The purpose of the Fundamentals Of Data Science course is to provide students with an in-depth comprehension of the fundamental concepts, techniques, and tools used in the field of data science. It covers a broad variety of topics, such as data cleansing, shaping, and comprehension via exploratory data analysis, descriptive statistics, and inferential data analysis. Students will learn techniques for statistical analysis, pattern recognition, and data representation, including linear and nonlinear models, hypothesis testing, clustering, and dimensionality reduction. In addition, the course covers big-data management, interactive visualizations, and advanced methods such as machine learning, deep learning, and real-world prediction. Students will apply their knowledge to data science assignments/projects and present their findings throughout the course. Students will be endowed with the knowledge and skills necessary to analyze, interpret, and extract valuable insights from large and complex datasets while effectively managing data and utilizing advanced modeling and prediction techniques by the end of the course.
Prerequisite(s)
Corequisite(s)
Special Requisite(s)
Instructor(s)
Assoc. Prof. Üyesi Fatma PATLAR AKBULUT
Course Assistant(s)
Schedule
Office Hour(s)
Teaching Methods and Techniques
Principle Sources
- Loshin, David. Big data analytics: from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph. Elsevier, 2013.
- Dean, Jared. Big data, data mining, and machine learning: value creation for business leaders and practitioners. John Wiley & Sons, 2014.
- Long, C. "Data science and big data analytics: Discovering, analyzing, visualizing and presenting data." Indianapolis, Indiana (2015).
Other Sources
Course Schedules
Week
Contents
Learning Methods
1. Week
Overview
Oral Presentation, Laboratory
2. Week
Data Science Introduction
Oral Presentation, Laboratory
3. Week
Python for Data Analysis
Oral Presentation, Laboratory
4. Week
Categorical and Quantitative Variables
Exploration, Preprocessing, Feature Engineering
Oral Presentation, Laboratory
5. Week
Textual Data Processing
Oral Presentation, Laboratory
6. Week
Statistical Methods and Descriptive Statistics
Oral Presentation, Laboratory
7. Week
Inferential Statistics Hypothesis Testing
Statistical Test for Data Analysis
Oral Presentation, Laboratory
8. Week
Midterm
Oral Presentation, Laboratory
9. Week
Introduction to Machine Learning
Oral Presentation, Laboratory
10. Week
Machine Learning: Regression Analysis I
Oral Presentation, Laboratory
11. Week
Machine Learning: Regression Analysis II
Oral Presentation, Laboratory
12. Week
Machine Learning: Clustering
Oral Presentation, Laboratory
13. Week
Machine Learning: Classification
Oral Presentation, Laboratory
14. Week
Project Presentations
Project Presentations
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Program Outcomes
PO-1
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
PO-3
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1
Students will develop relevant programming abilities for data analysis.
LO-2
Students will obtain abilities about, clean/process, and transform data
LO-3
Students will demonstrate proficiency with statistical analysis of data.
LO-4
Students will analyze large datasets in the context of real world problems
LO-5
Students will develop and implement data analysis strategies base on theoretical principles, ethical considerations, and detailed knowledge of the underlying data
LO-6
Students will use appropriate models of analysis, assess the quality of input, derive insight from results, and investigate potential issues
LO-7
Interpret data findings effectively to any audience, orally, visually, and in written formats
