Advanced Data Analysis Techniques
Mastering Modelling, Simulation, and Predictive Analytics in Excel
Course Schedule
Prepare Yourself for Advanced Data Analysis Techniques Course
The Advanced Data Analysis Techniques Course empowers professionals to solve complex business challenges using advanced modelling, simulation, optimization, and predictive analytics tools within Microsoft Excel. As organizations increasingly rely on data to guide decisions, professionals must move beyond basic analytics to harness the full potential of modern data modeling and simulation.
This course explores how advanced analytical methods—such as linear programming, Monte Carlo simulation, and Markov models—can be applied to diverse business problems. Participants will learn how to structure data, design optimization models, and simulate real-world systems to predict behavior, minimize costs, and maximize efficiency.
Through guided, hands-on sessions, this course transforms Excel into a powerful analytical environment, enabling participants to shift from intuition-based decisions to data-driven strategies that improve performance, forecasting, and risk management across industries.
Key Learning Outcomes and Objectives?
Upon completing this course, participants will gain the expertise to design, simulate, and analyze data-driven models that enhance organizational decision-making. They will learn to:
- Apply linear programming techniques to optimize production and logistics systems
- Use Excel’s Solver to perform both linear and nonlinear optimization
- Develop and simulate predictive models for risk and performance assessment
- Build and interpret Markov and Monte Carlo models to forecast outcomes
- Perform scenario and what-if analyses for uncertain business conditions
- Integrate genetic algorithms and stochastic optimization for complex problems
- Utilize Excel for simulation, forecasting, and decision modeling
- Transition from descriptive analytics to predictive and prescriptive insights
Course Outline Summary
- Introduction to optimisation and linear programming
- Objective functions, constraints, and feasibility regions
- Solving production and supply chain problems in Excel
- Linear and non-linear optimisation methods
- Genetic algorithms and stochastic search strategies
- Scenario analysis and what-if modeling in Excel
- One-variable and two-variable data tables
- Introduction to Markov models and risk analysis
- Monte Carlo simulations and statistical modeling
- Real-world optimisation and forecasting applications
Accreditation
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