Instructor

Here is the study guide for the first exam.

In addition, Sajin will hold an exam review session. Look for email separately.

1. Read

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

2. Take quiz

3. Do assignment 1 due to me 3 pm Sept. 29. Also note that the data set is different than the in-class exercise we did. Assignment 1: Tableau  (Covid cases spreadsheet)  

4. Check out some tools and how-tos on visualization. There are several ‘how to’ articles on how to tell a story with data, just google ‘telling stories with data’:

https://www.google.com/search?q=telling+stories+with+data

They all tell pretty much the same story, so you can choose the one that works for you. 

Differences between infographics and data visualizations are discussed in these two posts:

https://www.statsilk.com/blog/real-difference-between-infographics-and-data-visualizations

https://killervisualstrategies.com/blog/difference-between-infographics-data-visualization.html

We use Piktochart to create an infographic on MIS0855, but there a numerous other web-based tools available:

https://www.creativebloq.com/infographic/tools-2131971

5. Remember that week 6 is exam week and we’ll meet on Canvas with exam available at 5:30 pm. We’ll have a review in class and Sajin will have one separately. Here’s the study guide of what to know.

 

Read

• Unwin – 2008 – Handbook of Data Visualization chapter

2. Take quiz

3. Reminder Tableau extra credit due to me Sept 25. This is the week we’ll be working with it.

4. Read the materials and look over in class and assignment 1, which is due to my inbox 3 p.m. Friday Sept. 29.

Bonus:

Look at how to use and not to use pie charts.

A skeptical look at John Snow’s Cholera Visualization

1. Read the following:

• 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?

2. Take the quiz due before next class start.

3. Download Tableau and do extra credit. 

4. Look over in-class work for week ahead (see schedule).

Bonus reads…

An old but still relevant TED talk by Eli Pariser on how the web is being inconspicuously personalized for individual tastes creating bubbles of like-minded people.…and its critique…and a more recent take on the topic.

In addition…

We will discuss open data sets this week. Aside from what’s in readings and the class materials here are some others:

• Business: data.gov’s “Impact” section
• Science: The Genomes Unzipped project
• Government: New York City parking violations
• Journalism: ProPublica data sets
• Random: Tableau public visualizations (often based on open data)
• More Random: Reddit’s Data Is Beautiful (usually has source data for the visualizations linked)

In addition take 2…

We will be talking about visualizations that are scheduled for Week 4. The idea here is to work ahead a bit so the following week is spent on Tableau hands on (in class and assignment 1) the entire session.

Note: This extra credit is due to me via email Sept. 24.

On your Temple portal, you have access to a service called LinkedIn Learning.

In either case, we are going to skip around a bit in the training to get you acquainted with it. It’ll will come in handy later. Once you’re in LinkedIn, you create a quick profile and search for Tableau. Here’s a popular course to use. 

There’s also a broad overview that’s popular, but less hands on.

Another option you could use is Tableau’s training videos here.

Instructions:

Basically it’s watch 3 of those Tableau videos in that second part (starting with the joining data). Get me a screen shot of the video and 3 bullets about what’s in the video.

The extra credit is 1 point tacked on to your final grade.

Go with:

• Part 2: First 3 videos Connecting to data sources, joining data and related fields.
• Part 3 videos: The first three: Displaying data underneath a workbook, adding…and reordering.
• Part 4 first three videos.

That should get you enough info to digest a bit of part 5 and 6. Those areas will be in the in-class assignment and first assignment.

 

Here’s a primer for week 2.

1. Read: 

• 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

2. Then take quiz.

3. Look over these data science terms that’ll be handy (2 minute read).

4. Download Tableau and do extra credit (see post on community site and your email for the product key). 

5. Look over in-class work for week ahead (see schedule).

 

This course uses Tableau a good bit. You can get a full copy of the Tableau software – PC or Mac – for free. The key you’ll need to activate is an email I sent.

To request a free copy of the software:

1. Download Tableau Desktop and Tableau Prep here
2. Select each product download link to get started. When prompted, enter your school email address for Business E-mail and enter the name of your school for Organization.
3. Activate with your product key that can be found in your email.

Tableau’s site has a series of quick start guides and video-based training. If you want to do something that we don’t cover in class, check there.

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

We are all drowning in data, and so is your future employer. Data pours in from sources as diverse as social media, customer loyalty programs, weather stations, smartphones, and credit card purchases. How can you make sense of it all? Those that can turn raw data into insight will be tomorrow’s decision-makers; those that can solve problems and communicate using data will be tomorrow’s leaders. This course will teach you how to harness the power of data by mastering the ways it is stored, organized, and analyzed to enable better decisions. You will get hands-on experience by solving problems using a variety of powerful, computer-based data tools virtually every organization uses. You will also learn to make more impactful and persuasive presentations by learning the key principles of presenting data visually.

It’s also worth noting that data science and literacy will apply to every profession. In fact, data science is forcing more integration between various fields and careers.

The schedule will show you the cadence of the course

Here’s the syllabus (you’ll need to be logged into your Temple account)