Syllabus|2017-18 Autumn Semestre Kadir Has University Faculty of Communication Data Journalism: The Basics

Course Objectives

The data journalism course will present fundamentals of open /data collecting, gathering, cleaning, analysis, visualisation  process and understanding FOIA. Data skills are getting more important to affect data driven works for media industry. In the developing Internet world; to understand and figure out how database journalism turns to data journalism; and as the future of journalism, to teach with open data disciplines how data became a strong role in transferring more efficient growing online resources, tools, and techniques. This course will also address common pitfalls in misinterpreting data..

Class Blog:
Class Notes:
Class Hastag: #Khasddj18
Hastags: #ddj #ddjedu #datajournalism #opendata
#vg #vgegt  #verigazeteciligi  #açıkveri
Facebook Data Journalism Group:

Pınar Dağ, @pinardag / Instructor
Hours: Fri 09:00-11:30 pm
Room:NM 421 -B-303
Phone:90/532- 134- 53- 92

Course Contents

What is Data Journalism — And Where Does it Come From
Definition of Messy Data, Accuracy of Data – How To Learn
The Recognition and Understanding of Data Formats
Finding Stories in Data – How Do You Do It
Cleaning and analysing Data – How to Make the Data Make Sense
Scraping Data – Using the Web as a Data Source
Introduction to Data Visualisation Tools (Open Source)
Visualising the data: what works and what doesn’t
Which tools are available with which (Graphics, Charts, Map etc)
Basic Statistics and Excel Working Practices
Freedom of Information Requests
Open Data – Definition
Open Data and Ethics
Sources and Techniques – How to Find Data
Social Media Data Analysis / Learn-Digital Content Verification Techniques

Learning Outcomes of the Course Unit

Understand the history, current practice and likely impact of data journalism.
Demonstrate the skills involved in accessing, analysing and curating data from publicly available sources, data collection.
Work efficiently in teams and newsroom environments to produce data journalism packages
Demonstrate editing and production skills, online web tools, understanding technologies available to create online, interactive data-driven stories
Design basics, effective visual communication, and data visualisation
Understanding the development process for creating data stories and dealing with messy data
Freedom of Information Request and Understanding the Open Data
Learning techniques for Verification of Digital Content
Making Open Data Creation Practice
Understanding how to use Social Media Data and Analysis & Technical Development
Learning Extended Search Techniques during the solo and group projects.

Planned Learning Activities and Teaching Methods

Weekly course reading materials, use and try the tools before lecture and homework linked  group, and solo projects practice and quizzes.

Books,sources,working with data:

Open Data  Institute
School of Data /
Getting Started with Data Journalism /Writing data stories in any size newsroom /by Claire Miller/
Data Journalism HandBook /Edited  by Jonathan Gray,Lilliona Bounegry and Lucy Chambers /
Data Journalism Heist /by Paul Bradshaw
Data Journalism: Mapping the Future
Gazeteciliğin Geleceği:Veri Gazeteciliği-Pınar Dağ

Online Visualising data tools/Desktop software

Excel  / Web Scraper
Google  Fusion tables
IBM Many Eyes
Google Spreadsheet Charts
OpenRefine (formally Google Refine
Tableau Public / Tableau data visualization software
Google Drive
Adobe Reader
Investigative Dashboard

Veri Gazeteciligi Blog
Participants/Students  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.


Attendance / Participation 20%
Practice/Exercise 10%
Project (Assignment) 20%
Homework Assignments 15%
Extra-Class Activities (Reading, Individual Work, etc.) 15%
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.

Any form of plagiarism is unacceptable and will result in serious penalties. All submitted works be fully supported by your own reaerch.

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

Assigment due no later than HOUR pm on DAY/ MONTH 2015-2016

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
Data Driven Journalism:
Open Data And Data Literacy Traning:  /
Read school of data stories:

Week 1| Due Sep 22:


Week 2-3 |Due Oct 6

Data Fundamentals
Different types of data
What is data?

Mini presentation:

Unstructured vs. Structured data

International Data

National Data

Civil Data: Social Media, crowdsourcing

Case Studies: Turkey–turkiye.html

Example: Tips & Tools–araclar-.html

Source: & Scoda


Solo Projects Applications  -FOI

A Great Big List of FOI Ideas


Week 4-5


What is Open Data? What is Open Goverment?

Global Open Data Index Review

Open Data Training: Turkey

What is Data Journalism? ( Data Driven Journalism &Structured Journalism)

The Data Journalism News Portals and Data Journalists

Examples: Awarded Data Journalism Projects (Since 2013 till today)

(  practice ) Find your data -solo

Week 6-7

1: Basic Statistics for everyone
2: Measuring Central Tendency

3: Measuring Distribution / Differentiation
4: Data Analysis with Excel & PivotTable
5: Data Extraction from PDF and Web

6: Messy Data Cleaning


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