Data Science and Machine Learning Essentials
Cynthia Rudin and Dr. Steve Elston via edX
- 6
- 5 weeks long, 3-4 hours a week
- edX
- English
Course Overview
Demand for Data science talent is exploding. Learn these essentials with experts from MIT and the industry, partnering with Microsoft to help develop your career as a data scientist. By the end of this course, you will know how to build and derive insights from data science and machine learning models. You will learn key concepts in data acquisition, preparation, exploration and visualization along with examples on how to build a cloud data science solution using Azure Machine Learning, R & Python.
Data Science is an essential skill for analyzing and deriving useful insights from data, big and small. McKinsey estimates that by 2018, a 500,000 strong workforce of data scientists will be needed in US alone. The resulting talent gap must be filled by a new generation of data scientists.
This course is organized into 5 weekly modules each concluding with a quiz. By achieving a passing grade in the final course assessment you will receive a certificate demonstrating that you have acquired data science skills and knowledge. Apart from answering your questions on the forum, faculty will host an office hour to address questions you may have while undertaking this course.
Get an ID verified certificate to demonstrate your data science knowledge and share on LinkedIn.
Course Circullum
- Introduction to Data Science
- Overview of the Data Science process
- Introduction to Data Science technologies
- Introduction to Machine Learning
- Regressions
- Classification
- Clustering
- Recommendation
Module 2: Working with Data in Azure ML
- Data Acquisition
- Data Ingestion and Ingress
- Data Sampling and Quantization
- Data Cleaning and Transformation
Module 3: Building and Evaluation of Models
- Data Exploration and Visualization
- Business Metrics and Cost-Based Metrics
- Model Evaluation, Comparison and Selection
Module 4: Models in Azure ML, Part 1
- Regression Models
- Classification Models
- Unsupervised Learning Models
Module 5: Models in Azure ML, Part 2
- Recommendation Models
- Publishing AML Models
- Course Exam
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This Course Include:
- Introduction to Data Science
- Overview of the Data Science process
- Introduction to Data Science technologies
- Introduction to Machine Learning
- Regressions
- Classification
- Clustering
- Recommendation
Module 2: Working with Data in Azure ML
- Data Acquisition
- Data Ingestion and Ingress
- Data Sampling and Quantization
- Data Cleaning and Transformation
Module 3: Building and Evaluation of Models
- Data Exploration and Visualization
- Business Metrics and Cost-Based Metrics
- Model Evaluation, Comparison and Selection
Module 4: Models in Azure ML, Part 1
- Regression Models
- Classification Models
- Unsupervised Learning Models
Module 5: Models in Azure ML, Part 2
- Recommendation Models
- Publishing AML Models
- Course Exam
- Provider:edX
- Certificate:Not Avalible
- Language:English
- Duration:5 weeks long, 3-4 hours a week
- Language CC: