UV9290 ¨C Data Science

Course content

In this course, you will learn to work with the core concepts and techniques of descriptive and inferential statistics that function as foundations for formulating and implementing successful data-based analysis strategies to perform evidence-based research.

You will be introduced to the essentials of basic programming and use of syntax-based data analysis as instantiated in the open-source statistical and graphic software environment R.

The course covers the following five key topics:

1. Data Management: wrangling & auditing

2. Descriptive Statistics

3. Data Visualization and Representations (i.e., plots, tables, diagrams)

4. Probability and Randomness

5. Statistical Inference & Design

Throughout the course, attention will be given to issues regarding questionable research practices and research ethics.

UV9290 Data Science is the PhD-level version of MAE4000 Data Science, a compulsory course in the master's program, Assessment Measurement and Evaluation. The content, schedule and reading list for UV9290 Data Science are the same as for MAE4000 Data Science.?

Learning outcome

Knowledge

• Recognize the challenges with respect to data collection, data quality, and alignment between research questions and the data.
• Recognize descriptive statistics as basic summaries of specific data features.
• Recognize that sampling variability and uncertainty are ubiquitous.

Skills

• Run basic data management, visualization, and analysis techniques using the open source statistical software environment R.

Competence

• Manage a core dataset by wrangling it into shape for specific data-analyses and performing an audit to document and clean unexpected irregularities.
• Visualize data paying attention to basic quality criteria to increase clarity and communication value.
• Perform and communicate basic data analyses, taking into consideration features of the study design and inferential uncertainty.

Admission to the course

There is a limited number of seats due to joint teaching with the master¡¯s level version of the course, MAE4000 Data Science.

PhD candidates at the Faculty of Educational Sciences will be given priority, but it is also possible for others to apply for the course.

The deadline for registration is on the corresponding semester page for the course.?

Candidates admitted to a PhD-program at the Faculty of Educational Sciences (UV) can apply in?StudentWeb.

Other applicants can apply by filling out and sending in a electronic registration form, which is found on the corresponding semester page for the course.?

Overlapping courses

• 5 credits overlap with MAE4000 ¨C Data Science.
• 1 credits overlap with UV9214 ¨C Regression Analysis (discontinued).
• 1 credits overlap with UV9214U.
• 1 credits overlap with UV9290U.

Teaching

This course combines lectures and computer labs with data analysis tasks in statistical software environments.

Obligatory course components:

• 80% attendance requirement for the lectures
• A diverse set of small and moderate assignments is used to keep track of student progress throughout the course.

Lectures are held by Professor?Johan Braeken.

The learning management system CANVAS is used for providing detailed information about requirements and deadlines for assignments.

Successful completion of each assignment is a prerequisite for being allowed to submit the final portfolio exam.

Schedule and literature: Please see the applicable semester page for the course.?

Examination

To obtain 5 credits, 80 % attendance, successful completion of the mandatory assignments and paper is required.

A more specific description of the mandatory assignments and paper will be given at the course.

Language of examination

The examination text is given in English, and you submit your response in English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

More about examinations at UiO

• Use of sources and citations
• Special exam arrangements due to individual needs
• Withdrawal from an exam
• Illness at exams / postponed exams
• Explanation of grades and appeals
• Resitting an exam
• Cheating/attempted cheating

You will find further guides and resources at the web page on examinations at UiO.