Data Science Essentials

Course content:

– Explore the data science process

– Probability and statistics in data science

– Working with data – Ingestion and preparation

– Data Exploration and Visualization

– Introduction to Supervised Machine Learning

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 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 Machine Learning, R, and Python

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 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 Machine Learning

 

Book Information: Teacher will provide course material and recommend reference books.

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