COP4434 IBM Big Data and AnalyticsBahçeşehir UniversityDegree Programs MATHEMATICSGeneral Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
MATHEMATICS
Bachelor TR-NQF-HE: Level 6 QF-EHEA: First Cycle EQF-LLL: Level 6

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
COP4434 IBM Big Data and Analytics Fall 3 0 3 6
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction: English
Type of course: Non-Departmental Elective
Course Level: Bachelor’s Degree (First Cycle)
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
Course Lecturer(s): Prof. Dr. TAŞKIN KOÇAK
Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
Recommended Optional Program Components: None
Course Objectives: The students will take lectures from senior executives from IBM. Each lecture will focus on a different subject and the lecturer will share his/her own experiences together with the theoretical basis of the subject.
The courses will include Business Analytics & Big Data capabilities and service areas including key concepts, services, IBM software, hardware offerings and IBM assets. In addition, industry use cases are used to illustrate effective use of Big Data services. The courses will help students to prepare for a successful professional career.

Learning Outcomes

The students who have succeeded in this course;
Expected benefits are multidimensional such as:
- Graduating engineers being much more ready for the professional work
- Directing academic research (including thesis) to real life problems and business needs
- Creating new industry projects formed as the application of new technologies
Hence, this course will be another good addition to our activities in industry-academia partnership.

Course Content

The course will include Big Data and analytics capabilities and service areas including key concepts, services, IBM software, hardware offerings and IBM assets. In addition, industry use cases are used to illustrate effective use of Big Data services.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Big Data & Analytics for Better Business Outcomes
2) Industry Aligned Big Data, Top Use Cases
3) Overview of Big Data Technology & IBM Big Data Platform
4) IBM Big Data Platform, Data Explorer
5) Data Warehousing
6) Information Integration, Master Data Management, Guardium, OPTIM
7) Hadoop Technology
8) Midterm
9) Master Data Management for Customer
10) Integrating Unstructured Data in the Enterprise
11) Text Analytics
12) Infrastructure for Big Data & Analytics
13) Infrastructure for Big Data & Analytics
14) Recap

Sources

Course Notes / Textbooks: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by E. Siegel
References:

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Quizzes 2 % 5
Project 1 % 25
Midterms 1 % 30
Final 1 % 40
Total % 100
PERCENTAGE OF SEMESTER WORK % 35
PERCENTAGE OF FINAL WORK % 65
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Project 1 20 20
Quizzes 2 14 28
Midterms 1 25 25
Final 1 30 30
Total Workload 145

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution
1) To have a grasp of basic mathematics, applied mathematics and theories and applications in Mathematics
2) To be able to understand and assess mathematical proofs and construct appropriate proofs of their own and also define and analyze problems and to find solutions based on scientific methods,
3) To be able to apply mathematics in real life with interdisciplinary approach and to discover their potentials,
4) To be able to acquire necessary information and to make modeling in any field that mathematics is used and to improve herself/himself, 4
5) To be able to tell theoretical and technical information easily to both experts in detail and non-experts in basic and comprehensible way,
6) To be familiar with computer programs used in the fields of mathematics and to be able to use at least one of them effectively at the European Computer Driving Licence Advanced Level,
7) To be able to behave in accordance with social, scientific and ethical values in each step of the projects involved and to be able to introduce and apply projects in terms of civic engagement,
8) To be able to evaluate all processes effectively and to have enough awareness about quality management by being conscious and having intellectual background in the universal sense, 4
9) By having a way of abstract thinking, to be able to connect concrete events and to transfer solutions, to be able to design experiments, collect data, and analyze results by scientific methods and to interfere,
10) To be able to continue lifelong learning by renewing the knowledge, the abilities and the competencies which have been developed during the program, and being conscious about lifelong learning,
11) To be able to adapt and transfer the knowledge gained in the areas of mathematics ; such as algebra, analysis, number theory, mathematical logic, geometry and topology to the level of secondary school,
12) To be able to conduct a research either as an individual or as a team member, and to be effective in each related step of the project, to take role in the decision process, to plan and manage the project by using time effectively.