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Data Science

Department of Management Information Systems, Temple University

Data Science

MIS 0855.750 ■ Spring 2024 ■ Lawrence Dignan
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    • Project 2
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Course materials: Readings

Most of the course materials (slide decks, session recordings, exercises) are linked from the schedule.

However, weekly readings are not linked to the schedule and can be found below. If the link takes you to the Temple library, search for the article title.

  • Most of the course materials (slide decks, exercises, etc.) are linked from the schedule.

    However, weekly readings are not linked to the schedule and can be found below. If the link takes you to the Temple library, search for the article title.

    Module 1 and 2

    Week 2

    • Dhar – 2013 – Data science and prediction
    • Rock – 2018 – A hypothesis can’t be right unless it can be proven wrong 
    • Wikipedia – Survivorship bias
    • Ziemann et al. – 2016 – Gene name errors are widespread in the scientific literature

    Week 3

    • Ball – 2018 – ‘News’ spreads faster and more widely when it’s false
    • Fenkel – 2021 – The Most Influential Spreader of Coronavirus Misinformation Online
    • Hayes – 2013 – In Data We Trust
    • Kaplan et al. – 2021 – Is Confirmation Bias Guiding COVID Vaccine Recommendations?
    • Open Data Handbook – Why Open Data?

    Week 4

    • Unwin – 2008 – Handbook of Data Visualization chapter

    Week 5

    • Davenport – 2013 – Telling a story with data
    • Ritchie – What Is an Infographic?

    Module 3

    Week 7

    • Moss – 2021 – Five Times Excel Led to Disaster
    • Redman – 2013 – Data’s Credibility Problem
    • Rosenblum and Dorsey – 2014 – Knowing Just Enough about Relational Databases
    • Tableau – Guide to data cleaning

    Week 8

    • IBM – 2020 – What is ETL (Extract, Transform, Load)?

    Week 9

    • Aaltonen – 2013 – The beauty and perils of metrics
    • Schambra – 2013 – The Tyranny of Success

    Module 4

    Week 11

    • CFI – Pivot Table Guide
    • Durcevic – 2020 – Move Beyond Excel, PowerPoint & Static Business Reporting with Powerful Interactive Dashboards

    Week 12

    • Feldman – 2013 – Techniques and applications for sentiment analysis
    • Grubbs et al. – 2020 – Understanding Political Twitter
    • Hanna et al. – 2021 – What is unstructured data?

    Week 13

    • Bertolucci 2013 – Big Data Analytics
    • Model Training with Machine Learning – Data Science Primer
    • UNSW – 2020 – Descriptive, Predictive, Prescriptive Analytics
    • Vaughn-Nichols: Here’s how the data we feed AI …
    • Trotta: Generative AI for beginners

Primary Sidebar

RECENT ANNOUNCEMENTS

Study Guide for Exam 3 (Final Exam)

Here is the study guide for the third (final) exam. … [More...] about Study Guide for Exam 3 (Final Exam)

What you need to know about Week 13

Read the following Model Training with Machine Learning – Data … [More...] about What you need to know about Week 13

What you need to know for Week 12

Read the following: Feldman – 2013 – Techniques and applications for … [More...] about What you need to know for Week 12

Assignment 3: Final (Group) Project due April 26, presentations April 29

This is the team project. Here are the assignment instructions and grading … [More...] about Assignment 3: Final (Group) Project due April 26, presentations April 29

What you need to know about Week 11

Read the following.  CFI – Pivot Table Guide Durcevic – 2020 – Move … [More...] about What you need to know about Week 11

Study guide for Exam 2

Here is the study guide for the second exam, which will be held on Canvas … [More...] about Study guide for Exam 2

[More Announcements...]

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