Syllabus| 2013-2014 Spring Semestre Data Journalism
2013-2014 Spring Semestre KadirHas University Faculty of Communication Data Journalism: The Basics
Spring 2014: Feb 6 – May 8
Thursdays 13:00-16:00 pm
Room 114 /New Media
Data Journalism course aims to provide reporters, editors, journalists, media practitioners and the general public with knowledge about what is data journalism, the techniques behind this approach, how to present the story, from world of data –social media updates, elections details,economy budgets and how this kind of journalism is impacting newsrooms and communities around the world. Newsrooms need to know how to play with data and fine stories and shape them in compelling way. DJ teaches journalist, editors to analyze and visualize data stories with discipline to touches on information and mapping, interactivity design,tools and data analysis. Participants/ Students are expected to report, pitch,create and produce stories working alone and in teams.
Class Blog: http://verigazeteciligi.wordpress.com
Class Notes: http://piratepad.net/KxVfSJhX4F
Pınar Dağ,Instructor , 114
pinar.dag@dagmedya.com
Skype:pinar.dag1
Hours: Thursdays 13:00-16:00 pm
Phone:90/532- 134- 53- 92
Hassel Fallas/ Data investigative journalist/ visualization/storytelling -Costa Rica
@nacion.com / about.me/hassel.fallas
hassel.fallas@nacion.com
March 13
Nicolas Kayser-Bril, 28, is CEO and co-founder of Journalism++ , an agency for data-driven stories. Before founding Journalism++ in late 2011, Nicolas was in charge of datajournalism at OWNI, where he led several experiments in crowdsourced data gathering. A self-taught programmer and journalist, Nicolas holds a degree in Media Economics. You can follow him on Twitter @nicolaskb. Nicolas will speak about the business side at the micro level, life as a #ddj agency in Europe.
Liliana Bounegru / Editor of http://DataJournalismHandbook.org
This three-credit course aims to teach Data Driven Journalism basics&practices. Students will discuss,pitch, report, design and produce a data-driven pieces.
Objectives: At the end of DJ course, students will come away with knowledge about:
- What is data journalism
- How data is used in the media industry today
- How to interview and clean messy data
- How to analyze&understand the data
- How to identify the proper presentation of the data with the story& Create visualisations from maps to social interactives
- How to use online web tools such as Tableau data visualization software , Google Fusion tables, IBM Many Eyes,DataWrangler,Google Spreadsheet Charts,DataMarket,BatchGeo, Maps, and integrate them in a non code-intensive development environment.
- Where to find data and the stories in the data & source data for your projects
Books,sources,working with data:
- Open Data Institute / http://theodi.org/
- School of Data / http://schoolofdata.org/
- Getting Started with Data Journalism /Writing data stories in any size newsroom /by Claire Miller/https://leanpub.com/datajournalism
- Data Journalism HandBook /Edited by Jonathan Gray,Lilliona Bounegry and Lucy Chambers /http://datajournalismhandbook.org/
- Data Journalism Heist /by Paul Bradshawhttps://leanpub.com/DataJournalismHeist
- Data Journalism: Mapping the Future
http://www.abramis.co.uk/books/bookdetails.php?id=184549616
Online Visualising data tools/Desktop software
Google Fusion tables
IBM Many Eyes
DataWrangler
Google Spreadsheet Charts
BatchGeo
http://querytreeapp.com/
datawrapper.de
OpenRefine (formally Google Refine https://code.google.com/p/google-refine/)
http://quartz.github.io/Chartbuilder/
Tableau Public / Tableau data visualization software
Google Drive
Openoffice
Adobe Reader
Online Mapping data tools
http://blog.cartodb.com/
Tips* ( Clair Miller Book)
Top Ten Things for getting Started in Data Journalism
Data Journalism:Resources to help reporters get started collecting and analyzing data
Manual on Microsoft Excel for data journalists
Learning outcomes
At the end of this course, students will be able to:
- Identify patterns in data and an understanding of data journalism& how it can work.
- Knowledge how to use the internet and social media to access and interpret data and conceptualize clear&concise ways to illustrate.
- Knowledge of the web tools and using them.
- Evaluate effectiveness of data-based storytelling projects, Data Journalism Awards stories both of their own creation and across the industry.
About instructors
Pınar Dağ, Journalist , Researcher, Lecturer / Hassel Fallas Data investigative journalist/ visualization/storytelling @nacion.com Master in Digital Journalism Univ. of Alcalá & Digital Marketing
UTA -Costa Rica. · about.me/hassel.fallas Liliana Bounegru / Editor of http://DataJournalismHandbook.org + http://DataDrivenJournalism.net
Veri Gazeteciligi WordPress Blog
Participants/Students final stories will be showcased in the class blog(Verigazeteciligi). Participants/students will be required to present the stories in class for discuss/critique. Project posts to the class blog are public by default, but can choose to keep them private. Participants/Students are encouraged to submit superior and/or timely work for publication elsewhere, including school outlets such as the Kadir Has University Faculty of Communication.
Grading
Assignment1 ( Group projects1-2): 30%
Assignment2 ((Senior Project): 30%
Participation&Blog&homework assigment : 20%
Final Exam 20%
Grade is determined by participation, completion of all solo homework assignments & completion of the two major team assignments. Assignments will be evaluated in terms of use of data, online web tools, story,context,design.
Grades for each major assignment are further broken down as follows:
Pitch (25%)
Storyboard (12.5%)
Draft (25%)
Final (25%)
Revision (12.5%)
What does it mean pitch?
*Why we care? *Who cares? *What pre-reporting you have done, incluiding a questions, an angle/a hook. why this story is matter/important?, a link of data (Means you found a data), one source.
What is Storyboard?
A sketch of how to organize a story and a list of its contents. A storyboard helps you: Define the parameters of a story within available resources and time. Organize and focus a story. Figure out what medium to use for each part of the story. Check out Mark Luckie’s thoughts on sketching/storyboarding, with examples, from 10,000 Words.
What is rough draft?
A late stage in the writing process.It is not a final report but it should be close that. Stundents should have created the visualizations that plan to use. Stundents complete rough draft must includes: clean data in spreadsheets, visualizations of the data,captions, credits, a headline, at least three links to other reporting that puts your story in a broader context, introductory text that includes information gleaned from at least one human source,a source list.
What is final report?
Story must be posted to the class blog. Need to post a headline, excerpt, image and linked text to the class blog.
Plagiarism
Any form of plagiarism is unacceptable and will result in serious penalties. All submitted works be fully supported by your own reaerch.
Copyright
In addition to being a serious academic issue, copyright is a serious legal issue. Please check for better understand, the Citizen Media Law Project it http://www.dmlp.org/legal-guide/search
Deadline
Assigment due no later than HOUR pm on DAY/ MONTH 2014
Absences and Tardiness
A minumum of 85% attendance is required across all elements of the course- specially group-based working etc. Please be in time for class and back to class from breaks. Repeated tardiness will result in a reduction of grade in participation.
Course Schedule:
Every Week:
Read Datablog http://www.theguardian.com/news/datablog
Read school of data stories: http://schoolofdata.org/category/data-stories/
Read Anna Cordrea-Rado’s “Between the Spreadsheets” column at Columbia Journalism http://www.cjr.org/between_the_spreadsheets/
Read Kevin Quealy’s blog, “Charts and Things”: http://chartsnthings.tumblr.com
Read:
Simon Rogers: Why data journalism is the new punk and what it’s got to do with Joe Strummer
http://jonslattery.blogspot.com.tr/2014/01/simon-rogers-why-data-journalism-is-new.html
Due Feb 6:
Watch history of data journalism at the Guardian: http://www.youtube.com/watch?v=iIa5EoxyvZI
Watch Geoff McGhee’s Knight Fellowship Report on Data Journalism at http://datajournalism.stanford.edu/ Chapter 2 Data Vis in Journalism / Chapter 3 Telling “Data Stories” / Chapter 6 Exploring Data
1| Feb 6: (Week 1/2/3)
Introduction to Data Journalism. Course introduction (expectations, syllabus review)
What is data journalism and its history &why data journalism
The components of data journalism
The mindset and framework to understanding Data journalism and its application in various contexts
Each Week:
-Videos lectures ( School of Data etc)
-A quiz ( School of Data Quiz )
-Discussion areas: Work in groups to evaluate ddj stories (max4-5 stories) (Also via Piratepad/)
Assignment (Assignment be will at class blog –verigazeteciligi.wordpress.com)
Due Feb 12:
Find 2 or 3 datasets that interest you (Around the world)
Find two datasets that interest you. Tell class where the data can be found (the URL) and in 1-2 sentences explain why the data is interesting or important. Read/check 30 sources to find th data you need: http://flowingdata.com/2009/10/01/30-resources-to-find-the-data-you-need/ Data Journalism Awards2013: https://review.wizehive.com/voting/dja2013/
2|Week 4/5/6:
Discuss homework /preparation
Spreadsheet review:data types, rows and columns, sorting, copy and paste, selections, formulas & introduction to Pivot tables
Where to Find the Data and the Stories
How to find the stories in the data
Best practices and examples of stories and visualizations using data in various contexts
Class Exercise:Using spreadsheets and Pivot tablets
Each Week:
-Videos lectures ( School of Data etc)
-A quiz ( School of Data Quiz )
-Discussion areas: Work in groups to evaluate ddj stories (max4-5 stories) (Also via Piratepad/)
Assignment (Assignment be will at class blog –verigazeteciligi.wordpress.com)
Week 7/8/9
How to Interview the Data
How to treat data like any other source in reporting process
How to look for flaws/limitations in datasets
How to ask questions of the data to lead to stories
Best practices in examining the data
Each Week:
-Videos lectures ( School of Data etc)
-A quiz ( School of Data Quiz )
-Discussion areas: Work in groups to evaluate ddj stories (max4-5 stories) (Also via Piratepad/)
Assignment (Assignment be will at class blog –verigazeteciligi.wordpress.com)
*Online/Guest teacher: Hassel Fallas / Data investigative journalist/ visualization/storytelling @nacion.com / about.me/hassel.fallas
Week 10/11/12:
How to Bring the Data to Life (part 1)
What is the atomic unit of dataset
The structure of a news application and its pages
The pitfalls of creating a web application
Design techniques of data web apps
-Videos lectures ( School of Data etc)
-A quiz ( School of Data Quiz )
-Discussion areas: Work in groups to evaluate ddj stories (max4-5 stories) (Also via Piratepad/)
Assignment (Assignment be will at class blog –verigazeteciligi.wordpress.com)
Assignment (Assignment will be at class blog –verigazeteciligi.wordpress.com)
Week 13/14:
How to Bring the Data to Life (part 2)
Understanding what is data visualization and how it can be used
Tenets of what goes into good storytelling and data visualization
Choosing the right visualisation tools and resources to build your own data visualization
-Videos lectures ( School of Data etc)
-A quiz ( School of Data Quiz )
-Discussion areas: Work in groups to evaluate ddj stories (max4-5 stories) (Also via Piratepad/)
Assignment (Assignment be will at class blog –verigazeteciligi.wordpress.com)
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Certificate of Completion// For those that meet all of the requirements, an electronic certificate of completion will be sent via email in PDF format. No formal course credit is associated with the certificate. The certificate is awarded by the Dağ Medya / Veri Gazeteciliği 2014
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