• Understand the principles and benefits of data science for business.
• Identify and formulate relevant business questions that can be answered by data.
• Use R and ggplot2 to create effective and attractive data visualizations.
• Use descriptive statistics, probability distributions, hypothesis testing, and regression analysis to summarize and explore data.
• Use machine learning techniques, such as classification, clustering, and recommendation systems, to discover patterns and trends in data.
• 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 professionals who want to learn how to use data science for business. It is suitable for professionals from any industry, function, or region who are interested in or responsible for data analysis and decision making in their organization or personally. No prior technical or mathematical background is required, but participants should have basic computer literacy and familiarity with common business software such as Excel.

Day One:

Introduction to Data Science for Business

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

Day Two:

Data Visualization with R and ggplot2

• What is R and ggplot2 and how to use them for data visualization?
• Grammar of graphics and aesthetics
• Geometric objects, scales, facets, coordinates, themes, etc.
• Plotting univariate, bivariate, multivariate, categorical, numerical, temporal, spatial, etc. data
• Customizing and saving plots

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:

Machine Learning with R

• What is machine learning and how to use it for data analysis?
• Machine learning basics: supervised, unsupervised, and reinforcement learning
• Classification techniques: logistic regression, k-nearest neighbors, decision trees, random forests, support vector machines, etc.
• Clustering techniques: k-means, hierarchical clustering, density-based clustering, etc.
• Recommendation systems: collaborative filtering, content-based filtering, hybrid filtering, etc.

Day Five:

Data Storytelling with R

• 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.