Statistical Consulting

Study Course Description

Course Description Statuss:Approved

Course Description Version:4.00

Study Course Accepted:14.03.2024 11:37:05

Study Course Information
Course Code:		SL_122		LQF level:		Level 7
Credit Points:		4.00		ECTS:		6.00
Branch of Science:		Mathematics; Theory of Probability and Mathematical Statistics		Target Audience:		Life Science
Study Course Supervisor
Course Supervisor:		Andrejs Ivanovs
Study Course Implementer
Structural Unit:		Statistics Unit
The Head of Structural Unit:
Contacts:		23 Kapselu street, 2nd floor, Riga, statistikarsu[pnkts]lv, +371 67060897
Study Course Planning
Full-Time - Semester No.1
Lectures (count)		9	Lecture Length (academic hours)		2	Total Contact Hours of Lectures		18
Classes (count)		14	Class Length (academic hours)		2	Total Contact Hours of Classes		28
Total Contact Hours								46
Part-Time - Semester No.1
Lectures (count)		9	Lecture Length (academic hours)		1	Total Contact Hours of Lectures		9
Classes (count)		12	Class Length (academic hours)		2	Total Contact Hours of Classes		24
Total Contact Hours								33
Study course description
Preliminary Knowledge:
• Students should have good knowledge of statistics – commonly used concepts, methods and models. • R software.
Objective:
The aim of this study course is to introduce students with various skills necessary to be an effective statistical consultant. Such skills include communication of technical statistical concepts to non-statisticians, collaboration with other researchers, turning a research question into a statistical problem, managing the process of consultation and delivery of results according to technical background of the client, and others. Software tools that can help to communicate results better are demonstrated in the course. A general overview of statistical methods and the context of their application is provided to encourage students to develop their own methods-roadmap that can help in presenting potentially relevant methods to the client. Common misconceptions and misuses of statistics as well as some ethical considerations are discussed.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Need for statistical consulting. Types of clients. Consulting vs collaboration, mutual benefit of consulting. Communication with a client, consulting process. Defining problem and results. Time management.			Lectures		1.00	auditorium
2	Basic principles of preparing a written report. Presentations. Graphs.			Lectures		1.00	auditorium
3	Common issues, misconceptions and misuse of statistical methods.			Lectures		1.00	auditorium
				Classes		0.50	computer room
4	Resources and software tools to communicate statistical concepts.			Classes		1.00	computer room
5	Case study (1): problem, data, methodology, work routine, output.			Classes		1.00	computer room
6	Guest lecture (1): talk and discussion with an expert from academy/industry.			Classes		1.00	auditorium
7	Reporting statistical results. Writing statistical methods section in scientific articles.			Lectures		1.00	auditorium
				Classes		0.50	computer room
8	Scientific method of research versus hypothesis generation from data. Issues related to inter-disciplinary nature of statistical consulting and ethical considerations.			Lectures		1.00	auditorium
9	Data collection methods. Data management.			Lectures		1.00	auditorium
				Classes		0.50	computer room
10	Case study (2): problem, data, methodology, work routine, output.			Classes		1.00	computer room
11	Guest lecture (2): talk and discussion with an expert from academy/industry.			Classes		1.00	auditorium
12	Overview of research designs.			Lectures		1.00	auditorium
13	Overview of statistical methods. Predictive vs explanatory models.			Lectures		1.00	auditorium
14	Documentation of project. Content and formatting of the final report. Advanced R Markdown for writing reports.			Lectures		1.00	auditorium
				Classes		0.50	computer room
15	Case study (3): problem, data, methodology, work routine, output.			Classes		1.00	computer room
16	Guest lecture (3): talk and discussion with an expert from academy/industry.			Classes		1.00	auditorium
17	Graphs and interactive visualizations in R (ggplot2, plotly).			Classes		2.00	computer room
18	Interactive results communication using R and R Shiny.			Classes		2.00	computer room
19	Final presentations of course projects.			Classes		1.00	auditorium
Topic Layout (Part-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Need for statistical consulting. Types of clients. Consulting vs collaboration, mutual benefit of consulting. Communication with a client, consulting process. Defining problem and results. Time management.			Lectures		1.00	auditorium
2	Basic principles of preparing a written report. Presentations. Graphs.			Lectures		1.00	auditorium
3	Common issues, misconceptions and misuse of statistical methods.			Lectures		1.00	auditorium
				Classes		0.50	computer room
4	Resources and software tools to communicate statistical concepts.			Classes		1.00	computer room
5	Case study (1): problem, data, methodology, work routine, output.			Classes		1.00	computer room
6	Guest lecture (1): talk and discussion with an expert from academy/industry.			Classes		1.00	auditorium
7	Reporting statistical results. Writing statistical methods section in scientific articles.			Lectures		1.00	auditorium
				Classes		0.50	computer room
8	Scientific method of research versus hypothesis generation from data. Issues related to inter-disciplinary nature of statistical consulting and ethical considerations.			Lectures		1.00	auditorium
9	Data collection methods. Data management.			Lectures		1.00	auditorium
				Classes		0.50	computer room
10	Case study (2): problem, data, methodology, work routine, output.			Classes		1.00	computer room
11	Guest lecture (2): talk and discussion with an expert from academy/industry.			Classes		1.00	computer room
12	Overview of research designs.			Lectures		1.00	auditorium
13	Overview of statistical methods. Predictive vs explanatory models.			Lectures		1.00	auditorium
14	Documentation of project. Content and formatting of the final report. Advanced R Markdown for writing reports.			Lectures		1.00	auditorium
				Classes		0.50	computer room
15	Case study (3): problem, data, methodology, work routine, output.			Classes		1.00	computer room
16	Guest lecture (3): talk and discussion with an expert from academy/industry.			Classes		1.00	auditorium
17	Graphs and interactive visualizations in R (ggplot2, plotly).			Classes		1.00	computer room
18	Interactive results communication using R and R Shiny.			Classes		1.00	computer room
19	Final presentations of course projects.			Classes		1.00	auditorium
Assessment
Unaided Work:
Students should explore further topics discussed in lectures and build their own personal views. This includes reading relevant journal articles, blogs of statistics practitioners and consultants, and other resources. Part of the contents of this course is beyond the scope of academic books. Development of the course project.
Assessment Criteria:
Assessment on the 10-point scale according to the RSU Educational Order: • Course project – Students can choose from multiple projects provided. Students have to prepare the report of the project and present the results giving a presentation – 60%. • Written exam – 40%.
Final Examination (Full-Time):		Exam (Written)
Final Examination (Part-Time):		Exam (Written)
Learning Outcomes
Knowledge:		• Explains the main steps and good practices of the statistical consulting process. • Understands the role of a statistical consultant in interdisciplinary research. • Is aware of the most common mistakes made when applying statistical methods. • Classifies different research designs, data collection methods and corresponding statistical methods. • Selects the main statistical methods for solving problems of different type. • Defines R code syntax and packages for frequently used statistical tests and models.
Skills:		• Communicates statistical concepts and methods (and misuse of them) with clients of different backgrounds. • Processes independently and transforms data for analysis. • Chooses and implements the most appropriate statistical method for the given data and problem. • Develops the final report and presentation using R Markdown functionality. • Prepares interactive R application to communicate results using R Shiny, can present the results in writing and orally to both industry professionals and non-specialists.
Competencies:		On successful course completion students should be able to take part in consulting process and obtain necessary information from the client to evaluate the possibility of collaboration. Students can describe steps necessary to perform analysis to the client, give overview of the corresponding methodology and outline the potential outcomes. Students are prepared to manage their work and issues during the consulting process (possibly under some supervision) to support reliable and scientific research.
Bibliography
No.	Reference
Required Reading
1	Cabrera, J. & McDougall, A. (2013). Statistical consulting. Springer Science & Business Media.
2	Hand, D. J. & Everitt, B. S. (Eds.). (2007). The statistical consultant in action. Cambridge University Press.
Additional Reading
1	Wasserman, L. (2013). All of statistics: a concise course in statistical inference. Springer Science & Business Media.
2	Friedman, J., Hastie, T. & Tibshirani, R. (2001). The elements of statistical learning. New York: Springer series in statistics. Available from: https://web.stanford.edu/~hastie/Papers/ESLII.pdf
3	Izenman, Alan J. (2001). Modern Multivariate Statistical Techniques. New York: Springer series in statistics.
4	Härdle, W. & Simar, L. (2007). Applied multivariate statistical analysis. Berlin: Springer.
5	Montgomery, D. C. (2017). Design and analysis of experiments. John Wiley & sons.
6	Xie, Y., Allaire, J. J. & Grolemund, G. (2018). R Markdown: The definitive guide. CRC Press.