Over the course of eight weeks, you will take your data analytics skills to the next level as you learn the theory and practice behind recommendation engines, regressions, network and graphical modeling, anomaly detection, hypothesis testing, machine learning, and big data analytics.

Through in-depth lectures from renowned faculty from across MIT, you’ll acquire the theories, strategies, and tools you need to answer questions such as:

What is clustering and when should I use it? What is the best way to design experiments and conduct hypothesis testing using my data? How should I do model selection and avoid over-fitting? What are the latest trends in machine learning? How do graphical models and network models differ?

LEARNING OUTCOMES: Apply data science techniques to your organization’s data management challenges. Identify and avoid common pitfalls in big data analytics. Deploy machine learning algorithms to mine your data. Interpret analytical models to make better business decisions. Understand the challenges associated with scaling big data algorithms. Convert datasets to models through predictive analytics.