Business Analytics: Data and Decisions
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Business Analytics: Data and Decisions Course
Introduction:
In today's business landscape, organizations are actively utilizing business analytics to enhance their decision-making processes. Business analytics encompasses quantitative and statistical analysis, predictive modeling, data mining, and multivariate testing. By dissecting past performance, it enables organizations to formulate plans for the future.
The Business Analytics: Data and Decisions training course aims to enhance your comprehension of business analytics. It will provide instruction on employing descriptive, predictive, and prescriptive analytics to identify, analyze, and resolve critical business problems.
Through this course, you will gain a comprehensive understanding of fundamental methods, frameworks, and techniques in business analytics. This knowledge will enable you to effectively interpret and utilize your data to make well-informed business decisions. Additionally, you will have the opportunity to explore real-world applications of the analytical frameworks taught 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.