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FutureLearn

Introduction to Data Science for Educators

University of California, Riverside via FutureLearn

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Overview

Learn how to apply data science to different teaching disciplines

This eight-week course will help you grasp the core concepts of data science. With this knowledge, you’ll be able to understand and teach more specialised topics to your students.

You’ll also gain a deep understanding of the application of data science in STEM disciplines, learn how to teach similar courses, and discover how you can utilise data science in your curriculum.

Delve into database management, data mining, and integration

Raw data is collected by organisations every day. The data goes through multiple key steps before it can be used for research, marketing, and other purposes.

On this course, you’ll learn the tools and processes used to organise and convert data. With this knowledge, you’ll have the skills to manipulate data at a beginner’s level.

Develop an understanding of machine learning and effective data visualisation

Machine learning and data visualisation are essential parts of the data science process which contribute to the analysis and representation of data.

Both processes can be quite complex, making use of high-level tools and operations. The course will help you expand your understanding of these integral processes to ensure you can teach these advanced topics

Learn from the experts at the University of California, Riverside

You’ll be guided by Professor Bahram Mobasher who developed the online Master of Science in Engineering – Data Science at UCR.

Armed with your foundational knowledge and skills in data science, you’ll be able to guide your students in the subject and lead brilliant young innovators into a promising career path.

This course is designed for educators interested in learning introductory data science, as well as administrators developing data science pathways for students.

It is also suitable for educators who have completed computer science and maths credentials.

Syllabus

  • Introduction to Data Science
    • Introduction to Data Science
    • What You Need to Know to Become a Data Scientist
    • Examples of Data Science
  • The Data Science Process
    • Data Science Process: The Beginning
    • Data File Formats
    • Processes Involved in Data Science
  • Introduction to Machine Learning
    • What is Machine Learning?
    • Types and Examples of Machine Learning
  • Statistics & Probability - Introduction of Bayes Probability and K-Nearest Neighbors
    • Probability: An Introduction and Bayes Probability
    • K-Nearest Neighbors (KNN)
  • Statistics & Probability - Introduction of Regression and Gradient Descent
    • Statistics: An Introduction
    • Regression
    • Gradient Descent
    • Logistic Regression
  • Introduction of Data Mining
    • Data Mining: An Introduction
    • Tools in Data Mining
    • Examples of Data Mining
  • Introduction of Big Data
    • Big Data: An Introduction
    • Market for Big Data
    • Efficient Management of Big Data
    • Big Data Tools
  • Visualization in Data Science
    • Understanding Data through Visualization
    • Aesthetics of Visualization
    • Types of Visualization
    • What We've Learned

Taught by

David Dayton

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