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Certificate in Applied Data Science & Machine Learning (Module Two)

A one-month intensive course designed for learners who have already established a foundational knowledge in the field, having completed Module One or its equivalent. This course delves into the core components of statistical modeling and machine learning, beginning with a crucial grounding in probability, sampling, and inferential statistics.

What You Will Learn:

Building on this base, the curriculum explores the primary paradigms of machine learning, including supervised learning techniques for both regression and classification tasks, as well as unsupervised learning methods such as clustering and dimensionality reduction. An introduction to time series analysis is also provided to equip students with the skills to handle temporal data. The theoretical concepts are directly applied through a hands-on mini-project, where participants are tasked with building and evaluating a predictive model using a real-world case study from domains like finance, health, or civic data. The outcome of this module is a practical and functional skill set; participants will be proficient in analyzing data, constructing and evaluating machine learning models, and generating predictive insights that can inform strategic decision-making.