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Data Science Podcasts


Data Skeptic: Your trusted podcast, centered on data science, machine learning, and artificial intelligence. Tune in for the latest interviews with leading experts.

Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Neil Lawrence, bring you clear conversations with experts in the field, discussions of industry news, and useful answers to your questions.

O’Reilly Data Show Podcast explores the opportunities and techniques driving big data, data science, and AI.

Not So Standard Deviations: The Data Science Podcast Roger Peng and Hilary Parker talk about the latest in data science and data analysis in academia and industry.

CS 641: Foundations of Data Science

Course Introduction: Welcome to Foundations of Data Science! Data science provides the insights necessary to make all manner of personal and professional decisions. For example, the global populace has been inundated with data from charts and graphs depicting the spread of the COVID-19 pandemic. You may have used this data to decide if you should work from home, send your child to school, or engage in social activities. Even when the world is operating outside of such a global crisis, there are countless other concepts, ideas, and decisions data science informs; consider the volatility of the stock market or the collapse of the housing market. Every corner of our reality benefits from information generated by data science because relying on data to make sound decisions is more effective than relying on raw emotion or gut instinct. Decision-makers in every type of organization from governments to non-profits, to Fortune 500 companies, rely on data scientists to solve problems, uncover solutions, and identify trends.

Relevant Library Search Results

The following links will take you to the library holdings for each topic. Once the page opens, you can narrow the results by utilizing the filters on the left side of the page, including "peer-reviewed," "full text online," and "publication date."

Required Texts

There is NO required text for this course. 

The APA Manual is recommended, see link below.

Required Software:

JMP Pro 15 is a statistical analysis and graphical tools software package available, for both
Windows and Mac, through the Kettering University Cloud and is necessary for completing several
assignments. JMP Pro 15 allows you to save files on your hard drive. For relevant assignments,
please upload the work you do in JMP Pro 15 as appendices to your paper.

To access JMP Pro 15:
1. Access the main Kettering University webpage:
2. Sign into “My Kettering”
3. Choose “Tools and Applications”
4. Choose “Tools and Applications Index”
1. Choose “KU Cloud”
5. Enter your Kettering University User Name and Password
2. Choose “Default”
3. JMP Pro 15 appears on the desktop

Once you click on JMP Pro 15, you will be prompted to register your software. You do not have to register this software, instead choose “Wait to Register”

Note – Please spend some time reviewing the JMP Pro 15 New User Welcome Kit, which includes information about foundational JMP principles and tools, short videos, practice activities, and best practices. Remember to save often when working in this, or any program in the KU Cloud. For more information about using the KU Cloud please see the FAQ by clicking on the “Learn more” link in your “My Kettering account.”

Contact The Library


  • Librarians and specialists are available in-person at our knowledge bar service desks on the third floor of the Learning Commons. Please visit this page for hours


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  • To chat KU Librarians, please see browser sidebar on the library website, libguides or searcheverything  


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  • KU Library available for texting services at 810-255-9009
  • KU Archives available for texting services at 810-255-0022


  • For general inquiries and research assistance, please call the Librarians at 810-762-9598
  • For information regarding materials (books/articles), your library account or reserves, please call Access Staff at 810-762-7814 
  • For historical questions, please call the Archivist at 810-762-9690

LinkedIn Learning: Data Science Resources

Introduction to Data Science 

Data Science Foundations: Fundamentals 

Learning Data Science: The Basics 

For a full list of courses and learning pathways related to data science, please click here. 

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