Poslední úprava: prof. PhDr. Ladislav Krištoufek, Ph.D. (23.09.2016)
Introductory course to Data Science with applications in the R programming environment. Special focus is put on data visualization, data & text mining, and machine learning methods.
Cíl předmětu - angličtina
Poslední úprava: prof. PhDr. Ladislav Krištoufek, Ph.D. (23.09.2016)
The main aim of the course is to train students to be able to properly analyze specific datasets with methods outside of standard econometric framework using the R programming environment.
Literatura - angličtina
Poslední úprava: prof. PhDr. Ladislav Krištoufek, Ph.D. (25.09.2017)
Mandatory literature:
Toomey, Dan (2014): R for Data Science, Packt Publishing Ltd., Birmingham, UK
Zumel, Nina & Mount, John (2014): Practical Data Science with R, Manning Publications Co., Shelter Island, NY, USA
Additional suggested literature:
Grolemung, Garret (2014): Hands-On Programming with R, O'Reilly Media Inc., Sebastopol, CA, USA
Ojeda, Tony et al. (2014): Practical Data Science Cookbook, Packt Publishing Ltd., Birmingham, UK
Metody výuky - angličtina
Poslední úprava: prof. PhDr. Ladislav Krištoufek, Ph.D. (01.10.2018)
Lectures + Seminars (2 parallel classes, Tuesdays and Wednesdays):
Group 1: Tuesdays 9:30 - 12:20 (room 016) with a break of 10 minutes
Group 2: Wednesdays: 9:30 - 12:20 (room 016) with a break of 10 minutes
Software: R and RStudio (available on all computers in room 016)
Požadavky ke zkoušce - angličtina
Poslední úprava: prof. PhDr. Ladislav Krištoufek, Ph.D. (10.10.2018)
The final grade consists of three ingredients:
DataCamp assignments: 25 (5*5)
Active participation during lectures and seminars: 10
Final project: 35
Final test: 30
Grading scale (according to Dean's Provision 17/2018):
A: above 90 (not inclusive)
B: between 80 (not inclusive) and 90 (inclusive)
C: between 70 (not inclusive) and 80 (inclusive)
D: between 60 (not inclusive) and 70 (inclusive)
E: between 50 (not inclusive) and 60 (inclusive)
F: below 50 (inclusive)
DataCamp.com assignments:
Assignment #1 - by the end of Week #4:
Introduction to R
Assignment #2 - by the end of Week #7:
Intro to Exploratory Data Analysis (optional)
Training and Evaluating of Regression Models
Issues to Consider
Tree-based Methods
Assignment #3 - by the end of Week #10:
Introduction to Machine Learning
Assignment #4 - by the end of Week #12:
Text Mining: Bag of Words
Assignment #5 - by the end of Week #12:
Unsupervised Learning in R
Sylabus - angličtina
Poslední úprava: prof. PhDr. Ladislav Krištoufek, Ph.D. (01.10.2018)
Week #1-#2: Course information + R basics (ZM 1, G 3-5)
Week #11: Machine learning techniques (ZM 9, T 10-12)
Week #12: aLook Analytics presentation
Vstupní požadavky - angličtina
Poslední úprava: prof. PhDr. Ladislav Krištoufek, Ph.D. (26.09.2016)
There are no formal course requirements. However, knowledge up to the level of Statisics (JEB105) and Econometrics I (JEB109) courses is assumed and expected.
Požadavky k zápisu - angličtina
Poslední úprava: prof. PhDr. Ladislav Krištoufek, Ph.D. (26.09.2016)
There are no formal course requirements. However, knowledge up to the level of Statisics (JEB105) and Econometrics I (JEB109) courses is assumed and expected.