Information Package / Course Catalogue
Introduction to Data Science With R
Course Code: FEK529
Course Type: Area Elective
Couse Group: Second Cycle (Master's Degree)
Education Language: Turkish
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 5
Objectives of the Course

In this course, it is aimed to introduce the important features of data sets, basic statistical modeling and basic techniques of data visualization by introducing data science with R.

Course Content

Throughout the semester, R programming languages are taught through theoretical and applied courses and used in homework. It is aimed to teach data science techniques.

Name of Lecturer(s)
Learning Outcomes
1.Learn the basics of data science.
2.Learn the use of R programming languages in data analysis.
3.Learn basic statistical methods and machine learning techniques required for big or small data analysis.
4.Learn basic data analysis techniques (data collection, cleaning, modeling and presentation.
5.Design and run experimental tests to evaluate hypotheses about data.
Recommended or Required Reading
1.Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. (2013). An introduction to statistical learning : with applications in R. New York :Springer,
2.Rafael A. Irizarry, (2019). Introduction to Data Science: Data Analysis and Prediction Algorithms with R Chapman & Hall/CRC data science series, CRC Press
Weekly Detailed Course Contents
Week 1 - Theoretical
What is Data Science and Big Data Analytics
Week 1 - Preparation Work
Week 2 - Theoretical
Installing R and R-Studio Software, Data Types Used in R, Basic R Operations
Week 2 - Preparation Work
Week 3 - Theoretical
Basic Statistics, General Functions
Week 3 - Preparation Work
Week 4 - Theoretical
Operations with Vectors and Matrices
Week 4 - Preparation Work
Week 5 - Theoretical
Data Visualization
Week 5 - Preparation Work
Week 6 - Theoretical
Data Visualization
Week 6 - Preparation Work
Week 7 - Theoretical
Machine Learning, Regression and Classification Problems, Training, Validation and Testing Phases, Model Evaluation Criteria
Week 7 - Preparation Work
Week 8 - Preparation Work
Week 8 - Intermediate Exam
Midterm
Week 9 - Theoretical
Decision Trees
Week 9 - Preparation Work
Week 10 - Theoretical
Decision Trees
Week 10 - Preparation Work
Week 11 - Theoretical
Time Series Analysis
Week 11 - Preparation Work
Week 12 - Theoretical
Clustering, K-Means Clustering
Week 12 - Preparation Work
Week 13 - Theoretical
Artificial neural networks
Week 13 - Preparation Work
Week 14 - Theoretical
Artificial neural networks
Week 14 - Preparation Work
Week 15 - Theoretical
Project Presentations
Week 15 - Preparation Work
Week 16 - Preparation Work
Week 16 - Final Exam
Final
Week 17 - Preparation Work
Week 18 - Preparation Work
Week 19 - Preparation Work
Week 20 - Preparation Work
Week 21 - Preparation Work
Week 22 - Preparation Work
Week 23 - Preparation Work
Week 24 - Preparation Work
Week 25 - Preparation Work
Week 26 - Preparation Work
Week 27 - Preparation Work
Week 28 - Preparation Work
Week 29 - Preparation Work
Week 30 - Preparation Work
Week 31 - Preparation Work
Week 32 - Preparation Work
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%70
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142370
Individual Work73235
Midterm Examination1819
Final Examination18311
TOTAL WORKLOAD (hours)125
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
OÇ-1
5
3
3
3
3
3
3
4
4
OÇ-2
5
5
5
4
4
4
4
4
4
OÇ-3
4
4
4
4
4
3
3
3
3
OÇ-4
5
5
5
3
3
3
5
5
5
OÇ-5
3
3
3
4
4
4
4
4
4
Adnan Menderes University - Information Package / Course Catalogue
2026