Foundation Level: Data Analytics [BEC_DA_FO_NYSC] – APR 5 – JUN 1

Course Summary

This course is designed to bridge the gap between data analytics and data science, introducing learners to programming, data manipulation, and foundational machine learning techniques. Over 8 weeks, participants will deepen their understanding of how to handle larger datasets, perform exploratory data analysis, and begin using predictive analytics.

Classes run at weekends 3 pm – 6 pm each day.

Course Outline

• Week 1: Introduction to Programming for Data
  • Overview of Python/SQL for data analysis
  • Basic programming concepts (variables, loops, functions)
  • Working with libraries (Pandas, Numpy)
• Week 2-3: Exploratory Data Analysis (EDA)
  • Understanding data distribution
  • Identifying patterns and relationships
  • EDA techniques and tools
• Week 4: Data Manipulation
  • Data transformation and cleaning using Python
  • Aggregating and summarising data
  • Dealing with time series data
• Week 5-6: Introduction to Machine Learning
  • Basics of supervised and unsupervised learning
  • Building simple models (linear regression, classification)
  • Evaluating model performance
• Week 7: Data Visualisation and Reporting
  • Advanced visualisation techniques
  • Interactive dashboards (Power BI/Tableau)
  • Automating reports
• Week 8: Capstone Project
  • Applying data analysis techniques to a real-world problem
  • Developing a presentation based on findings and insights

Learning Objectives

• Develop programming skills essential for data science
• Perform exploratory data analysis to uncover insights
• Build and evaluate basic machine learning models
• Create interactive visualisations and reports

Who Should Attend

• Participants with basic knowledge of data analytics
• Professionals wanting to move into data science
• Analysts looking to learn more about data manipulation and predictive modelling