Statistical inference is the process of deducing properties of an underlying probability distribution (model/theory) by analysis of data. What does it mean when we quantify new measurements, claim findings like discoveries or exclusions of new phenomena? Extracting knowledge from data is about probability and uncertainties. Basic statistical inference knowledge and skills are essential for all professionals dealing with data analysis and interpretation.
This one day course serves students, researchers and professionals who wish to refresh and deepen their statistical inference skills with a modern peer level approach. Course material is offered as Jupyter notebooks. The course is part of the block Data Science Funadamentals 1 and 2, but can be attended indiviually.
Note for CAS Applied Data Science Participants: This course does not provide anything beyond the content of Module 2 (however, may be a nice repitition)
- Understand probability and know typical distributions
- Understand the meaning of common characteristics and uncertainties (standard deviations, sigmas, p-values, confidence levels, ...)
- Give and correctly interprete statistical results
- Apply linear regression for parameter estimation
- Apply statistical tests
- Students, researchers and professionals working with data
- Equivalent to Data Science Fundamentals 1, 2 and 3.
- Basic programming skills are necessary, we don't reserve time for basic programming concepts.
- Course languages are Python and English
- Short theory sessions followed by hands-on tutorials with Jupyter notebooks
- Certificate of attendance.
- Participants attending DSF 4-5 are offered to make a project (30 hours). Upon a successful presentation 2 ECTS credit points are given.
- The coaches are local and external experts
Time : 2020.06.11 from 09:00 to 17:00
Location : Online and ...
Online Room:
- Upload and run them on google colab (google account needed)
- Upload and run them from noto.epfl.ch (AAI/campus account needed)
- Run them locally (jupyter needs to be installed)
For remote attendance, it is very helpful to have two screens, one for Zoom and one for your notebook.
Training language: English
Participants : Max 24
Registration : Mandatory (use the Join button, one has to be logged into Ilias, button in the upper right corner)
Coaches : PD Dr. Sigve Haug (responsible)
Fee: Free of charge
Certificate: Certificate of Attendance and part of Data Science Fundamentals (2+2 ETCS)
09:00 Welcome
09:10 Probability Density Distributions, Moments
10:30 Break
11:00 p-Values, Confidence intervalls
12:30 Lunch
13:30 Parameter Estimation
15:00 Break
15:30 Hypothesis testing
17:00 End
- scits.unibe.ch