BDA5011 Big Data and AnalyticsBahçeşehir UniversityDegree Programs ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NONTHESIS)General Information For StudentsDiploma SupplementErasmus Policy StatementNational Qualifications
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
BDA5011 Big Data and Analytics Fall 3 0 3 12
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 Objectives: This course provides an overview of the fields of big data analytics and data science. Topics are covered in the context of data analytics include the terminology and the core concepts behind big data problems, applications, and systems. In this course, the students learn how to use Hadoop and related Big Data Processing tools that are used for scalable big data analysis and have made it easier and more accessible.

Learning Outcomes

The students who have succeeded in this course;
• Get a broad understanding of this high trend Big Data and NoSQL concepts
• Learn big data analytics skills that are highly demanded on the market currently.
• Be able to implement and use MapReduce programs and NoSQL databases.

Course Content

In this course, the technologies associated with big data analytics including NoSQL databases, moving data into Hadoop, real-time data analysis using HBase, big data analytical tools such as Apache Hive and Pig will be covered.

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Discussion of course contents, Overview of Hadoop ecosystem
2) Hadoop Architecture, Basic Linux Commands, Hadoop Installation
3) Data Transfer into and out from Hadoop (Hands on Exercise -SQOOP)
4) Overview of Apache Pig and Pig Latin Basics
5) Pig Latin Operators and Examples
6) Programming with Pig and Examples
7) Overview of Hive and Hive Architecture
8) Hands on HIVE Exercises in Configuration, Database and table operations
9) Hands on HIVE Exercises in Partitions, Buckets, Operators and Built in Functions, Loading data and Overview of Impala
10) Hands on HIVE Exercises in Views and Indexes, HIVEQL –Where, Order By and JOINS
11) Overview of Spark and Spark Architecture
12) Spark & Hands on Exercises
13) Spark & Hands on Exercises
14) Student Presentations of Group Assignments

Sources

Course Notes / Textbooks: Lecture notes will be provided.
References: Big Data Science & Analytics: A Hands-On Approach. Bahga, A. and Madisetti, V., 2016.

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Project 1 % 20
Midterms 1 % 30
Final 1 % 50
Total % 100
PERCENTAGE OF SEMESTER WORK % 30
PERCENTAGE OF FINAL WORK % 70
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Study Hours Out of Class 14 10 140
Project 1 12 12
Midterms 1 3 3
Final 1 3 3
Total Workload 200

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Program Outcomes Level of Contribution