| Study program / study programs: Advanced data analytics |
| Course: Big Data Analytics |
| Teacher(s): Vukmirović Dragan, Jeremić Veljko, Tomašević Nikola, Batić Marko |
| Course status: Elective |
| ECTS points: 10 |
| Prerequisites: none |
| Course objective: This course will cover the basic concepts of big data analytics, methodologies for analyzing structured and unstructured data with emphasis on the relationship between the Data Scientist and the business needs. |
| Learning outcomes: After the course, student will be able to critically analyse existing Big Data datasets and implementations, taking practicality, and usefulness metrics into consideration. Moreover, to understand and demonstrate advanced knowledge of statistical data analytics as applied to large data sets. |
| Course structure and content: The concepts and techniques of Big Data analytics are first introduced through the review and presentation of theoretical foundations, followed by practical work using contemporary statistical and simulation software. Research Methodology Introduction to Data & Data Science Data Analytics Lifecycle and methodology Data Cleaning & Preparation Data Summarization & Visualization Building a Data Model in a software environment Data Analytics: Theory & Methods (supervised and unsupervised learning in Big Data) Spark 2.0, Spark ML Library, R The ethics of using (and misusing) data |
| Literature/Readings: Walkowiak, S. (2016). Big Data Analytics with R: Leverage R Programming to uncover hidden patterns in your Big Data. Packt Publishing. Available online: https://www.packtpub.com/big-data-and-business-intelligence/big-data-analytics-r Bahga, A., & Madisetti, V. (2016). Big Data Science & Analytics: A Hands-On Approach. VPT. Available online: https://www.amazon.com/Big-Data-Science-Analytics-Hands/dp/0996025537 Li, K. C., Jiang, H., Yang, L. T., & Cuzzocrea, A. (Eds.). (2015). Big data: Algorithms, analytics, and applications. CRC Press.. Available online: https://www.crcpress.com/Big-Data-Algorithms-Analytics-and-Applications/Li-Jiang-Yang-Cuzzocrea/p/book/9781482240559 Erl, T., Khattak, W., & Buhler, P. (2016). Big Data Fundamentals: Concepts, Drivers & Techniques. Prentice Hall Press.. Available online: https://www.amazon.com/Big-Data-Fundamentals-Techniques-Technology/dp/0134291077 Provost, F., & Fawcett, T. (2013). Data Science for Business: What you need to know about data mining and data-analytic thinking. O’Reilly Media. Available online: http://shop.oreilly.com/product/0636920028918.do |
| The number of class hours per week: Lectures: 4 Labs: 0 Workshops: 1 Research study: 2 Other classes: 0 |
| Teaching methods: Individual and group work; lectures and labs |
| Evaluation/Grading (maximum 100 points): Pre-exam requirements (Project): 60 Final exam (Oral exam): 40 |
