VPYT6000 Python Programming and Data Science for Journalists Emneplan

This course is an introduction to principles of Python Programming and Data Science.

Most journalists do not have a solid quantitative background\and face difficulties when analysing data independently. This difficulty represents a major drawback in journalistic research. Journalists waste time learning analytical methods by themselves that could be more quickly learned with proper instruction and support. As consequence, the quality of insights and research productivity suffer. This course provides a comprehensive introduction to data science and big data applied to investigative journalism.

One of the skills that are crucial for implementing and conducting Data Science projects is the knowledge of programming. This course focuses on the development of basic programming techniques, analytical thinking, comprehension of code, and problem-solving skills achieved through a programming-based approach. It aims to develop basic programming skills relevant for professional use within the realm of journalism.

The content is designed to train the participants in state-of-the-art techniques in data analysis. This will enable the students to interact independently with the data and draw insights from them. This is a hands-on course where the students will learn from implementing the analysis themselves with close supervision. The course will focus on case studies using data from real cases; advanced students may choose to use their own data. The students will develop understanding through constant presentation of their work and dialectical reflection over their choices, results, and interpretations.

Language of instruction is English.

None.

After completing the course, the student should have the following overall learning outcomes defined in terms of knowledge, skills and general competence:

Knowledge

The student knows:

• basic theoretical and practical aspects of data management and data processing for different data types.
• how statistics, dimensionality reduction, and supervised and unsupervised learning may be applied to data analysis in investigations.
• basic data visualization techniques and how to employ them in exploratory data analysis and scientific communication.

The student has:

• an understanding of what an algorithm is and the ability to use algorithmic problem-solving to address real-life problems in business and administration
• basic knowledge of how processes within the realm of journalism can be automated using software
• insight in how software is written, and an understanding of various types of programming languages and their function in various areas of business and administration.

Skills

The student can:

• apply data analysis techniques to collect data, pre-process it, analyse it, and reach conclusions about the data.
• identify and define a problem and craft a solution using data analytics.
• critically assess the results as well as justify and explain the methodological choice.

The student has acquired an ability to:

• format and write basic code
• explain how problem-solving principles are used in programming
• understand how to frame and elicit unstructured business and/or administration problems to solve them through programming.

General competence

The student can apply:

• data analysis principles to data in research.
• methods and tools for data analysis and visualization.

The student:

• is proficient in planning and implementing a project plan for software development for journalism
• is familiar with basic theoretical and practical aspects of data management and data processing for different data types.
• is familiar with basic data visualization techniques and how to employ them in exploratory data analysis and scientific communication.

The course is assembly-based, encompassing two full day gatherings that necessitate in-person presence on campus, and four online gatherings.

The course will take a hands-on learning approach in addition to learning the theoretical concepts. Course participants will work in groups under guidance on a project relevant to journalism.

The following coursework requirements must have been approved for the student to take the exam

• Coursework 1: The students need to submit a project proposal in groups consisting of 2-3 students. The proposal needs to take into consideration all of the aspects covered in class regarding the planning and structuring of a data investigation. The students need to demonstrate the ability to assess where and how to collect the data, how to formulate a hypothesis to test and the capability of designing a possible data analysis pipeline including risk-mitigation strategies. The project proposal will consist of maximum 500 words.

All required coursework must be completed and approved by the given deadline for the student to take the exam. If the coursework requirements have not been approved, the student will be given one opportunity to submit an improved version by a given deadline.

The exam in the course will be carried out in groups consisting of 2-3 candidates.

For the exam, students will submit a portfolio that includes a project report and a code repository.

The project report must have a scope of 4000 to 5000 words. Font and font size: Arial / Calibri 12 points. Line spacing: 1.5.

Students awarded a fail grade are given one opportunity to submit an improved version of the portfolio for assessment.

All support materials are allowed.

Grade scale A-F.

The exam papers are assessed by one internal and one external examiner. At least 25% of the exam papers will be assessed by two examiners. The grades awarded for the papers assessed by two examiners form the basis for determining the level for all the exam papers.