Course Syllabus


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Semester & Location:

Spring 2024 - DIS Stockholm

Type & Credits:

Elective Course - 3 credits

Major Disciplines:

Mathematics, Engineering


Two mathematics courses.

Faculty Members:

Maria de la Paz Celorio, Ph.D. (current students please use the Canvas Inbox)

Program Director: Natalia Landázuri Sáenz, Ph.D. 
Academic Support: 
Time & Place:

Tuesdays and Fridays 10:05-11:25

Classroom: 1C-502

Course Description

This is an introduction to statistical methods for data analysis. The course covers fundamentals of probability, experimental design, and analysis of small and large data sets. Using a hands-on approach, the course emphasizes hypothesis generation, testing, and applicability of correct statistical tools depending on the dataset. Students work with publicly-available data and also collect their own data. To conduct statistical analyses, students are introduced to the software R and its interface, R-Studio.

The course is structured as follows:

Module 1: Data summarization and hypothesis testing

  • Basic statistical concepts-measures of central tendency and dispersion
  • Probability, population and sampling distributions
  • Scientific method and hypothesis generation
  • Hypothesis testing involving one and two samples

Module 2: Experimental design and data analysis

  • Experimental design
  • Statistical model diagnostics
  • Analysis of variance and multiple comparisons
  • Correlation and linear regression
  • Categorical data analysis and goodness of fit

A word on multivariate analysis and Bayesian learning

Learning Objectives

By the end of this course, students will be able to:

- Describe experimental designs 

- Identify and propose suitable experimental designs to test given hypotheses 

- Identify and utilize appropriate statistical tests to analyze datasets and draw conclusions

- Utilize R to conduct statistical analysis utilizing real datasets

- Critically analyze the validity of chosen statistical analyses from published scientific studies


Maria de la Paz_17-Edit.jpg

Maria de la Paz CelorioPh. D. in Plant Biology from the University of California, Davis, with more than 15 years of research experience and counting. During her years as postdoctoral researcher at the Max Planck Institute of Chemical Ecology (Jena, Germany) and at Stockholm University, she contributed to the understanding of gene-expression plasticity in butterflies and genetic differentiation of populations of wild fish using genome-wide data. Has taught courses and led practical laboratories on biotechnology, statistics and population genetics for American and Swedish students. 



    • Statistics, 13th Edition, James T. McClave, Terry T Sincich, 2021, Pearson.

Field Studies

Students participate in two field studies which have the purpose of highlighting the importance of Statistics in our lives on a daily basis.  Students will generate their own datasets, conduct statistical analysis and draw conclusions.

We conduct field studies in collaboration with Karonlinska Institutet, KTH Royal Institute of Technology and Stockholm University.

Guest Lecturers


Approach to Teaching

We use various teaching methods, including interactive lectures, class discussions, workshops,  and group exercises. Students take an active role in their learning by actively engaging in discussions and group work. 

Expectations of the Students

  • Students should participate actively during lectures, discussions, group work and exercises.
  • Laptops are needed and used for note‐taking, fact‐checking, or assignments in the classroom, but only when indicated by the instructor. At all other times laptops and electronic devices should be put away during class time.
  • Reading must be done prior to the class session. 
  • Students need to be present, arrive on time and participate o receive full credit. The final grade will be affected by unexcused absences and lack of participation. The participation grade will be reduced by 10 points (over 100) for every unexcused absence. Remember to be in class on time!
  • Classroom etiquette includes being respectful of other opinions, listening to others and entering a dialogue in a constructive manner.
  • Students are expected to ask relevant questions in regards to the material covered.


To be eligible for a passing grade in this class, all of the assigned work must be completed.

Students are expected to turn in all the assignments on the due date. If an assignment is turned in after the due date, the grade of the assignment will be reduced by 10 points (over 100) for each day the submission is late.


Active participation. Includes attendance, preparation for lectures and other sessions, active participation in learning activities and class discussions 

Exams. Exams to evaluate understanding of material covered in class

Assignments:  Assignments related to field studies and to analysis of scientific publications, quizzes related to reading material

Final project: At the end of the semester, students will work on a project where they apply the knowledge acquired in class to analyze a data set generated either by them or extracted from public databases. Students will utilize R to complete this project. 

Active participation




Midterm Examination


Group project


Final Examination



Academic Regulations  

Please make sure to read the Academic Regulations on the DIS website. There you will find regulations on: 



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Course Summary:

Date Details Due