COURSE UNIT TITLE

: BIG DATA PROCESSING AND STATISTICAL MODELS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
UEC 2018 BIG DATA PROCESSING AND STATISTICAL MODELS COMPULSORY 3 0 0 4

Offered By

Economics (English) (UOLP-New York State University (Suny Albany))

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR BURÇAK MÜGE VURAL

Offered to

Economics (English) (UOLP-New York State University (Suny Albany))

Course Objective

Course Objective:
To introduce students to the principles and tools of big data processing and to develop their ability to apply statistical modeling techniques to analyze and interpret large and complex datasets in real-world scenarios.

Learning Outcomes of the Course Unit

1   1. Understand the fundamental concepts of big data and data processing architectures (e.g., Hadoop, Spark).
2   2. Apply statistical models such as regression, classification, and clustering to large datasets.
3   3. Use programming tools (e.g., Python, R) for data preprocessing, transformation, and analysis.
4   4. Interpret the results of statistical models and communicate insights effectively.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to Big Data and Analytics
2 Types and Sources of Big Data
3 Data Storage Systems and File Formats
4 Hadoop Ecosystem and MapReduce Basics
5 Apache Spark and Distributed Computing
6 Data Cleaning and Preprocessing
7 Exploratory Data Analysis (EDA)
8 Statistical Modeling: Linear and Logistic Regression
9 Classification Methods and Performance Metrics
10 Clustering Algorithms (K-Means, Hierarchical)
11 Dimensionality Reduction and Feature Selection
12 Model Evaluation and Cross Validation
13 Case Study: Predictive Analytics in Big Data
14 Final Project Presentations and Review

Recomended or Required Reading

To be announced.

Planned Learning Activities and Teaching Methods

1. Lecture
2. In-class studies
3. Group Project

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MT Midterm
2 ASS Assignment
3 FN Final
4 FCG FINAL COURSE GRADE MT * 0.35 +ASS * 0.25 + FN * 0.40
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MT * 0.35 + ASS * 0.25 + RST * 0.40


Further Notes About Assessment Methods

None

Assessment Criteria

Attendance & Participation: Active involvement in discussions.
Assignments: Hands-on exercises with real or simulated big data sets.
Midterm Exam: Covers theoretical foundations and analytical techniques.
Final Project: Students will work on a real-world big data problem, implement a model, and present results.
Quizzes: Short in-class assessments on technical and conceptual topics.

Language of Instruction

English

Course Policies and Rules

1. Attending at least 70% of lectures is mandatory.
2. Plagiarism of any type will result in disciplinary action.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Tutorials 14 1 14
Preparations before/after weekly lectures 14 2 28
Preparation for midterm exam 1 10 10
Preparation for final exam 1 14 14
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 98

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.134
LO.234
LO.344
LO.444

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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