MIT xPRO Data Science & Big Data Analytics

Discover how to use data more effectively by incorporating data science with big data analytics.

Learn what a recommendation is and what data it involves.
Learn what is the structure of temporal data and how can we clearly define training inputs and outputs for prediction. Also learn how can we utilize feature engineering techniques to extract meaningful insights from temporal data. Finally, find out effective strategies for evaluating model performance and preparing to deploy it in the real world.
Learn how to use graphical models to estimate and display a network of interactions.
Deep learning: this recent technique has been the driving force behind the rise of artificial intelligence. Professor Ankur Moitra will demystify this method by describing its underpinnings and limitations. 
Learn what are the common descriptive measures of a network, such as centrality, closeness, and betweenness. Also find out what are the standard stochastic models for networks, such as: Erdos-Renyi, preferential attachment, infection models, notions of influence, etc.
This course will cover the basics of anomaly detection and classification: for these tasks there are methods coming from either statistics or machine learning that are built on different principles. As well as the fundamentals of hypothesis testing, which is the formalization of scientific inquiry. This delicate statistical setup obeys a certain set of rules that will be explained and…
Learn the limitations of traditional prediction and the fundamentals of personalized recommendations. Also learn the many variations such as the use of side information, dynamic models or active models to develop even more accurate recommendation systems
Learn the basics of regression for prediction and inferential purposes. Also, gain an understanding of modern linear and non-linear regression for prediction and inferential purposes. Finally, get practical experience of using classical and modern regression methods for prediciton and inferential purposes.
How do we get from raw data to improving the level of performance? The answer is found in this opening course, which introduces us to the tools and techniques developed to make sense of unstructured data and discover hidden patterns.
How do we get from raw data to improving the level of performance? The answer is found in this opening course. This course will introduce us to the tools and techniques developed to make sense of unstructured data and discover hidden patterns.