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Data Science with R I - JEM227

Title:	Data Science with R I
Czech title:	Data Science with R I
Guaranteed by:	Institute of Economic Studies (23-IES)
Faculty:	Faculty of Social Sciences
Actual:	from 2020
Semester:	winter
E-Credits:	6
Examination process:	winter s.:combined
Hours per week, examination:	winter s.:2/0, Ex [HT]
Capacity:	unlimited / unknown (200)
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	English
Teaching methods:	full-time
Note:	course can be enrolled in outside the study plan enabled for web enrollment

Guarantor:	prof. PhDr. Ladislav Krištoufek, Ph.D.
Teacher(s):	prof. PhDr. Ladislav Krištoufek, Ph.D. Mgr. Ivan Trubelík
Class:	Courses for incoming students
Incompatibility :	JEM181, JEM221
Is incompatible with:	JEM221
In complex pre-requisite:	JEM220

Examination dates WS schedule Noticeboard

Annotation -

Introductory course to Data Science with applications in the R programming environment. Special focus is put on understanding of basic practical programming in R, covering model evaluation, memorization methods, advanced regression techniques, and training variance reduction. The Data Science with R I course will be followed by Data Science with R II covering clustering, text mining, support vector machines, neural networks, and networks.

Last update: Čuprová Michaela, Mgr. (07.06.2020)

Aim of the course -

The main aim of the set of courses (Data Science with R I + II) is to train students to be able to properly analyze specific datasets with methods outside of standard econometric framework using the R programming environment.

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (10.09.2019)

Literature -

Mandatory literature:

Ledolter, Johannes (2013): Data Mining and Business Analytics with R, John Wiley & Sons, Hoboken, New Jersey, NJ, USA
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

Last update: Bednařík Petr, PhDr., Ph.D. (05.06.2020)

Requirements to the exam -

There are 4 components to the final score and grade:

3 Core Assessments in DataCamp (3*5 = 15 points)
3 Courses in DataCamp (3*10 = 30 points)
1 Topical Assessments in DataCamp (25 points)
1 Research Report (30 points)

Use this LINK to register to DataCamp, fill in the profile (properly, use your name, it will be used to track fulfillment of assignments), and complete your assignments there. If you do not have a @fsv.cuni.cz/@cuni.cz/@m365.cuni.cz email, let me know, I will send you an invite.

Core Assessments (upload a printscreen of your finished assessments to the Study Roster, make sure you name is visible in the printscreen):

R Programming (5 points) - by 20 October 2024 CET
Exploratory Analysis Theory (5 points) - by 20 October 2024 CET
Analytic Fundamentals (5 points) - by 20 October 2024 CET
You need to get at least 120 score to obtain 5 points for each of these three Core Assessments (MANDATORY).
You can re-take the assessments twice a week up till the deadline. Remember that the last one counts (not necessarily the best one).

Courses (upload certificates or screenshots of completion to the Study Roster, separately for the completed courses):

Supervised Learning in R: Classification (10 points) - by 24 November 2024 CET
Supervised Learning in R: Regression (10 points) - by 8 December 2024 CET
Machine Learning with Tree-Based Models in R (10 points) - by 22 December 2024 CET

Topical Assessment (upload a printscreen of your finished assessments to the Study Roster, make sure you name is visible in the printscreen):

Machine Learning Fundamentals in R (25 points) - by 26 January 2025 CET
To get the score, use the DataCamp score x and fit it to (x-60)/80*100%
At least 50%, i.e. at least 12.5 points, is a necessary (not a sufficient) condition for passing the course.
You can re-take the assessments twice a week during the whole semester (up till the deadline). Remember that the last one counts (not necessarily the best one).

Research Report (upload a zip file including the report, R code, and dataset, to the Study Roster):

Teams of up to 4 students.
Up to 10 pages (including everything but the code and data which will form separate attachments).
Submit by 2 February 2025 CET

Grading scale follows the faculty regulations:

A: 90+
B: 80-90
C: 70-80
D: 60-70
E: 50-60
F: below 50

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (04.10.2024)

Syllabus -

See the Teaching methods section.

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (05.10.2023)

Entry requirements -

There are no formal course requirements. However, knowledge up to the level of Statisics (JEB105), Econometrics I (JEB109), and Data Analysis in R (JEB157) courses is assumed and expected.

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (03.10.2024)

Registration requirements -

There are no formal course requirements. However, knowledge up to the level of Statisics (JEB105), Econometrics I (JEB109), and Data Analysis in R (JEB157) courses is assumed and expected.

Last update: Krištoufek Ladislav, prof. PhDr., Ph.D. (03.10.2024)