Big Data Analytics (English) (Non-Thesis) | |||||
Master | Length of the Programme: 1.5 | Number of Credits: 90 | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF: Level 7 |
School/Faculty/Institute | Gradutate School of Science and Engineering | |||||||||||||||
Course Code | BDA 523 | |||||||||||||||
Course Title in English | Marketing Analytics | |||||||||||||||
Course Title in Turkish | Pazarlama Analitiği | |||||||||||||||
Language of Instruction | EN | |||||||||||||||
Type of Course | Flipped Classroom | |||||||||||||||
Level of Course | Intermediate | |||||||||||||||
Semester | Spring | |||||||||||||||
Contact Hours per Week |
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Estimated Student Workload | 179 hours per semester | |||||||||||||||
Number of Credits | 7.5 ECTS | |||||||||||||||
Grading Mode | Standard Letter Grade | |||||||||||||||
Pre-requisites | None | |||||||||||||||
Expected Prior Knowledge | Basic probability knowledge | |||||||||||||||
Co-requisites | None | |||||||||||||||
Registration Restrictions | Only Graduate Students | |||||||||||||||
Overall Educational Objective | To learn the application of basic analysis, modeling and measurement methods on fundamental marketing problems from different industries. | |||||||||||||||
Course Description | The aim of the course is to give the basic understanding of analytics problems in marketing. Statistical methods, models, and cases are employed to illustrate approaches to marketing intelligence problems, such as forecasting, price sensitivity and campaign management. | |||||||||||||||
Course Description in Turkish | Bu dersin amacı, farklı endüstrilerde ortaya çıkan analitik problemleri ile ilgili genel bilgi bir vermektir. İstatistik metotlar, modeller ve vaka analizleri ile tahminleme, fiyat hassasiyeti ve kampanya yönetimi gibi problemlerle pazarlama analitiği incelenektir. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Introduction to Marketing Analytics 2) Introduction to Marketing Analytics 3) Key Metrics in Marketing 4) Key Metrics in Marketing 5) Business Forecasting 6) Business Forecasting 7) Multi Product Association Analysis 8) Multi Product Association Analysis 9) Customer Churn and Life Time Value 10) Customer Churn and Life Time Value 11) Pricing and Revenue Management 12) Pricing and Revenue Management 13) Campaign Management and Advertising 14) Campaign Management and Advertising 15) Final Examination Period 16) Final Examination Period |
Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
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1) Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. | ||||||||||||||||
2) Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | ||||||||||||||||
3) Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | ||||||||||||||||
4) Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. | ||||||||||||||||
5) Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. | ||||||||||||||||
6) Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. | ||||||||||||||||
7) Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | ||||||||||||||||
8) Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | ||||||||||||||||
9) Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | ||||||||||||||||
10) Understanding of social and environmental aspects of machine learning applications. |
N None | S Supportive | H Highly Related |
Program Outcomes and Competences | Level | Assessed by | |
1) | Building on the skills acquired during the undergraduate degree, an improved and deepened level of expertise in the field of big data analytics related to machine learning. | S | Participation |
2) | Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | S | HW |
3) | Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | H | Project |
4) | Ability to design and perform exploratory research based on analytics, modeling and experimentation; to generate solutions to complex situations encountered in this process and to interpret the results. | H | Project |
5) | Ability to describe the analytics process and its results both verbally and in writing on national and international platforms within or outside of the field of machine learning. | H | Project |
6) | Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. | H | Project |
7) | Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | S | Participation |
8) | Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | S | Project |
9) | Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | S | Project |
10) | Understanding of social and environmental aspects of machine learning applications. | N |
Prepared by and Date | ÖZGÜR ÖZLÜK , February 2024 |
Course Coordinator | ÖZGÜR ÖZLÜK |
Semester | Spring |
Name of Instructor | Öğr. Gör. KALENDER KARAKOC |
Week | Subject |
1) | Introduction to Marketing Analytics |
2) | Introduction to Marketing Analytics |
3) | Key Metrics in Marketing |
4) | Key Metrics in Marketing |
5) | Business Forecasting |
6) | Business Forecasting |
7) | Multi Product Association Analysis |
8) | Multi Product Association Analysis |
9) | Customer Churn and Life Time Value |
10) | Customer Churn and Life Time Value |
11) | Pricing and Revenue Management |
12) | Pricing and Revenue Management |
13) | Campaign Management and Advertising |
14) | Campaign Management and Advertising |
15) | Final Project |
16) | Final Project |
Required/Recommended Readings | |||||||||||||||||||
Teaching Methods | Flipped classroom/Exercise/Laboratory/Active learning | ||||||||||||||||||
Homework and Projects | Students are required to complete a portfolio to be able to enter the final exam | ||||||||||||||||||
Laboratory Work | Students will apply the methods they learned using R and Excel at the laboratory hours | ||||||||||||||||||
Computer Use | Students will apply the methods they learned using R and Excel at the laboratory hours | ||||||||||||||||||
Other Activities | None | ||||||||||||||||||
Assessment Methods |
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Course Administration |
02123953600 |
Activity | No/Weeks | Hours | Calculation | ||||
No/Weeks per Semester | Preparing for the Activity | Spent in the Activity Itself | Completing the Activity Requirements | ||||
Course Hours | 14 | 3 | 3 | 84 | |||
Application | 4 | 4 | 16 | ||||
Project | 1 | 40 | 40 | ||||
Homework Assignments | 6 | 4 | 24 | ||||
Quiz(zes) | 5 | 3 | 0.5 | 17.5 | |||
Total Workload | 181.5 | ||||||
Total Workload/25 | 7.3 | ||||||
ECTS | 7.5 |