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A220A0010 Free Analytics Environment R, 6 cr 
Code A220A0010  Validity 01.08.2018 -
Name Free Analytics Environment R  Abbreviation Free Analytics 
Credits6 cr   
TypeBasic studies  
ClassCourse   
  Grading scaleStudy modules 0-5,P/F 
  Eligibility for post-graduate studiesno
    Allowed to study several timesno
Organisation LUT School of Business and Management 

Teachers
Name
Christoph Lohrmann 
Pasi Luukka 

Description by Study Guide
Note 

Location: Lappeenranta

 
Year 

M.Sc. (Econ & Bus. Adm.) 1

 
Period 

1

 
Teaching Language 

English

 
Teacher(s) in Charge 

Junior Researcher, M.Sc. (Econ.) Christoph Lohrmann, Professor, D.Sc. (Tech.) Pasi Luukka.

 
Aims 

In this course the students will be introduced to the statistical programming environment R with the main focus on learning how to utilize it in typical business analytics problems. By the end of the course, students will be able to:
- use RStudio and perform exploratory data analysis on datasets
- identify and use different pre-processing steps and types of visualizations in R
- identify different types of problems (Regression, Classification and Clustering)
- construct their own linear regression, classification and clustering models in R
- evaluate models based on certain metrics (such as R2 and F-statistic, the classification accuracy, sensitivity, specificity, etc).

 
Contents 

The course will incorporate knowledge for 6 distinct areas: (1) Introduction to R and using analytics in R, (2) Visualization of data (theory, base graphics, ggplot2), (3) Data pre-processing, (4) Forecasting (including linear regression), (5) Classification models such as decision trees, k-nearest-neighbor classifier and neural networks, and (6) Clustering with hierarchical and heuristic methods (with a focus on k-means clustering).
Exercise classes accompany the lecture, in which the students will be introduced to selected examples for which they will perform programming tasks together with the lecturer in class that are related to the content of each week's lecture.

 
Teaching Methods 

Lectures 12 h, exercises 12 h. Online course at the start: 4 h, weekly online quizzes 4 h, independent study 64 h, two practical assignments 60 h. Total work load 156 h.

 
Examination in Examination schedule (Yes/No) 

No

 
Examination in Moodle (Yes/No) 

No

 
Examination in Exam (Yes/No) 

No

 
Assessment scale and assessment methods 

Completion of introductory R course online (obligatory)
Weekly Online Quizzes: 20%
First Assignment: 40%
Second Assignment: 40%
Grading 0-5

 
Course Materials 

Attending the lectures and computer room sessions and studying the material in Moodle is sufficient for completing the course. There is a wide variety of (free) resources available, here are some:

- An introduction to R (https://cran.r-project.org/doc/manuals/R-intro.pdf)
- Robert Kabacoff (2011), R in Action.Data abakysis and graphics with R, 1st editon, Manning Publications
- The website http://www.statmethods.net/ provides an introduction to R used for statistical data analysis through several examples
- Online Courses (e.g edx, DataCamp).

 
Prerequisites 

None. Basic knowledge in analytics and statistics is helpful but not required.

 
Limitation for students? (Yes, number, priorities/Leave empty) 

Yes, 80. Preference will be given to students of the Master’s Programme in Business Analytics and of the MSF programme.

 
Places for exchange-students? (Yes, number/No) 

No

 
Places for Open University Students?(Yes, number/No) 

No

 


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