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Data Analysis in R - JEB157

Title:	Data Analysis in R
Czech title:	Data Analysis in R
Guaranteed by:	Institute of Economic Studies (23-IES)
Faculty:	Faculty of Social Sciences
Actual:	from 2022
Semester:	winter
E-Credits:	6
Examination process:	winter s.:combined
Hours per week, examination:	winter s.:2/2, Ex [HT]
Capacity:	unlimited / unlimited (unknown)
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:	enabled for web enrollment

Guarantor:	prof. PhDr. Ladislav Krištoufek, Ph.D.
Teacher(s):	Anna Drahozalová prof. PhDr. Ladislav Krištoufek, Ph.D.
Class:	Courses for incoming students
Pre-requisite :	JEB142

Examination dates WS schedule Noticeboard

Annotation -

Last update: prof. PhDr. Ladislav Krištoufek, Ph.D. (19.09.2022)

An introductory course to data analysis in R. The course covers the basics of practical programming in the R environment, including data structures, data manipulation, graphs, graphical outputs, basic statistics, variance analysis, test powers, bootstrapping, and component and factor analysis.

Aim of the course -

Last update: prof. PhDr. Ladislav Krištoufek, Ph.D. (19.09.2022)

The aim of Data Analysis in R is to lay foundations for analytical work with data complememtary to the other compulsory courses, namely Introductory Statistics, Statistics, and Econometrics I + II.

Literature -

Last update: prof. PhDr. Ladislav Krištoufek, Ph.D. (19.09.2022)

Kabacoff, Robert I. (2015): R in Action (2E), Manning Publications, Shelter Island, NY

Teaching methods -

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

Combination of online (pre-recorded) lectures and in-person group consultations. The group consultations will take place in room 016 on 27 Oct (Week 4), 10 Nov (Week 6), 24 Nov (Week 8), 8 Dec (Week 10), and 22 Dec (Week 12) between 11:00 and 12:30., and they will be led by Anna Drahozalova, the TA of the course. Please direct your questions for consultations to her, ideally some time ahead.

Software: R a RStudio (available on all computers in room 016), available here a here (freeware).

Requirements to the exam -

Last update: prof. PhDr. Ladislav Krištoufek, Ph.D. (02.10.2023)

There are two components to the final score and grade:

3 tracks in DataCamp (35 points)
4 assessments in DataCamp (65 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):

Track 1: Skill Track "R Programming" (7.5 points) - by 19 November 2023 CEST
Track 2: Skill Track "Importing & Cleaning Data" (7.5 points) - by 10 December 2023 CET
Track 3: Career Track "Data Analyst with R" (20 points) - by 4 February 2024 CET

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

Assessment 0: Understanding and Interpreting Data (5 points) - by 5 November CEST
Assessment 1: R Programming (20 points) - by 19 November 2023 CEST
Assessment 2: Importing & Cleaning Data with R (20 points) - by 10 December 2023 CET
Assessment 3: Data Manipulation with R (20 points) - by 4 February 2024 CET
Scoring:

For Assessment 0, you need to get at least 120 score in DataCamp to pass and obtain 5 points.
For Assessments 1-3:

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 Analysis in R course.

You can re-take the assessments twice a week during the whole semester. 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. (26.12.2023)

BLOCK I - GETTING STARTED:

Introduction to R and RStudio (Course info, R basics)
Creating a dataset (Intro, Data structures, Data input)
Basic data management (Basic data management)
Advanced data management (Math & Stats functions, Data-management challenge, Control-flow and others)
Getting started with graphs (ggplot basics, ggplot details)

BLOCK II - BASIC METHODS:

Basic graphs (Bar charts & tree maps, Histograms, kernels, box plots)
Basic statistics (Descriptive statistics, Contingency tables, Correlations, Parametric and non-parametric tests)

BLOCK III - INTERMEDIATE METHODS:

Regression basics (Types of regression, Picking the "best" model, Cross-validation)
Analysis of variance (Terminology, One-way ANOVA, Other ANOVA types)
Intermediate graphs (Scatter plots, Line charts, correlograms, mosaic plots)