Final Project Pitch due April 18 at end of class

Worth 5 points

For your final project for this class, you will produce a publishable feature story using data and visualizations.

You have two options for selecting a topic:

  1. You either pick your own story OR
  2. I can assign you a topic about Rowan.

Here are suggestions for how to come up with a topic…

  • Start with a topic that you are interested in.
  • Think about questions you have? Frame your story as a question.
  • Try to narrow it down a particular focus. Keep it concrete, specific and simple.
  • Think about access. Can you go places and talk to people for your topic?
  • Start searching for where and how you might obtain the data you need.

Here’s how you are going to pitch your story:

Post your Pitch and Data Plan to in the following format…

Your Name’s Final Project Pitch and Data Plan

Pitch: Tell me:

  • What is your story?
  • Why is it interesting?
  • Where could you pitch it? (Take this seriously. Find a real publication where you think you could pitch your story.)

Sources of data: I’m using the following sources:

  • Source with link if available
  • Or if you are creating your own data set through surveys or some other means, tell me your plan.

Story framed as question(s): I am looking to answer the following question(s):

  • Question 1
  • Question 2 (if needed)
  • Question 3 (if needed)

It should look something like this…

Sam’s Final Project Pitch

Pitch: My story is about the NBA draft and how important draft position is to yielding a star player. It is interesting because there is the perception that teams need to get the first overall pick to draft a superstar, but many of the league’s star players today were drafted outside of the top 2, 5, and even 10 picks. I am writing it for a sports blog like SBNation. In addition, I will be comparing the results of my findings to the “tanking” strategy that was employed by the Sixers over the last few years and looking at if this a viable way to rebuild a NBA teams

Sources of data: I will be using the records of NBA drafts dating back to the 1970’s and player performance metrics such as MVP voting, scoring averages, First and Second All-NBA teams, Championships won, and more to create tiers of star players over the decades and then chart their draft positions.

Questions: I am looking to answer these questions…

  • At what draft position are NBA teams most likely to draft a superstar?
  • How often do superstar players get drafted outside of the top 10?
  • Is the Sixers “tanking” strategy a viable rebuild plan for NBA teams in the future?

If you want to be assigned a topic, you must still figure out your specific angle, data source, and write a pitch as outlined above.

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Week 10 – Tues., April 2 and Thurs., April 4

On Thursday we will Skype with Stephen Stirling, data reporter. In preparation, please review the following and come with questions:

In Class:
-Review your three basic charts with Google Sheets
-Intro to some out-of-the-box visualization tools

Assignment for next Tuesday:
-Sign up for a Flourish account.
-Explore the examples of what you can do with it.
Make these two election graphics. These are not graded, but you will start doing graded assignments with Flourish next week. So this is a chance to get familiar with it. Bring your graphics and questions on Tuesday.

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Making Election Maps using Flourish


To begin, create an account on Flourish


Continue reading

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Intro to Making Maps with Google My Maps

Google’s My Maps

If you want to make a simple map with just a few points or with a relatively small spreadsheet (no more than 2000 rows), you can use Google’s My Maps. You can manually add points, lines, and shapes. Or you can import a spreadsheet with addresses, places, or long/lat coordinates.

For example, see My Maps Gallery

Here is an Intro to My Maps

How to Create a Map

Continue reading

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Week 9 – Tues., March 26 and Thurs., March 28

In Class:
Scraping using Google Sheets (revisited with Paste Special)
-Inverted Pyramid of Data Journalism
-My Data Journalism “Pick of the Week” – FlowingData Browse through these and tell us one of your favorites on Thursday.
Key concepts of data visualization
Visualization 101(Visage + Hubspot)
Charts and Graph Thought-Starter
In-Class Viz exercise

-Make 3 basic charts with Google Sheets. Due at start of class on Tuesday.

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In-Class Lego Viz Exercise


Get your partner.

Open the file RowanCrime15-17forViz

Analyze the data. Do some calculations.

Ask: What is significant? What is newsworthy?

Pick one thing you think is the most significant and newsworthy and figure out how to visualize it.

Sketch your idea on paper.

Figure your block-to-data ratio.

Get a green board. Gather the legos you need. Build it.

Use paper to make a title and labels.

Bring your finished product up front by 12:00pm.

Be prepared to explain it.

Your visualization should be:

  • Clear – viewer should understand what they are seeing without your explanation.
  • Simple
  • Informative
  • Visually appealing.
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Basic Charts with Google Sheets

Create three basic charts over the weekend. Bring them to class with you on Tuesday. We’ll look at them and discuss.

Using the Rowan University crime stats in this Google Sheet

Copy data to your own document.

Refer to “Design best practices” slides for pie, bar and line charts in Data Visualization 101 


Use Google Sheets to make your charts

1. Create a pie chart the shows composition of 2017 drug arrests and drug referrals. Use percentage; your two categories should total 100%.

2. Create a bar chart that shows comparison of four categories from 2017: drug arrest, drug referrals, alcohol arrests, alcohol referrals. Use the number of violations.

3. Create line chart that shows the total reported crime for 2015, 2016 and 2017.

Here are some step-by-step instructions:

Your charts should have clear and descriptive titles.

Your charts should have appropriate labels.

They should be functional and visually appealing.


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