Executive Development Programme in Data-Driven Decision Making in Analytics

This course is designed for business executives and leaders seeking to enhance their strategic decision-making capabilities through data analytics. Participants will gain skills in interpreting complex data, understanding analytics tools and techniques, and integrating data insights into corporate strategy.

Upon completion, attendees will be equipped to lead data-driven initiatives, make informed decisions, and drive organizational growth by leveraging data analytics. The program covers key areas such as data literacy, predictive analytics, and big data management, ensuring participants can navigate the data landscape effectively.

1. 1. Fundamentals of Data-Driven Decision Making: Learners will understand the importance of data-driven decision making and explore basic principles such as data collection, cleaning, and basic statistical analysis. They will gain practical skills in using tools like Excel or Google Sheets for data manipulation and basic data visualization.
2. 2. Introduction to Analytics: Learners will delve into the basics of business analytics, including types of data (structured and unstructured), key performance indicators (KPIs), and basic analytical methods. They will practice using analytics software to analyze small datasets and interpret results.
3. 3. Data Visualization Techniques: This module focuses on creating effective visual representations of data to support decision making. Learners will study various visualization tools and techniques, and practice creating compelling charts, graphs, and dashboards using tools such as Tableau or Power BI.
4. 4. Advanced Statistical Analysis: Building on foundational statistics, learners will explore more advanced statistical methods including regression analysis, hypothesis testing, and time series analysis. Practical skills in R or Python will be developed to perform these analyses.
5. 5. Data Mining and Machine Learning: This module introduces learners to data mining techniques and machine learning algorithms. They will learn how to apply these techniques to analyze large datasets and build predictive models using tools like Python’s Scikit-learn library.
6. 6. Big Data Technologies: Learners will gain an understanding of big data technologies and platforms such as Hadoop and Spark. They will practice working with large datasets and processing them using these technologies.
7. 7. Decision Support Systems: This module covers the design and implementation of decision support systems. Learners will learn how to integrate data, analytics, and user interfaces to create effective decision support tools.
8. 8. Leadership and Communication in Data-Driven Decision Making: Focusing on the human aspects of data-driven decision making, this module teaches learners how to effectively communicate analytical findings to non-technical stakeholders and lead data-related projects.
9. 9. Ethics and Privacy in Data Analytics: Learners will explore ethical considerations and privacy issues in data analytics. They will learn about regulatory frameworks and best practices for handling sensitive data.
10. 10. Case Studies and Real-World Applications: This final module involves applying all learned skills in real-world scenarios through case studies. Learners will work in teams to analyze data, develop recommendations, and present their findings to a simulated executive board.