• Understand the principles and benefits of data analysis for project management
• Identify and formulate relevant project 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

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.

Day One:

Introduction to Data Analysis for Project Managers

• What is data analysis and why does it matter for project management?
• Data analysis process and pipeline
• Data types, sources, and quality
• Data analysis tools and platforms
• Data analysis applications and use cases in various project 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 project progress and outcomes?
• Data storytelling principles and practices
• Data storytelling tools and platforms
• Data storytelling techniques: narrative, context, visuals, interactivity, etc.
• Data storytelling examples and best practice

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.