BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, THESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
Course Introduction and Application Information
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| BDA5001 | Introduction to Big Data | Fall | 3 | 0 | 3 | 8 |
| 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: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Assist. Prof. SERKAN AYVAZ |
| Course Lecturer(s): |
Assoc. Prof. YÜCEL BATU SALMAN Assist. Prof. SERKAN AYVAZ |
| Course Objectives: | The objective of this course is introduce fundamental concepts and methods in Big Data analytics and Data science, and provide students the insights into the basics of using "Big Data" in real-world scenarios. |
Learning Outcomes
|
The students who have succeeded in this course; Will learn how to develop fundamental statistical models using R programming language. Will learn how to analyze big data sets to provide insight regarding the assumptions, value drivers, and risks. Will use statistical models to explore different ways to think about uncertainty, guide decision-making, and persuasively communicate analytical results. Will learn how to apply basic methods to text mining, building search engines and recommendation systems. |
Course Content
| Introduction to Big Data and Data Science Statistical programming: Introduction to R and RStudio Data Modeling Basics/Data Collection/Cleansing/Processing Data Visualization and Communication Simple Regression: Introduction, Statistical and Practical Flexible Regression Models: Dummy Variables Flexible Regression Models: Data Transformation Selective Regression Models Fundamentals of Text Mining Building Search Engines Inner workings of Recommendation Engines |
Weekly Detailed Course Contents
| Week | Subject | Related Preparation |
| 1) | Introduction to Big Data and Data Science | |
| 2) | Statistical programming: Introduction to R and RStudio | |
| 3) | Data Modeling Basics/Data Collection/Cleansing/Processing | |
| 4) | Data Visualization and Communication | |
| 5) | Simple Regression: Introduction, Statistical and Practical Significance of Predictors | |
| 6) | Flexible Regression Models: Dummy Variables | |
| 7) | Flexible Regression Models: Data Transformation | |
| 8) | Selective Regression Models | |
| 9) | Text Mining | |
| 10) | Building Search Engines | |
| 11) | Inner workings of Recommendation Engines | |
| 12) | Project Presentations | |
| 13) | Project Presentations |
Sources
| Course Notes / Textbooks: | Jeffrey Stanton. An Introduction to Data Science (2013) edition 3. Wolfgang Jank. Business Analytics for Managers (2011). Roger D. Peng. R Programming for Data Science(2016) |
| References: | Doing Data Science, Rachel Schutt and Cathy O’Neil. 2014. O’Reilly. The Art of Data Science: A Guide for Anyone Who Works with Data, Roger D. Peng and Elizabeth Matsui An Introduction to Statistical Learning with Applications in R, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani R for Beginners, Emmanuel Paradis, 2005 , http://cran.r-project.org/other-docs.html |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Homework Assignments | 1 | % 10 |
| Project | 1 | % 35 |
| Midterms | 1 | % 15 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 25 | |
| PERCENTAGE OF FINAL WORK | % 75 | |
| Total | % 100 | |
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |