README
DAT203.1x: Data Science Essentials
https://github.com/MicrosoftLearning/Data-Science-Essentials
https://github.com/sblack4/Data-Science-Essentials
About
course I completed in late 2016
Contents
Before You Start
Course Introduction
Lab Overview
Setting Up Azure Machine Learning
Installing R or Python
Module 1: Introduction to Data Science
Principles of Data Science
What Is Data Science?
Data Analytic Thinking
The Data Science Process
Further Reading
Data Science Technologies
Introduction to Data Science Technologies
An Overview of Data Science Technology
Azure Machine Learning
Using Code in Azure ML
Jupyter Notebooks
Creating a Machine Learning Model
Further Reading
Lab
Lab
Module 2: Probability and Statistics for Data Science
Probability and Random Variables
Overview of Probability and Random Variables
Introduction to Probability
Discrete Random Variables
Discrete Probability Distributions
Binomial Distribution Examples
Poisson Distributions
Continuous Probability Distributions
Cumulative Distribution Functions
Central Limit Theorem
Further Reading
Introduction to Statistics
Overview of Statistics
Descriptive Statistics
Summary Statistics
Demo: Viewing Summary Statistics
Z-Scores
Correlation
Demo: Viewing Correlation
Simpson's Paradox
Further Reading
Lab
Lab This content is graded
Lab Instructions
Lab Verification
Module 3: Simulation and Hypothesis Testing
Simulation and Confidence Intervals
Introduction to Simulation and Hypothesis Testing
Simulation
Demo: Performing a Simulation
Confidence Intervals
Demo: Confidence Intervals
Further Reading
Hypothesis Testing
Overview of Hypothesis Testing
Introduction to Hypothesis Testing
Z-Tests, T-Tests, and Other Tests
Hypothesis Test Examples
Type 1 and Type 2 Errors
Demo: Hypothesis Testing
Misconceptions About Hypothesis Testing
Further Reading
Lab
Lab This content is graded
Lab Instructions
Lab Verification
Module 4: Exploring and Visualizing Data
Exploring Data
Introduction to Data Exploration
Data and Data Frames
Working with Data in Code
Demo: Getting Started with Data Frames
Working with Data Frames in Azure ML
Demo: Working with Data Frames in Azure ML
Metadata
Demo: Working with Metadata
Further Reading
Visualizing Data
Overview of Data Visualization
Introduction to Data Visualization
Conditioned Plots
Plotting in R or Python
Demo: Plotting in R or Python
Demo: Plotting in Azure ML
Further Reading
Lab
Lab This content is graded
Lab Instructions
Lab Verification
Module 5: Data Cleansing and Manipulation
Data Ingestion and Flow
Overview of Data Ingestion and Flow
Data Flow in Azure ML
Joining Data Sets
Demo: Ingesting and Joining Data
Demo: Joins in R or Python
Further Reading
Data Cleansing
Introduction to Data Cleansing
Overview of Data Cleansing
Missing and Repeated Values
Demo: Handling Missing and Repeated Values
Feature Engineering
Outliers and Errors
Demo: Finding Outliers
Demo: Handling Outliers in Azure ML
Demo: Cleaning Data with R or Python
Introduction to Data Scaling
Demo: Scaling Data in Azure ML
Demo: Scaling Data in R or Python
Further Reading
Lab
Lab This content is graded
Lab Instructions
Lab Verification
Module 6: Introduction to Machine Learning
Getting Started with Machine Learning
Machine Learing Overview
Introduction to Machine Learning - Classification
Evaluating Classifiers
Demo: Creating a Classification Model in Azure ML
Regression
Evaluating Regression Models
Demo: Creating a Regression Model
Clustering
Demo: K-Means Clustering
Further Reading
Publishing a Machine Learning Web Service
Introduction to Azure ML Web Services
Overview of Publishing a Web Service
Demo: Publishing a Web Service
Demo: Consuming a Web Service
Custom Code Considerations
Key Points and Further Reading
Lab
Final Exam and Survey
Course Exam
Post-Course Survey
https://github.com/MicrosoftLearning/Data-Science-Essentials
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