MIT xPRO Data Science & Big Data Analytics

  • 10 Courses | 10h 50m 43s
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Discover how to use data more effectively by incorporating data science with big data analytics.

GETTING STARTED

Clustering

  • Playable
    1. 
    What Is Unsupervised Learning and Its Challenges?
    7m 8s
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    2. 
    Tools And Applications: How They Fit Together
    5m 55s
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COURSES INCLUDED

Clustering
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.
1h 11m has Assessment
Dimensionality Reduction & Spectral Techniques
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.
40m has Assessment
Regression and Prediction
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.
1h 1m has Assessment
Hypothesis Testing and Classification
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 put in context with classification.
1h 50m has Assessment
Deep Learning
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.
31m has Assessment
Recommendations and Ranking
Learn what a recommendation is and what data it involves.
1h 10m has Assessment
Collaborative Filtering & Personalized Recommendations
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
1h 19m has Assessment
Networks
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.
1h 8m has Assessment
Graphic Models
Learn how to use graphical models to estimate and display a network of interactions.
1h 13m has Assessment
Predictive Modeling For Temporal Data
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.
44m has Assessment
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