- Course Code
- TKM308
- Semester
- Sixth Term (Spring)
- Theory+Practice
- 3 + 0
- ECTS
- 5
| Course Type | Compulsory Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | 3 |
|---|
| Semester | Sixth Term (Spring) |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- They will see the general use of R programming language, functions, conditions, loop structures in R, probability calculation in R, and probability distributions.
- They will see the use of some functional libraries and packages in R, data visualization and statistical inference.
- They will learn theoretically and practically some methods commonly used in Data Science.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | |
|---|
| Course Description | This course will emphasize practical techniques for working with large-scale date. Specific topics covered will include statistical modeling and machine learning, data pipelines, programming languages and real world topics and case studies. The use of R programming language will be used. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to R Programming Language, |
|
2nd Week |
Matrix, Factor, List and Data Frames |
|
3rd Week |
Data Entry in R, Function, Loop, Condition Structures in R |
|
4th Week |
Probability and Probability Distributions in R |
|
5th Week |
Data Visualization in R |
|
6th Week |
Descriptive Statistics |
|
7th Week |
Overview of Mid-Term |
|
8th Week |
Mid-Term Exam |
|
9th Week |
Regression and Correlation and R Applications |
|
10th Week |
Time Series in R |
|
11th Week |
Cluster Analysis and R Applications |
|
12th Week |
Discriminant Analysis and R Applications |
|
13th Week |
Applications |
|
14th Week |
Overview of the term |
|
| Recommended or Required Reading | "Univariate, Bivariate, and Multivariate Statistics Using R", Daniel J. Denis, Wiley, 2020.
"İstatistikte R ile Programlama", Necmi Gürsakal, Dora, 2018.
"Introduction to Data Science", Laura Igual, Santi Segui, Springer, 2017.
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Discussion
- Case Study
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Project |
1 |
30 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
3 |
42 |
| Preparation for Final exam |
1 |
25 |
25 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
25 |
25 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Project |
1 |
12 |
12 |
| Total Work Load | 150 |
| Total Work Load / 30 (h) | 5 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
Programme Qualifications / Learning Outcomes
|
LO1 |
LO2 |
LO3 |
| K1 |
X |
|
|
| K2 |
X |
|
|
| K3 |
X |
X |
|
| K4 |
X |
|
|
| K5 |
|
X |
|
| K6 |
X |
|
|
| K7 |
X |
X |
|
| K8 |
|
X |
|
| K9 |
X |
|
|
| K10 |
X |
|
|
| K11 |
X |
|
|
| K12 |
X |
X |
|
| K13 |
X |
|
X |
| K14 |
|
|
X |
| K15 |
|
|
X |
