionals seeking to enter into the field of analytics & data science.
Mid-to-senior functional managers looking to improve their decision-making using data.
Consultants aiming to develop their knowledge of business analytics.

Course Outlines:

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 neighbors 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

Optimization problems.
Production planning problem.
Capital budgeting problem Identifying the constraints.
The optimal solution.
Solving the problem in Excel.
Model business problems as linear programs Integer programming.
Optimization models.
Tricks-of-the-trade for business decisions.
Real-life applications.

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