Business Analytics: Data and Decisions
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Business Analytics: Data and Decisions Course
Introduction:
Today, every organization is trying its best to use business analytics for its decision-making purposes. It includes quantitative and statistical analysis, predictive modeling, data mining, and multivariate testing. It breaks down past performances to draw the plan for the future.
Business Analytics: Data and Decisions training course will help you to 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.
Understand and explore fundamental methods, frameworks, and business analytics techniques to make sense of your data and use it to make informed business decisions. You will also explore the practical applications of the analytical frameworks you are learning.
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 implement 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 the 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.
