BDA5001 Introduction to Big DataBahçeşehir UniversityDegree Programs ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NONTHESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational QualificationsBologna Commission
ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NONTHESIS)
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 : Dr. Öğr. Üyesi SERKAN AYVAZ
Course Lecturer(s): Dr. Öğr. Üyesi YÜCEL BATU SALMAN
Dr. Öğr. Üyesi 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