Course Syllabus

Statistics

Semester & Location:	Fall 2024 - DIS Stockholm
Type & Credits:	Elective Course - 3 credits
Major Disciplines:	Mathematics, Engineering
Prerequisites:	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.
Program Contact:	csc-engr@disstockholm.se
Time & Place:	Tuesdays and Fridays 10:05-11:25 Classroom: 1C-505

Course Description

This course focuses on statistical methods for data analysis. It 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 throughout the course, students utilize the software R and its interface, R-Studio.

The course is structured as follows:

Module 1: Data summarization, probability distributions and hypothesis testing

• Statistical notation, data summarization and visualization
• Probability: additive and multiplicative rule
• Population distributions: Poisson, binomial, normal
• Central limit theorem
• Sampling distributions: student's T, F distribution, chi-square distribution
• Scientific method, hypothesis generation, significance level, power of the test
• Calculation of confidence intervals around measures of central tendency and dispersion
• Hypothesis testing involving one and two samples (independent and dependent)

Module 2: Experimental design and data analysis

• Experimental design: randomization, pseudo-replication, blocking design
• Data transformation
• Statistical model diagnostics
• Parametric vs. non-parametric statistical tests
• Proportion tests
• Analysis of variance and multiple comparisons: one-way, two-way, factorial
• Correlation and linear regression
• Categorical data analysis and goodness of fit
• Multivariate analysis with an example on gene expression data (transcriptomics)
• Introduction to 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

Faculty

Maria de la Paz Celorio. Ph. 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. 

Readings

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

TBA

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.

Evaluation

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.

Grading

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	10%
Quiz	15%
Midterm Examination	35%
Group project	15%
Final Examination	25%

 

Academic Regulations  

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

• Course Enrollment and Grading  
• Academic Expectations and Honor Code 

 

 

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