This course provides a comprehensive introduction to the fundamental concepts, methods, and tools used in modern data analytics. It equips students with the knowledge and practical skills required to collect, clean, integrate, analyze, visualize, and interpret data for evidence-based decision-making.
The course covers the entire analytics value chain, including data acquisition from multiple sources, data preprocessing and transformation, foundational statistical analysis, exploratory data analysis, and introductory machine learning techniques such as classification and clustering. Students will also learn how to communicate analytical findings effectively through data visualization using Python libraries and Tableau dashboards.
Emphasis is placed on hands-on learning using Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and other industry-relevant tools. In addition, the course introduces students to big data concepts, NoSQL systems, graph analytics, and the ethical and privacy considerations associated with data-driven technologies.
By the end of the course, students will be able to transform raw data into meaningful insights, build basic predictive models, develop interactive visualizations, and apply analytical thinking to real-world business and societal problems. The course serves as a foundation for advanced studies in data science, artificial intelligence, business analytics, and related computing disciplines.
- Teacher: EMMANUEL BUGINGO