Kocaeli University / European Credit Transfer System (ECTS)

Name of Lecturer(s)

Associate Prof. Dr. Süleyman EKEN
Research Assistant Seda BALTA

Learning Outcomes of the Course Unit
Program Competencies-Learning Outcomes Relation

Learning Outcomes of the Course Unit

1) Learns the fundamental ideas to clean, manipulate, process and analyze data.
2) Applies statistical tools to learn from data.
3) Models data with conceptual, relational, and recent models.
4) Learns learn basics of transforming problems into analytical/quantitative/mathematical models, and how to formulate and solve simple mathematical models that represent optimization problems.
5) Learns background to start to develop programs that will run on HDFS.
6) Understands fundamental aspects of Machine Learning and applies them on different platforms.
7) Summarizes and visualizes the important characteristics of a data set.
8) Understands the fundamental theory behind each ML technique, as well as implementing them using an environment such as RapidMiner.
9) Studies different social applications and how they can be modeled.
10) Understands the basics of graph theory.
11) Understands and perform basic social network analysis
12) Provides industry insight into the world of project management and business communication.

Program Competencies-Learning Outcomes Relation

Program Competencies

Learning Outcomes

No relation

High

No relation

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Not Required

Course Contents

Topics required in all aspects of data analytics, with flexibility to allow different interests.

Weekly Schedule

1) Course Overview. Introduction to Data Analytics and Management
2) İstatistiksel modelleme teknikleri (doğrusal regresyon, temel bileşen analizi, çapraz validasyon ve p-değerleri)
3) Data modeling (ER and UML models, relational model, key-value stores, dcument databases and graph databases)
4) Optimization (linear, nonlinear and integer optimization problems, network flow and network design problems)
5) Big Data Processing using Hadoop and Spark
6) Fundamental machine learning techniques
7) Exploratory Data Analysis (EDA)
8) Midterm exam
9) Data analysis life cycle with RapidMiner
10) Social Network Analysis
11) Practical Case Studies in Data Analytics
12) Information Law and Data Ethics
13) Project Management and Business Communication
14) Project presentations
15) Project presentations
16) Final exam

Recommended or Required Reading

1- Python for Data Analysis, 2nd Edition Data Wrangling with Pandas, NumPy, and IPython, William McKinney, 2017
2- Learning scikit-learn: Machine Learning in Python– November 25, 2013, Raúl Garreta, Guillermo Moncecchi
3- Building Machine Learning Systems with Python, Willi Richert, Luis Pedro Coelho, 2013
4- MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems, 2nd Edition, Donald Miner, Adam Shook, February 25, 2017
5- Learning Spark : Lightning-Fast Big Data Analysis, Holden Karau, Andy Kowinski, Mark Hamstra, Matei Zaharia, 01 Nov 2015
6- Pro Spark Streaming: The Zen of Real-Time Analytics Using Apache Spark, 1st ed. Edition, Zubair Nabi, June 14, 2016
7- Graph Databases, Second Edition, Ian Robinson, Jim Webber, and Emil Eifrem, June 2015

Planned Learning Activities and Teaching Methods

1) Lecture
2) Question-Answer
3) Drill and Practice
4) Case Study
5) Self Study
6) Project Based Learning

Assessment Methods and Criteria

Contribution of Semester Studies to Course Grade

60%

		Number	Percentage
Semester Studies	Midterm Examination	1	40%
Semester Studies	Project	1	60%

Contribution of Final Examination to Course Grade

40%

Total

100%

Language of Instruction

Turkish

Work Placement(s)

Required

Course Unit Title	Course Unit Code	Type of Course Unit	Level of Course Unit	Year of Study	Semester	ECTS Credits
Big Data Analytics and Management	BTM608	Elective	Doctorate degree	1	Spring	8