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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
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 -

Last update: Mgr. Michaela Čuprová (07.06.2020)

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.

Aim of the course -

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

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.

Literature -

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

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

Requirements to the exam -

Last update: prof. PhDr. Ladislav Krištoufek, Ph.D. (21.10.2023)

There are two components to the final score and grade:

3 Tracks in DataCamp (40 points)
4 Core Assessments in DataCamp (20 points)
2 Topical Assessments in DataCamp (40 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 email, let me know, I will send you an invite.

Tracks (upload certificates of completion to the Study Roster, separately for the completed tracks):

Skill Track "Statistics Fundamentals with R" (10 points) - by 3 December 2023 CET
Skill Track "Machine Learning Fundamentals in R" (15 points) - by 4 February 2024 CET
Skill Track "Supervised Machine Learning in R" (15 points) - by 4 February 2024 CET

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

Exploratory Analysis Theory (5 points) - by 12 November 2023 CET
Analytic Fundamentals (5 points) - by 12 November 2023 CET
Understanding and Interpreting Data (5 points) - by 12 November 2023 CET
R Programming (5 points) - by 12 November 2023 CET
You need to get at least 120 score to obtain 5 points for each of these four Core Assessments.
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).

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

Statistics Fundamentals with R (20 points) - by 4 February 2024 CET
Machine Learning Fundamentals in R (20 points) - by 4 February 2024 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 10 points, from each assessment is a necessary (not a sufficient) condition for passing the Data Science wiht R I 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).

Grading scale follows the faculty regulations:

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

Syllabus -

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

See the Teaching methods section.

Entry requirements -

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

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

Registration requirements -

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

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