Information Package / Course Catalogue
| 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 |
Ders Genel Tanıtım Bilgileri
| School/Faculty/Institute | Gradutate School of Science and Engineering | ||||
| Course Code | BDA 564 | ||||
| Course Title in English | End-To-End Big Data Analytics | ||||
| Course Title in Turkish | Uçtan Uca Büyük Veri Analitiği | ||||
| Language of Instruction | EN | ||||
| Type of Course | Flipped Classroom | ||||
| Level of Course | Select | ||||
| Semester | Summer School | ||||
| Contact Hours per Week |
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| Estimated Student Workload | 172 hours per semester | ||||
| Number of Credits | 10 ECTS | ||||
| Grading Mode | Standard Letter Grade | ||||
| Pre-requisites | None | ||||
| Expected Prior Knowledge | None | ||||
| Co-requisites | None | ||||
| Registration Restrictions | Only Graduate Students | ||||
| Overall Educational Objective | To learn and apply the fundamentals and techniques of end-to-end big data analytics with hands-on applications. | ||||
| Course Description | This course comprehensively addresses the fundamental concepts, methods, and applications of big data analytics. It thoroughly examines all stages of the process, from data collection and processing to analysis and interpretation of results. Students will gain the ability to extract valuable information from large data sets by learning data mining, machine learning, artificial intelligence, and statistical analysis techniques. | ||||
| Course Description in Turkish | Bu yüksek lisans dersi, büyük veri analitiğinin temel kavramlarını, yöntemlerini ve uygulamalarını kapsamlı bir şekilde ele alır. Kurs, veri toplamadan işlemeye, analizden sonuçların yorumlanmasına kadar olan süreçlerin tüm aşamalarını detaylı bir şekilde inceler. Öğrenciler, veri madenciliği, makine öğrenimi, yapay zeka ve istatistiksel analiz tekniklerini öğrenerek büyük veri setlerinden değerli bilgiler çıkarma becerisini kazanacaklardır. |
Course Learning Outcomes and CompetencesUpon successful completion of the course, the learner is expected to be able to:1) Develop a strong proficiency in analyzing large datasets, utilizing statistical methods and machine learning algorithms to extract meaningful insights. 2) Gain hands-on experience with industry-standard big data tools and platforms, enabling the practical application of theories learned in class. 3) Enhance critical thinking abilities by engaging in complex problem-solving scenarios related to big data challenges, fostering the ability to develop innovative solutions. 4) Acquire skills in data visualization, learning to effectively communicate complex analytical results to both technical and non-technical audiences. 5) Develop an understanding of the ethical and legal considerations in big data analytics, including data privacy and security, essential for responsible practice in the field. |
| Program Learning Outcomes/Course Learning Outcomes | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 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. |
Relation to Program Outcomes and Competences
| 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. | H | |
| 2) | Applied in-depth theoretical and practical knowledge in the fields of statistics, computation and computer science related to machine learning. | H | |
| 3) | Extensive knowledge about the analysis and modeling methods used in machine learning and their limitations. | H | |
| 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 | |
| 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. | S | |
| 6) | Awareness of social, scientific and ethical values regarding the machine learning, processing, usage, interpretation and dissemination stages and in all related professional activities. | S | |
| 7) | Professional awareness new and emerging applications in the machine learning field and an ability to demonstrate their uses. | S | |
| 8) | Competence to act as a leader in multi-disciplinary teams, to develop big data-driven solutions to complex situations; to take responsibility. | H | |
| 9) | Ability to communicate in English both verbally and in writing at European Language Portfolio General Level B2. | S | |
| 10) | Understanding of social and environmental aspects of machine learning applications. | S |
| Prepared by and Date | , |
| Course Coordinator | ÖZGÜR ÖZLÜK |
| Semester | Summer School |
| Name of Instructor | Asst. Prof. Dr. TUNA ÇAKAR |
Course Contents
| Week | Subject |
| 1) | ● Overview of Big Data concepts and the importance in the modern world. ● Discussion of key challenges and opportunities in Big Data. |
| 2) | ● Techniques for collecting big data from various sources. ● Data cleaning, transformation, and preprocessing methods. |
| 3) | ● Understanding different data storage solutions (SQL, NoSQL). ● Principles of data warehousing and database management. |
| 4) | ● Fundamentals of statistical analysis in data analytics. ● Introduction to statistical software and tools. |
| 5) | ● Basic concepts and types of machine learning: supervised and unsupervised learning. ● Simple machine learning algorithms and their applications. |
| 6) | ● Exploring advanced machine learning models. ● Application of machine learning in big data scenarios. |
| 7) | ● Role of AI in big data. ● AI techniques for pattern recognition and predictive modeling. |
| 8) | ● Introduction to big data technologies like Hadoop, Spark, etc. ● Hands-on exercises with big data tools. |
| 9) | ● Principles of effective data visualization. ● Use of tools like Tableau, PowerBI for visualizing big data. |
| 10) | ● Techniques for real-time data processing. ● Stream processing frameworks and their applications. |
| 11) | ● Integration of cloud computing with big data analytics. ● Exploring cloud platforms for data analytics. |
| 12) | ● Data privacy, security, and ethical implications in big data. ● Understanding legal frameworks and compliance issues. |
| 13) | ● Case studies from different industries like healthcare, finance, and retail. ● Discussion on how big data analytics is transforming businesses. |
| 14) | ● Review of key concepts and techniques learned. ● Discussion on emerging trends and the future of big data analytics. |
| 15) | Final Examination Period |
| 16) | Final Examination Period |
| Required/Recommended Readings | None | ||||||||||||
| Teaching Methods | Flipped classroom/Exercise/Laboratory/Active learning | ||||||||||||
| Homework and Projects | Students will be given 5 assignments: one assignment each week from week #2 to week #7. Each assignment will include numerical applications of the methods or models that will be taught in class. Students will have one week to submit an assignment. | ||||||||||||
| Laboratory Work | Students will apply the methods they learned using a statistical computation program. | ||||||||||||
| Computer Use | Students will apply the methods they learned using a statistical computation program. | ||||||||||||
| Other Activities | None | ||||||||||||
| Assessment Methods |
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| Course Administration |
cakart@mef.edu.tr 02123953600 |
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ECTS Student Workload Estimation
| 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 | 2 | 1.5 | 1 | 63 | ||
| Laboratory | 14 | 2 | 1.5 | 1 | 63 | ||
| Homework Assignments | 5 | 12 | 60 | ||||
| Total Workload | 186 | ||||||
| Total Workload/25 | 7.4 | ||||||
| ECTS | 10 | ||||||