Professional Certificate in Data Science and Analytics

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Program Topics

Module 1: Introduction to Data Science

Module 2: Thinking about Risk and Uncertainty through Probability and Distributions

Module 3: Correlation

Module 4: Clustering

Module 5: Linear Regression Part 1

Module 6: Linear Regression Part 2

Module 7: Logistic Regression

Module 8: Collaborative Filtering

Module 9: Optimization Part 1

Module 10: Optimization Part 2

Module 11: Optimization Part 3

Module 12: Optimization Part 4

Module 13: Optimization Part 5

Module 14: Regression and Classification

Module 15: Ensemble Models

Module 16: Fairness and Bias Issues in Data-Driven Predictions

Module 17: Neural Networks Part 1

Module 18: Neural Networks Part 2

Module 19: Neural Networks Part 3

Module 20: Natural Language Processing (NLP) Part 1

Module 21: Natural Language Processing (NLP) Part 2

Module 22: Interpretability and Causality in Models

Module 23: Data, Models, and Decisions

Module 24: Leading Digital Transformations

Features

24 modules
4 faculty subject matter experts
diverse international design collaboration
designed multiple interactive instructional supplements
real-world case studies and current event applications
focus on ethics in data science
final portfolio demonstrating learner success

Program Summary

The Professional Certificate in Data Science and Analytics is a comprehensive program designed to equip learners with the skills and knowledge required for success in the field. As an associate director of design, I played a key role in shaping this transformative project. Collaborating with a diverse international design team, we worked closely with four faculty subject matter experts to develop 24 interactive modules. Drawing upon my skills, I designed multiple instructional supplements that enhanced the learning experience. The program incorporates real-world case studies and current event applications to provide learners with practical insights into data science. A notable feature of the program is its emphasis on ethics in data science, ensuring learners understand the importance of responsible and ethical data practices. The culmination of the program is the final portfolio, where learners demonstrate their mastery of skills and showcase their success in applying data science techniques. Through my role, I facilitated the creation of a dynamic and engaging learning experience, ensuring that learners are well-prepared for a successful career in the ever-evolving field of data science and analytics.

Professional Certificate in Data Science and Analytics

Subject Matter Experts: Vivek Farias, Robert Freund, Retsef Levi, and Rama Ramakrishnan

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