• Understand the principles and benefits of data analysis for decision making
• Identify and formulate relevant business questions that can be answered by data
• Use Excel and Power BI to create effective and attractive data visualizations
• Use R and Python to perform advanced data analysis techniques, such as descriptive statistics, probability distributions, hypothesis testing, regression analysis, and machine learning
• Use data storytelling techniques to communicate and persuade your audience with data
• Avoid common mistakes and pitfalls in data analysis and visualization

This course is designed for leaders who want to learn how to use data analysis for their organizations. It is suitable for leaders from any industry, function, or region who are interested in or responsible for data analysis and decision making in their organization or personally. Some prior experience with Excel is required, but no prior technical or mathematical background is necessary.

Day One:

Introduction to Data Analysis for Leaders

• What is data analysis and why does it matter for decision making?
• Data analysis process and pipeline
• Data types, sources, and quality
• Data analysis tools and platforms
• Data analysis applications and use cases in various business domains

Day Two:

Data Visualization with Excel and Power BI

• What are Excel and Power BI and how to use them for data visualization?
• Data visualization principles and practices
• Data visualization techniques: charts, graphs, tables, maps, dashboards, etc.
• Plotting univariate, bivariate, multivariate, categorical, numerical, temporal, spatial, etc. data
• Customizing and saving visualizations

Day Three:

Data Analysis with R

• What is R and how to use it for data analysis?
• Data structures and manipulation
• Descriptive statistics and probability distributions
• Hypothesis testing and statistical inference
• Linear regression and correlation analysis

Day Four:

Data Analysis with Python

• What is Python and how to use it for data analysis?
• Data structures and manipulation
• Descriptive statistics and probability distributions
• Hypothesis testing and statistical inference
• Machine learning techniques: classification, clustering, and recommendation systems

Day Five:

Data Storytelling with Data

• What is data storytelling and why is it important for communicating data-driven insights?
• Data storytelling principles and practices
• Data storytelling tools and platforms
• Data storytelling techniques: narrative, context, visuals, interactivity, etc.
• Data storytelling examples and best practices

To enhance learning and practical application of concepts, the training course will use a combination of interactive lectures, case studies, group discussions, practical exercises, and real-world examples. Participants will also get the chance to collaborate on group projects and create action plans adapted to the needs of their respective organizations.