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Self Paced

Data Science Essentials (edX)

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Explore data visualization and exploration concepts with experts from MIT and Microsoft, and get an introduction to machine learning. Demand for data science talent is exploding. Develop your career as a data scientist, as you explore essential skills and principles with experts from MIT and Microsoft. In this data science course, you will learn key concepts in data acquisition, preparation, exploration, and visualization. Plus, look at examples of how to build a cloud data science solution using Azure Machine Learning, R, and Python.

What you'll learn:

- Explore the data science process

- Probability and statistics in data science

- Data exploration and visualization

- Data ingestion, cleansing, and transformation

- Introduction to machine learning




Course Syllabus


Skip Syllabus Description Explore the data science process – An Introduction

• Understand data science thinking

• Know the data science process

• Use AML to create and publish a first machine learning experiment

• Lab: Creating your first model in Azure Machine Learning


Probability and statistics in data science

• Understand and apply confidence intervals and hypothesis testing

• Understand the meaning and application of correlation Know how to apply simulation

• Lab: Working with probability and statistics

• Lab: Simulation and hypothesis testing


Working with data – Ingestion and preparation

• Know the basics of data ingestion and selection

• Understand the importance and process for data cleaning, integration and transformation

• Lab: Data ingestion and selection - new

• Lab: Data munging with Azure Machine Learning, R, and Python on Azure stack


Data Exploration and Visualization

• Know how to create and interpret basic plot types

• Understand the process of exploring datasets

• Lab: Exploring data with visualization with Azure Machine Learning, R and Python


Introduction to Supervised Machine Learning

• Understand the basic concepts of supervised learning

• Understand the basic concepts of unsupervised learning

• Create simple machine learning models in AML

• Lab: Classification of people by income

• Lab: Auto price prediction with regression

• Lab: K-means clustering with Azure Machine Learning