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 3-4 hours per week for four weeks, including lecture, further reading, hands-on 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 self-paced, 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.
Curriculum
- 8 Sections
- 69 Lessons
- 4 Weeks
- Before You StartIntroduction4
- Module 1: Introduction to Data Science12
- 3.1Principles of Data Science – Data Analytic Thinking
- 3.2Principles of Data Science – The Data Science Process
- 3.3Further Reading
- 3.4Data Science Technologies – Introduction to Data Science Technologies
- 3.5Data Science Technologies – An Overview of Data Science Technologies
- 3.6Data Science Technologies – Azure Machine Learning Learning Studio
- 3.7Data Science Technologies – Using Code in Azure ML
- 3.8Data Science Technologies – Jupyter Notebooks
- 3.9Data Science Technologies – Creating a Machine Learning Model
- 3.10Data Science Technologies – Further Reading
- 3.11Lab Instructions
- 3.12Lab Verification
- Module 2: Probability & Statistics for Data Science21
- 4.1Probability and Random Variables – Overview of Probability and Random Variables
- 4.2Probability and Random Variables – Introduction to Probability
- 4.3Probability and Random Variables – Discrete Random Variables
- 4.4Probability and Random Variables – Discrete Probability Distributions
- 4.5Probability and Random Variables – Binomial Distribution Examples
- 4.6Probability and Random Variables – Poisson Distributions
- 4.7Probability and Random Variables – Continuous Probability Distributions
- 4.8Probability and Random Variables – Cumulative Distribution Functions
- 4.9Probability and Random Variables – Central Limit Theorem
- 4.10Probability & Random Variables – Further Reading
- 4.11Introduction to Statistics – Overview of Statistics
- 4.12Introduction to Statistics – Descriptive Statistics
- 4.13Introduction to Statistics – Summary Statistics
- 4.14Introduction to Statistics – Demo: Viewing Summary Statistics
- 4.15Introduction to Statistics – Z-Scores
- 4.16Introduction to Statistics – Correlation
- 4.17Introduction to Statistics – Demo: Viewing Correlation
- 4.18Introduction to Statistics – Simpson’s Paradox
- 4.19Introduction to Statistics – Further Reading
- 4.20Introduction to Statistics – Lab Instructions
- 4.21Introduction to Statistics – Lab Verification
- Module 3: Simulation & Hypothesis Testing16
- 5.1Simulation – Introduction to Simulation
- 5.2Simulation – Start
- 5.3Lab
- 5.4Simulation – Demo: Performing a Simulation
- 5.5Simulation – Further Reading
- 5.6Hypothesis Testing – Overview
- 5.7Hypothesis Testing – Introduction
- 5.8Hypothesis Testing – Z-Tests, T-Tests, and Other Tests
- 5.9Hypothesis Testing – Test Examples
- 5.10Hypothesis Testing – Type 1 and Type 2 Errors
- 5.11Hypothesis Testing – Confidence Intervals
- 5.12Hypothesis Testing – Demo with R & Python
- 5.13Hypothesis Testing – Misconceptions
- 5.14Hypothesis Testing – Further Reading
- 5.15Hypothesis Testing – Lab Instructions
- 5.16Hypothesis Testing – Lab Verification
- Module 4: Exploring & Visualizing Data4
- Module 5: Data Cleansing & Manipulation4
- Module 6: Introduction to Machine Learning4
- Final Exam & Survey4
