Veidlapa Nr. M-3 (8)
Study Course Description
Statistical Consulting
Course Code
SL_122
Branch of Science
Other social sciences
ECTS
6.00
Target Audience
Life Science
LQF
Level 7
Study Type And Form
Full-Time; Part-Time
Study Course Implementer
Course Supervisor
Andrejs Ivanovs
Structure Unit Manager
Andrejs Ivanovs
Structural Unit
Statistics Unit
Contacts
14 Balozu street, Block A, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
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.
Preliminary Knowledge
• Students should have good knowledge of statistics – commonly used concepts, methods and models. • R software.
Learning Outcomes
Knowledge
1.• 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
1.• 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.
Competences
1.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.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual 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.
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
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%.
|
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
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.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Basic principles of preparing a written report. Presentations. Graphs.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Common issues, misconceptions and misuse of statistical methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Resources and software tools to communicate statistical concepts.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Case study (1): problem, data, methodology, work routine, output.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Guest lecture (1): talk and discussion with an expert from academy/industry.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Reporting statistical results. Writing statistical methods section in scientific articles.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Scientific method of research versus hypothesis generation from data. Issues related to inter-disciplinary nature of statistical consulting and ethical considerations.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Data collection methods. Data management.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Case study (2): problem, data, methodology, work routine, output.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Guest lecture (2): talk and discussion with an expert from academy/industry.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Overview of research designs.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Overview of statistical methods. Predictive vs explanatory models.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Documentation of project. Content and formatting of the final report. Advanced R Markdown for writing reports.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Case study (3): problem, data, methodology, work routine, output.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Guest lecture (3): talk and discussion with an expert from academy/industry.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Graphs and interactive visualizations in R (ggplot2, plotly).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Graphs and interactive visualizations in R (ggplot2, plotly).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Interactive results communication using R and R Shiny.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Interactive results communication using R and R Shiny.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Final presentations of course projects.
|
Total ECTS (Creditpoints):
6.00
Contact hours:
46 Academic Hours
Final Examination:
Exam (Written)
PART-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
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.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Basic principles of preparing a written report. Presentations. Graphs.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Common issues, misconceptions and misuse of statistical methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Resources and software tools to communicate statistical concepts.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Case study (1): problem, data, methodology, work routine, output.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Guest lecture (1): talk and discussion with an expert from academy/industry.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Reporting statistical results. Writing statistical methods section in scientific articles.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Scientific method of research versus hypothesis generation from data. Issues related to inter-disciplinary nature of statistical consulting and ethical considerations.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Data collection methods. Data management.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Case study (2): problem, data, methodology, work routine, output.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Guest lecture (2): talk and discussion with an expert from academy/industry.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Overview of research designs.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Overview of statistical methods. Predictive vs explanatory models.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
1
|
Topics
|
Documentation of project. Content and formatting of the final report. Advanced R Markdown for writing reports.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Case study (3): problem, data, methodology, work routine, output.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Guest lecture (3): talk and discussion with an expert from academy/industry.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Graphs and interactive visualizations in R (ggplot2, plotly).
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Interactive results communication using R and R Shiny.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Auditorium
|
2
|
Topics
|
Final presentations of course projects.
|
Total ECTS (Creditpoints):
6.00
Contact hours:
33 Academic Hours
Final Examination:
Exam (Written)
Bibliography
Required Reading
1.
Cabrera, J. & McDougall, A. (2013). Statistical consulting. Springer Science & Business Media.Suitable for English stream
2.
Hand, D. J. & Everitt, B. S. (Eds.). (2007). The statistical consultant in action. Cambridge University Press.Suitable for English stream
Additional Reading
1.
Wasserman, L. (2013). All of statistics: a concise course in statistical inference. Springer Science & Business Media.Suitable for English stream
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.pdfSuitable for English stream
3.
Izenman, Alan J. (2001). Modern Multivariate Statistical Techniques. New York: Springer series in statistics.Suitable for English stream
4.
Härdle, W. & Simar, L. (2007). Applied multivariate statistical analysis. Berlin: Springer.Suitable for English stream
5.
Montgomery, D. C. (2017). Design and analysis of experiments. John Wiley & sons.Suitable for English stream
6.
Xie, Y., Allaire, J. J. & Grolemund, G. (2018). R Markdown: The definitive guide. CRC Press.Suitable for English stream