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 to process a large data set
2) Performs big data analysis and visualization on various platforms
3) Learns Python, one of the most preferred programming languages for data analysis
4) Develops applications on various large data processing frameworks
5) Understands NoSQL database systems
6) Understands traditional and deep learning models

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

Lineer Algebra, Probability and Statistics

Course Contents

Data Analysis and Visualization Using Spreadsheets Advanced visualization using Tableau Relational databases and SQL, Advanced SQL Introduction to Pyhton programming 1 Introduction to Pyhton programming 2 Python data analysis and visualization Machine Learning - Regression Machine Learning - Classification and Clustering Machine Learning Using Python Apache Hadoop Apache Spark Flowing Data Analysis NoSQL Databases, Network Analysis, Graf Databases, Neo4j Non-structural data analysis, text analysis Convolutional Neural Networks and Tensor Flow Convolutional Neural Networks and Tensor Flow 2

Weekly Schedule

1) Data Analysis and Visualization Using Spreadsheets
2) Relational databases and SQL, Advanced SQL
3) Introduction to Pyhton programming 1
4) Introduction to Pyhton programming 2
5) Python data analysis and visualization
6) Machine Learning - Regression Machine Learning - Classification and Clustering
7) Machine Learning Using Python
8) Midterm exam
9) Apache Hadoop
10) Apache Spark
11) Stream Data Analysis
12) NoSQL Databases, Network Analysis, Graf Databases, Neo4j
13) Non-structured data analysis, text analysis
14) Convolutional Neural Networks and Tensor Flow
15) Convolutional Neural Networks and Tensor Flow 2
16) Final exam

Recommended or Required Reading

1- Tableau Your Data! : Fast and Easy Visual Analysis with Tableau Software, 2nd Edition, Dan Murray, January 2016
2- Python for Data Analysis, 2nd Edition Data Wrangling with Pandas, NumPy, and IPython, William McKinney, 2017
3- Learning scikit-learn: Machine Learning in Python– November 25, 2013, Raúl Garreta, Guillermo Moncecchi
4- Building Machine Learning Systems with Python, Willi Richert, Luis Pedro Coelho, 2013
5- MapReduce Design Patterns: Building Effective Algorithms and Analytics for Hadoop and Other Systems, 2nd Edition, Donald Miner, Adam Shook, February 25, 2017
6- Learning Spark : Lightning-Fast Big Data Analysis, Holden Karau, Andy Kowinski, Mark Hamstra, Matei Zaharia, 01 Nov 2015
7- Pro Spark Streaming: The Zen of Real-Time Analytics Using Apache Spark, 1st ed.
8- 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) Discussion
4) Drill and Practice
5) Modelling
6) Group Study
7) Lab / Workshop
8) Self Study
9) Problem Solving
10) Project Based Learning

Assessment Methods and Criteria

Contribution of Semester Studies to Course Grade

50%

		Number	Percentage
Semester Studies	Midterm Examination	1	50%
Semester Studies	Project	1	50%

Contribution of Final Examination to Course Grade

50%

Total

100%

Language of Instruction

Turkish

Work Placement(s)

Not Required

Course Unit Title	Course Unit Code	Type of Course Unit	Level of Course Unit	Year of Study	Semester	ECTS Credits
Introduction To Big Data Analysis	TBL456	Elective	Bachelor's degree	4	Fall	5