Syllabus (2024-2025)
Classroom:
see Solus or OnQ
Day, time:
see Solus or OnQ
Instructors:
Gunnar Blohm,
Joe Nashed
Tutorial format, Python based. We will use
Google Colab.
Recommended pre-requisite for this course is basic knowledge of
Python - see
this course.
All
teaching materials are available on the Blohm lab Github page.
- Week 1: Introduction (Gunnar)
The research process
Statistics and models in scientific discovery (Pearl)
Study design (power, sample size, effect size)
- Week 2: Intro Python (Joe)
Google Colab interface
Basic syntax and commands
Importing and manipulating data
Graphics
- Week 3: Advanced Python (Joe)
Vectors and Matrices
Functions
- Week 4: Data collection / signal processing (Joe)
Data types
Sampling
DAQ
Filtering (noise, differentiation, integration)
Time vs frequency analysis
- Week 5: Statistics and Hypothesis testing - basics
(Joe)
Descriptors: central tendencies (mean, median,
mode), Spread (Range, variance, percentiles), Shape (skew,
kurtosis)
Correlation / regression
The logic of hypothesis testing
Statistical significance
Multiple comparisons
Different test statistics
Confidence intervals and bootstrap
- Week 6: Statistics and Hypothesis testing - advanced
(Joe)
ANOVA (between-subject, factorial,
within-subject/repeated measures)
Measuring effect size
Multiple regression
Non-parametric tests
- Week 7: Quantitative wet lab / bench methods (Joe)
Image processing
- Week 8: Statistics and Hypothesis testing - Bayesian
(Gunnar)
Motivation and pitfalls of classic methods
Conditional probabilities and Bayes rule
Bayes Factor
Maximum A Posteriori (MAP) estimation
Bayesian ANOVA
- Week 9: Models in Neuroscience (Gunnar)
Models in scientific discovery (Pearl)
Usefulness of models
Parameter search (Newton) and model fitting methods
- Week 10: Data Neuroscience overview (Gunnar)
Promises and limitations (Pearl)
Data organization (format, DB)
Blind data processing: machine learning techniques
(classification, dimensionality reduction, decoding)
- Week 11: Correlation vs causality (Gunnar)
What’s causality?
How to achieve causality
Problem of unobserved variables in high-dimensional problems
- Week 12: Reproducibility, reliability, validity
(Gunnar)
Statistical considerations (multiple comparisons,
exploratory analysis, hypothesis testing)
Open Science methods
Open science vs patents (required for drug discovery)
Course evaluation
(Virtual) pre-registration of research plan (i.e. stage 1 of
registered report). Note, an actual pre-registration is not
required (but encouraged); rather it is about generating the
pre-registration materials, i.e. title and authors, proposal
summary (abstract), literature review and justified hypotheses
(introduction section), experimental approach and analysis plan
including statistics (Methods).
Preliminary submission (for round of formative feedback,
optional): April 4, 2025
Final due date: April 25, 2025
Follow
Stage
1 instructions of registered reports!
See
Center for Open Science
for more info.
Additional guidelines:
- 10
steps how to structure papers
- please remember to motivate / justify your hypotheses
- if you have multiple hypotheses, please number them in the
introduction and then address how you will test each of the
hypotheses in the methods section
- the methods need to describe specifically and explicitly
how each hypothesis will be addressed
- you do NOT need to submit a cover letter
- general section lengths: see eNeuro
guide for authors (include title, abstract,
significance statement, Introduction, and Methods)
Further
readings:
Check out Statistics
Learning Resources on the Blohm
Lab WIKI.
