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Introduction to Data Science for Educators

Master the basics of data science and learn how to use this knowledge to enhance your teaching practices.

Black scientist working with scientific images

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.

Syllabus

  • Week 1

    Introduction to Data Science

    • Introduction to Data Science

      We go over the beginnings of Data Science for Educators, what you need to know to teach data science and examples of the work done in data science.

    • What You Need to Know to Become a Data Scientist

      What you need to know to become a Data Scientist.

    • Examples of Data Science

      We'll be going into specific examples of data science.

  • Week 2

    The Data Science Process

    • Data Science Process: The Beginning

      We'll discuss the process of data science, data types, and categories of data science.

    • Data File Formats

      We'll be diving into the exciting world of data formats used in Data Science.

    • Processes Involved in Data Science

      We dive into the processes involved in Data Science.

  • Week 3

    Introduction to Machine Learning

    • What is Machine Learning?

      We discuss and define what Machine Learning is, an introduction and history of the concept and terminology used in Machine Learning.

    • Types and Examples of Machine Learning

      We go into detail about the types of Machine Learning and give examples of each.

  • Week 4

    Statistics & Probability - Introduction of Bayes Probability and K-Nearest Neighbors

    • Probability: An Introduction and Bayes Probability

      We discuss and define Bayes Probability.

    • K-Nearest Neighbors (KNN)

      An introduction of the K-Nearest Neighbors algorithm and its importance for classification.

  • Week 5

    Statistics & Probability - Introduction of Regression and Gradient Descent

    • Statistics: An Introduction

      We define statistics and it's deeper meaning in Data Science.

    • Regression

      We discuss Regression and what it means in Data Science.

    • Gradient Descent

      We discuss gradient descent in Data science.

    • Logistic Regression

      We discuss and define Logistic Regressoin.

  • Week 6

    Introduction of Data Mining

    • Data Mining: An Introduction

      We define what Data Mining is.

    • Tools in Data Mining

      We discuss various tools used in Data Mining.

    • Examples of Data Mining

      We give real-world examples of Data Mining.

  • Week 7

    Introduction of Big Data

    • Big Data: An Introduction

      What is Big Data? We talk about it.

    • Market for Big Data

      We discuss the markets for Big Data.

    • Efficient Management of Big Data

      We discuss efficient management, algorithms and problems and their solutions of Big Data.

    • Big Data Tools

      We discuss the tools and software used in the study of Big Data.

  • Week 8

    Visualization in Data Science

    • Understanding Data through Visualization

      We discuss visualization in Data.

    • Aesthetics of Visualization

      We discuss the aesthetics of visualization.

    • Types of Visualization

      We discuss the types of visualization used in Data Science.

    • What We've Learned

      We review what we've learned in the course by assessing our obtainment of the learning objectives and consider next steps for incorporating data science in the high school classroom.

When would you like to start?

Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.

  • Available now

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Explore the rapidly growing field of data science and what it means to work as a data scientist.
  • Investigate the core concepts and methods of Machine Learning, Data Mining, Big Data.
  • Develop knowledge of Probability and Statistics - classification, regression, Bayesian methods and utilization of the loss function.
  • Identify data management methods and programming languages used in data science.
  • Experiment with effective visualization of data.

Who is the course for?

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.

Who will you learn with?

I am an Academic Director of Professional Programs at the University of California, Riverside Extension. I have a background in education and instructional design and will be facilitating UCR courses.

Who developed the course?

University of California, Riverside

Established in 1954, UCR University Extension is the continuing and professional development division of the University of California, Riverside (UCR), and is an internationally-recognized leader in education for individuals, organizations, and communities–regionally, nationally, and internationally.

  • Established

    1954
  • Location

    UCR University Extension 1299 University Ave. Suite 201 Riverside, CA 92507 Tel: 951.827.4105
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