Advanced Data Analysis Tools and Techniques
Information Systems Engineering
Institute of Natural and Applied Sciences
Second Cycle (Master's Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Advanced Data Analysis Tools and Techniques | BTM550 | Elective | Master's degree | 1 | Spring | 8 |
Name of Lecturer(s)
Associate Prof. Dr. Süleyman EKEN
Research Assistant Seda BALTA
Research Assistant M.M. Enes YURTSEVER
Learning Outcomes of the Course Unit
1) Learns data analysis tools
2) Learns information about the concept and foundations of data analysis,
3) Applies methods and technologies used for storing, manipulating and analyzing data
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
| Learning Outcomes | ||||||||
| 1 | No relation | Middle | No relation | Low | No relation | No relation | No relation | |
| 2 | No relation | No relation | No relation | High | No relation | No relation | No relation | |
| 3 | No relation | No relation | No relation | No relation | No relation | No relation | No relation | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Data Analysis & Visualization Using Spreadsheets, Advanced Data Visualization Using Tablea, Relational Databases and Advanced SQL, Introduction to Python, Python for Data Analysis & Visualization, Linear Algebra, Statisrics and Probability, Machine Learning - Regression, Machine Learning - Classification and Clustering, Using Python for Machine Learning, Apache Hadoop MapReduce Design Patterns, Amazon Big Data Platforms and Services, Apache Spark, Spark ML, Streaming Data Analysis, NoSQL databases, Nework Analysis, Graph Databases, Neo4j, Unstructured Data Analysis, Text Analysis, CNN and Tensor Flow
Weekly Schedule
1) Data Analysis & Visualization Using Spreadsheets, Advanced Data Visualization Using Tablea2) Relational Databases and Advanced SQL
3) Introduction to Python
4) Python for Data Analysis & Visualization
5) Linear Algebra, Statistics and Probability
6) Machine Learning - Regression, Machine Learning - Classification and Clustering
7) Python for Machine Learning
8) Apache Hadoop MapReduce Design Patterns
9) Midterm exam
10) Amazon Big Data Platforms and Services
11) Apache Spark, Spark ML, Streaming Data Analysis
12) NoSQL databases, Nework Analysis, Graph Databases, Neo4j
13) Unstructured Data Analysis, Text Analysis
14) CNN and Tensor Flow
15) CNN and Tensor Flow
16) Final exam
Recommended or Required Reading
1- Tablea 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. Edition, Zubair Nabi, June 14, 2016
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) Case Study
6) Lab / Workshop
7) Self Study
8) Problem Solving
9) Project Based Learning
Assessment Methods and Criteria
Contribution of Midterm Examination to Course Grade |
30% |
|---|---|
Contribution of Final Examination to Course Grade |
70% |
Total |
100% |
Language of Instruction
Turkish
Work Placement(s)
Not Required