Executive Development Programme in Data Science with R Programming Basics

This course is designed for senior executives and mid-level managers seeking to enhance their strategic decision-making capabilities through data-driven insights. Participants will gain foundational knowledge in data science and R programming, including data manipulation, visualization, and basic statistical analysis, enabling them to interpret complex data and leverage it for business advantage.

Upon completion, attendees will be able to communicate effectively with data scientists, understand key analytical outputs, and develop data-informed strategies to drive organizational success.

1. 1. Introduction to Data Science: Learners will understand the fundamental concepts of data science and the role of R programming in data analysis. They will gain foundational skills in using R for basic data manipulation and visualization.
2. 2. Data Manipulation with R: Learners will study techniques for importing, cleaning, and transforming data using R packages like dplyr and tidyr. They will develop skills to efficiently manage and preprocess datasets.
3. 3. Statistical Concepts for Data Science: Learners will explore key statistical concepts necessary for data science, including probability distributions, hypothesis testing, and regression analysis. They will apply these concepts to real-world datasets using R.
4. 4. Data Visualization with R: Learners will learn to create effective visualizations using R’s ggplot2 package. They will develop skills in crafting insightful and aesthetically pleasing plots to communicate data insights.
5. 5. Machine Learning Fundamentals: Learners will be introduced to fundamental machine learning concepts and algorithms. They will gain practical skills in implementing and evaluating simple models using R.
6. 6. Advanced Data Manipulation Techniques: Learners will delve into advanced data manipulation techniques, including time series analysis and working with large datasets. They will enhance their R skills for complex data handling.
7. 7. Predictive Modeling with R: Learners will study advanced predictive modeling techniques such as decision trees, random forests, and support vector machines. They will apply these models to predictive analytics projects using R.
8. 8. Deep Learning with R: Learners will be introduced to deep learning concepts and how to implement neural networks using R packages like Keras. They will develop skills in building and training complex models.
9. 9. Project Management in Data Science: Learners will learn project management best practices in the context of data science projects. They will develop skills in planning, executing, and delivering data science projects effectively.
10. 10. Ethical Considerations in Data Science: Learners will explore ethical issues in data science, including data privacy, bias, and fairness. They will gain the skills to ensure their data science projects are conducted ethically and responsibly.