Business Analytics: Data and Decisions

Course Information

  • the date

07/04/2025

  • Duration

5 days

  • The city

Dubai

  • Cost
  • Type

in-house

Course details

In today's data-driven economy, organizations face vast amounts of information, but not all data leads to meaningful decisions. Many professionals struggle to connect the dots between raw data and actionable insights, resulting in missed opportunities, inefficient operations, and poor strategic choices. To compete and grow in a dynamic market, businesses must develop the capability to turn data into decisions that are backed by sound analytical methods and aligned with strategic goals.

Business Analytics: Data and Decisions is a comprehensive, hands-on training program designed to empower professionals with the skills and tools to analyze data effectively and make smarter business decisions. This 5-day course blends theory and practice in statistics, probability, programming, optimization, and machine learning—all key pillars of business analytics. Participants will learn to extract insights from data, build predictive models, and apply optimization techniques to solve real-world business challenges.

Through a mix of practical exercises, Python programming demonstrations, and applied analytics case studies, this course provides a robust foundation for professionals looking to harness the power of analytics to drive results.

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

  • Understand how data analytics supports smarter, evidence-based decision-making in business contexts.
  • Apply core concepts of probability, statistics, and probability distributions to model business uncertainty.
  • Use Python programming to work with datasets, manage variables, apply conditional logic, and build reusable functions.
  • Conduct descriptive analytics to summarize and interpret data using statistical measures and visual techniques.
  • Apply predictive analytics and machine learning techniques to build models for forecasting, classification, and decision support.
  • Use linear programming and optimization techniques to solve business problems such as resource allocation and budgeting.
  • Develop and interpret real-world analytical solutions for marketing, finance, operations, and strategic planning decisions.

This course is ideal for professionals who want to leverage data for better decision-making, including:

  • Business analysts and data analyst
  • Operations managers and financial analysts
  • Marketing and product professionals
  • Strategy and business development teams
  • IT and data science professionals
  • Project managers and consultants
  • Anyone interested in transitioning into a business analytics role

Course outline

Maths and Statistics Primer

  • Understanding the importance of data analytics in a business context
  • The role of data in drawing business decisions
  • Introduction to probability theory and its models
  • Conditional probability and Bayes' Theorem applications
  • Explaining the law of total probability
  • Introduction to probability and binomial distributions
  • Describing the central limit theorem and its implications
  • Techniques for manipulating normal distributions

Python Primer

  • Overview of operating systems and their role
  • Methods of working with variables in Python
  • Process of creating, managing, and using lists in Python
  • Ways of working with numerical lists and tuples
  • Exploring dictionaries and their use in Python
  • Defining Boolean variables and their applications
  • Tools for efficient usage of conditional statements and variables in Python
  • Importance of functions in Python programming
  • Demonstration of Python coding and techniques for code manipulation

Descriptive Analytics

  • Understanding data and its nature
  • The role of data in informed decision-making
  • Methods for estimating statistics of a data set
  • Techniques for Maximum Likelihood Estimation
  • Identifying and measuring the correlation between variables
  • Analyzing outliers in data sets
  • Describing linear regression for predictive modeling
  • Case study: Applying descriptive analytics to real-world scenarios

Predictive Analytics

  • Understanding the machine learning workflow
  • Basics of supervised learning techniques
  • Differences between forecasting and inference
  • Applications of nearest neighbors in problem classification
  • Steps in applying regression trees to predict business outcomes
  • Methodology of classifying data with support vector machines
  • Defining data cluster similarities
  • Exercise: Real-world applications of machine learning
  • Machine learning in the real world.

Foundations of Linear Programming

  • Main optimization challenges and their definition
  • Efficient strategies to address production planning issues
  • Describing capital budgeting and investment decisions
  • Defining and recognizing constraints in capital budgeting
  • Methods of finding the optimal solution to problems
  • Using Excel to solve optimization problems
  • Stages in modeling business scenarios as linear programs
  • Explaining optimization models for decision-making
  • Practical tips for enhancing business decisions
  • Case study: Linear programming in real-world scenarios

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