| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Define critical decision problems for businesses.
- Learn analytical tools such as data processing, data visualization, forecasting, and optimization required to analyze and solve business problems.
- Organize data, create tables and graphs using Excel.
- Develop forecasting models using Excel.
- Will be able to solve optimization problems using Excel at an advanced level.
- Will be able to evaluate how companies can improve increasingly complex decision-making processes and enhance business performance.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Business analytics is the process of converting data into meaningful information through analysis and forecasting methods for corporate decision-making and problem-solving. This course involves processes that enable decision-makers to make data-driven and analytical decisions using information technologies, statistical analysis, and mathematical models. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction to business analytics |
|
2nd Week |
Descriptive analytics: visualizing and exploring data |
|
3rd Week |
Descriptive analytics: descriptive statistical measures |
|
4th Week |
Descriptive analytics: sampling and estimation |
|
5th Week |
Applications of descriptive analytics with Excel |
|
6th Week |
Simple linear regression analysis |
|
7th Week |
Multiple linear regression analysis |
|
8th Week |
Midterm exam |
|
9th Week |
Applications of predictive analytics with Excel |
|
10th Week |
Prescriptive analytics: linear optimization |
|
11th Week |
Applications of linear optimization in predictive analytics |
|
12th Week |
Integer optimization in predictive analytics |
|
13th Week |
Applications of optimization with Excel |
|
14th Week |
Problem solving |
|
| Recommended or Required Reading | Evans, J. (2016). Business analytics: Methods, models, and decisions (2nd ed.). Pearson/Prentice-Hall. |
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
40 |
| Total(%) |
|
40 |
| Contribution of In-term Studies to Overall Grade(%) |
|
40 |
| Contribution of Final Examination to Overall Grade(%) |
|
60 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1 |
1 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
13 |
2 |
26 |
| Preparation for Final exam |
1 |
30 |
30 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
15 |
15 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
1 |
1 |
| Homework |
|
|
|
| Project |
1 |
30 |
30 |
| Total Work Load | 145 |
| Total Work Load / 30 (h) | 4.83 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
