Data-Driven Decision-Making
Course code
DTI6003.DT
old course code
Course title in Estonian
Andmepõhine otsustamine
Course title in English
Data-Driven Decision-Making
ECTS credits
6.0
Assessment form
Examination
lecturer of 2023/2024 Spring semester
Paula Joanna Sillat (language of instruction:Estonian)
lecturer of 2024/2025 Autumn semester
Not opened for teaching. Click the study programme link below to see the nominal division schedule.
Course aims
Support the development of knowledge and skills for data-driven decision making. Develop the learner’s skills in applying methods of research design, data collection, analysis and presentation.
Brief description of the course
Data types and structures. Quantitative and qualitative data. Big data, open data, linked data, metadata. Data-driven decision making. Decision data quadrants. Data anlysis and data analytics. Types of analysis: descriptive analysis, diagnostic analysis, predictive analysis, prescriptive analysis. Applying analytics (business analytics, deep analytics, learning analytics, cultural analytics, etc.). Data literacy and data-analytic thinking. Data culture. Research design as a planned strategy for conducting research. Research designs: quantitative, qualitative, mixed methods. Quantitative and qualitative methods of data collection. Purposeful choice of data collection methods. Collecting data in studies with qualitative, quantitative and mixed methods research design.
Qualitative data analysis: transcribing, coding (inductive and deductive approaches), thematic analysis, discourse anlysis.
Quantitative data analysis: overview of data anlysis softwares, organization and coding of data, types of variables, descriptive statistics, frequency tables, tests of statistical significance, correlation and regression analysis.
Overview of data visualization softwares. Graphical data presentation: bar chart, histogram, pie chart, line graph, area chart, scatter plot, range chart, network diagram, matrix, box plot.
Qualitative data analysis: transcribing, coding (inductive and deductive approaches), thematic analysis, discourse anlysis.
Quantitative data analysis: overview of data anlysis softwares, organization and coding of data, types of variables, descriptive statistics, frequency tables, tests of statistical significance, correlation and regression analysis.
Overview of data visualization softwares. Graphical data presentation: bar chart, histogram, pie chart, line graph, area chart, scatter plot, range chart, network diagram, matrix, box plot.
Learning outcomes in the course
Upon completing the course the student:
- understands types of data in different fields and the nature of data-driven decision-making;
- plans research design and creates data collection instruments for data-driven decision-making;
- organizes the collected data for analysis;
- selects and applies suitable data analysis methods and tools based on the goal;
- uses data visualization softwares to present results and make scientific conclusions based on the data.
- understands types of data in different fields and the nature of data-driven decision-making;
- plans research design and creates data collection instruments for data-driven decision-making;
- organizes the collected data for analysis;
- selects and applies suitable data analysis methods and tools based on the goal;
- uses data visualization softwares to present results and make scientific conclusions based on the data.
Teacher
Paula Joanna Sillat
Study programmes containing that course