MIT University - MIT Data Science and Big Data Analytics
- Offered byPearson
MIT Data Science and Big Data Analytics at Pearson Overview
Duration | 7 weeks |
Total fee | ₹54,000 |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
MIT Data Science and Big Data Analytics at Pearson Highlights
- Earn a certificate of completion from MITxPro
- 2 case studies for practical learning
- Learning through social interactions and communication
MIT Data Science and Big Data Analytics at Pearson Course details
- For Professionals at any career stage, looking to turn large volumes of data into actionable insights.
- For Past learners' job roles have included: business intelligence analysts, management consultants, technical managers, business managers, data science mangers.
- For Data science enthusiasts and IT professionals.
- For Participants may include: Technical managers, intelligence analysts, Management consultants IT practitioners, Business managers, Data science managers, Data science enthusiasts
- Apply data science techniques to your organization's data management challenges
- Determine the difference between graphical models and network models
- Convert datasets to models through predictive analytics
- Deploy machine learning algorithms to improve business decision making
- Master best practices for experiment design and hypothesis testing
- Identify and avoid common pitfalls in big data analytics
- Discover how to turn big data into even bigger results in this seven-week online course and earn an MIT Certificate on Data Science as well as 1.8 Continuing Education Units (CEUs) upon completion
- To help uncover the true value of your data, MIT Institute for Data, Systems, and Society (IDSS) created the online course Data Science and Big Data Analytics: Making Data-Driven Decisions for data scientist professionals looking to harness data in new and innovative ways
- 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
MIT Data Science and Big Data Analytics at Pearson Curriculum
Making sense of unstructured data
Clustering
Spectral Clustering, Components and Embeddings
Case Studies
Regression and Prediction
Classical Linear & nonlinear regression & extension
Modern Regression with High-Dimensional Data. The use of modern Regression for causal inference
Case Studies
Classification, Hypothesis Testing and Anomaly Detection
Hypothesis Testing and Classification
Deep Learning
Case Studies
Recommendation Systems
Recommendations and ranking
Collaborative filtering
Personalized recommendations
Case Studies
Wrap-up: Parting remarks and challenges
Networks and Graphical Models
Introduction
Networks
Graphical Models
Case Studies
Predictive Modeling for Temporal Data
Introduction
Prediction engineering
Feature engineering
Modeling and evaluating predictive models