Data journalism(JOU308)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| JOU308 | Data journalism | 6 | 1 | 2 | 0 | 3 | 4 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | Turkish |
| Course Type | |
| Course Level | Bachelor Degree |
| Course Instructor(s) | Tolga ÇEVİKEL tcevikel@gsu.edu.tr (Email) |
| Assistant | |
| Objective |
The Data Journalism course aims to understand practically and theoretically the basic stages of this field, which are data finding, data collection, data cleaning, data analysis, data visualization, and to understand the open data relationship by using the right to information. In addition, the course emphasizes computer-assisted journalism and data-based journalism as a result of data journalism in the developing Internet world and describes how it plays an essential role in more effective transmission of news in newsrooms as the future of journalism, with developing online open sources, tools and techniques. |
| Content |
Data Types Open Data, Data Disclosure Methods, and Ethics Definition/History of Data Journalism - Where It Came From Definition of Scattered Data, Accuracy of Data Recognizing and Understanding Data Formats How to Find Data Data Sources and Database Management Finding a Story with Data - How-To Data Cleaning - How to Make sense of Data Data Scraping - Websites as a data source Introduction to Data Visualization Tools (Open Source) Right Graph by Subject, Selection Map Basic Statistics and Working Practices in Excel Relationship between Right to Information Request and Open Data Resources and Techniques Social Media Data Analysis / Usage Verifying Digital Content |
| Course Learning Outcomes |
In this course, the student Learns concepts related to data journalism, its history, and tools with examples, Data Journalism experiences and collects data using the most appropriate tools online, what the data sources are and how datasets are created, Gains knowledge of data analysis, visualization, interviewing data and conducts effective teamwork, Understands and applies the use of data in news centers and news, Performs design fundamentals, effective visual communication and data visualization, mapping studies, Understands and implements the process of producing data-based news. |
| Teaching and Learning Methods |
Weekly pre-class online reading. Studying tools, group, and solo project-produced assignments/tests. Practical studies from the 3rd week, the use of tools. Fieldwork, data center visits. |
| References |
AVVG http://www.avvg.org.tr/yazilar/94-acik-veri-ve-veri-okuryazarligi-egitimleri%C2%A0herkesin-erisimine-acildi.html Data Journalism: Inside the global future http://www.slideshare.net/mrdamian/data-journalism-inside-the-global-future https://www.amazon.co.uk/d/Books/Data-Journalism-Inside-global-future-Tom-Felle/1845496639 Data Journalism: Mapping the Future http://www.abramis.co.uk/books/bookdetails.php?id=184549616 Scraping for Journalists https://leanpub.com/scrapingforjournalists NUMBERS IN THE NEWSROOM: USING MATH AND STATISTICS IN NEWS, SECOND EDITION, E-VERSION http://store.ire.org/products/numbers-in-the-newsroom-using-math-and-statistics-in-news-second-edition-e-version 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 Gazeteciliğin Geleceği:Veri Gazeteciliği-Pınar Dağ https://docs.google.com/document/d/1ueHNSvev8C5FRf9c-wwfoTrdbTLfrRPYakpphCOj2Ig/edit Finding Stories in Spreadsheets https://leanpub.com/spreadsheetstories |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Basic Data Concepts |
| 2 | Data Research Techniques |
| 3 | Data Cleaning |
| 4 | Data Literacy, Metadata, Open Data |
| 5 | Effective Utilization Methods of BEH |
| 6 | Thinking Like a Data Journalist |
| 7 | What Does Good/Bad Datasets Mean? |
| 8 | Midterm exam |
| 9 | Data Analysis and Interpretation |
| 10 | Data Visualization Principles |
| 11 | Data Visualization Applications |
| 12 | R Visualization for Data Journalism |
| 13 | Storytelling with Data |
| 14 | Presentation of Group and Solo Projects |
Practice Topics
| Week | Weekly Contents |
|---|
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 1 | 40 |
| Contribution of final exam to overall grade | 1 | 60 |
| Toplam | 2 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 0 | 0 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 0 | 0 |
| Project | 0 | 0 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 0 | 0 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Toplam | 0 | 0 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 14 | 3 | 42 |
| Working Hours out of Class | 14 | 3 | 42 |
| Midterm Examinations (including preparation) | 1 | 10 | 10 |
| Final Examinations (including preparation) | 1 | 10 | 10 |
| Total Workload | 104 | ||
| Total Workload / 25 | 4,16 | ||
| Credits ECTS | 4 | ||