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Data Science

Department of Management Information Systems, Temple University

Data Science

MIS 0855.750 ■ Fall 2023 ■ Lawrence Dignan
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Course readings, decks

August 28, 2023 Leave a Comment

MODULE 1

Session 1

Decks

  • 1.1 Introduction
  • 1.2 Science and Data Science

Exercise

  • 1.2 Data sources

Session 2

Readings

  • 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

Decks

  • Data and science
  • Intro to datasets

Exercises

  • 2.1 Developing Hypotheses
  • 2.2 Building a Data Dictionary

Session 3

Decks

  • 3.1 Where do we get data?
  • 3.2 Trust in data
  • 4.1 Exploring data visually

Readings

  • 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?

Bonus read

  • We’re Trapped in a Social Filter Bubble (Rousseau)

Exercises

  • 3.1 Finding Open Data sets
  • 3.2 Assessing the trustworthiness of data
  • 4.1 Finding good and bad visualizations

Session 4 (we will use this class for one exercise and first assignment)

Exercise

  • Getting familiar with Tableau
  • Data set for exercise

Readings

  • Unwin – 2008 – Handbook of Data Visualization chapter

Session 5

Decks

  • 5.1 Telling stories with data visualizations
  • 5.2 Infographics

Readings

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

Bonus read:

  • 5 Ways Writers Use Misleading Graphs to Manipulate You (McCready)

Exercises

  • 5.1 Effective Data Visualizations and Tableau workbook for 5.1 and Excel to go with it
  • 5.2 Creating infographics with Piktochart

Session 6

Exam 1

Session 7

Readings

  • 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

Decks

  • 7.1 Corrupting and cleansing data
  • 7,2 Relational database

Exercises

  • 7.1 How to obtain corrupt data
  • 7.2 Locating and correcting suspicious data points using Excel and the data set 

Session 8

Readings

  • 8.1 IBM – 2020 – What is ETL (Extract, Transform, Load)?
  • Latest Trends in Medical Monitoring Devices and Wearable Health Technology (Phaneuf)
  • Wearables Could Transform Insurance from Reactive to Proactive

Decks:

  • 8.1 Integrating data from different sources
  • 8.2 Storing large datasets for analysis

Exercises:

  • 8.1 Setting up an ETL Process to Merge Data, Carrier names.csv, Delta – PHL – Jan-2018.csv, Frontier – PHL – Jan-2018.csv, JetBlue – PHL – Jan-2018.csv, Republic – PHL – Jan-2018.csv, Southwest – PHL – Jan-2018.csv, Hourly weather for PHL in January 2018.csv

Session 9

Readings

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

Decks

  • 9.1 Aggregating data for insights
  • 9.2 Scorecards and dashboards

Exercise

  • 9.1 Identifying a Key Performance Indicator
  • 9.2 Assess different product scorecards

Session 10

Exam 2

Session 11

Readings

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

Decks:

  • 11.1 Interactive dashboards and pivot tables

Exercises:

  • 11.1 Pivot Table Analysis in Excel, Employee monthly expenses.xlsx
  • 11.2 Creating an Interactive Dashboard, Daily delays from PHL by airline.xlsx

Session 12

Readings

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

Decks: 

  • Working with unstructured data

Exercise:

  • 12.1 Manually Determining the Sentiment in Text
  • 12.2 Building a Sentiment Analysis Pipeline, Sentiment Analysis Tools.xlsm

Session 13

Readings

  • 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

Decks

  • Descriptive, Predictive, Prescriptive analytics
  • Machine Learning, GenAI

Exercises

  • 13.1 Simple Predictive Analytics, VandelayOrdersAll.xlsx
  • 13.2 Learn ChatGPT, Train image recognition system, files

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