Welcome to DAT203.1x: Data Science Essentials. We’re very excited to have you join us to learn how to build predictive analytics solutions with Azure Machine Learning, and we hope you have an enriching and engaging learning experience.
To get started, open the Course page and view the topics in the Introduction section, which will help you get set up for the practical labs. We welcome your feedback, so if something is confusing or doesn’t work the way you expect, let us know and we’ll do our best to address it. Please use the Discussion page to introduce yourself and let us know how you’re doing.
The course is organized into modules that each contain one or more lessons, which in turn contain topics. The course is designed to be easy to navigate, but if you have not taken a course on edX.org before, we encourage you to check out the edX DemoX course to familiarize yourself with the course interface.
The course will take an estimated 34 hours per week for four weeks, including lecture, further reading, handson labs, and assessments. You have until the course end date listed in the Important Dates section to complete all graded coursework. Note that the course is scheduled to end at 11:59pm UTC, which is a minute before midnight at the end of the day in the UTC (or GMT) timezone. You can find out when this is in your local timezone at http://www.worldtimeserver.com/convert_time_in_UTC.aspx.
The course is selfpaced, so while staff will periodically check the discussion forums, you are expected to work through the materials and submit the graded assignments at your own pace. With so many students taking the course over a long period of time, it will be impossible for staff to answer every question individually; so please use the discussion forums to share questions and comments with your fellow students – the chances are, someone else taking the course will have encountered and resolved the issue you are having!
Module assessments account for 60% of the total grade, and the final assessment accounts for the remaining 40%. You must achieve an overall score of 70% to pass the course.
Again, welcome and thanks for taking this course from Microsoft.

Before You Start
Introduction

Module 1: Introduction to Data Science
 Principles of Data Science – Data Analytic Thinking
 Principles of Data Science – The Data Science Process
 Further Reading
 Data Science Technologies – Introduction to Data Science Technologies
 Data Science Technologies – An Overview of Data Science Technologies
 Data Science Technologies – Azure Machine Learning Learning Studio
 Data Science Technologies – Using Code in Azure ML
 Data Science Technologies – Jupyter Notebooks
 Data Science Technologies – Creating a Machine Learning Model
 Data Science Technologies – Further Reading
 Lab Instructions
 Lab Verification

Module 2: Probability & Statistics for Data Science
 Probability and Random Variables – Overview of Probability and Random Variables
 Probability and Random Variables – Introduction to Probability
 Probability and Random Variables – Discrete Random Variables
 Probability and Random Variables – Discrete Probability Distributions
 Probability and Random Variables – Binomial Distribution Examples
 Probability and Random Variables – Poisson Distributions
 Probability and Random Variables – Continuous Probability Distributions
 Probability and Random Variables – Cumulative Distribution Functions
 Probability and Random Variables – Central Limit Theorem
 Probability & Random Variables – Further Reading
 Introduction to Statistics – Overview of Statistics
 Introduction to Statistics – Descriptive Statistics
 Introduction to Statistics – Summary Statistics
 Introduction to Statistics – Demo: Viewing Summary Statistics
 Introduction to Statistics – ZScores
 Introduction to Statistics – Correlation
 Introduction to Statistics – Demo: Viewing Correlation
 Introduction to Statistics – Simpson’s Paradox
 Introduction to Statistics – Further Reading
 Introduction to Statistics – Lab Instructions
 Introduction to Statistics – Lab Verification

Module 3: Simulation & Hypothesis Testing
 Simulation – Introduction to Simulation
 Simulation – Start
 Lab
 Simulation – Demo: Performing a Simulation
 Simulation – Further Reading
 Hypothesis Testing – Overview
 Hypothesis Testing – Introduction
 Hypothesis Testing – ZTests, TTests, and Other Tests
 Hypothesis Testing – Test Examples
 Hypothesis Testing – Type 1 and Type 2 Errors
 Hypothesis Testing – Confidence Intervals
 Hypothesis Testing – Demo with R & Python
 Hypothesis Testing – Misconceptions
 Hypothesis Testing – Further Reading
 Hypothesis Testing – Lab Instructions
 Hypothesis Testing – Lab Verification

Module 4: Exploring & Visualizing Data

Module 5: Data Cleansing & Manipulation

Module 6: Introduction to Machine Learning

Final Exam & Survey