ECTS - Introduction to Data Science
Introduction to Data Science (SE422) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Introduction to Data Science | SE422 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Technical Elective Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | . |
| Course Lecturer(s) |
|
| Course Objectives | |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Python programming language for data science, data scraping, data manipulation, data visualization, use of vectors and matrices in data science, review of statistical concepts for data science, conditional probability, Bayes?s theorem, normal distribution, prediction, regression, classification and clustering. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to Data Science | Chapter 1 + Lecture Notes |
| 2 | Python Programming for Data Science I | Chapter 2 |
| 3 | Python Programming for Data Science II | Lecture notes |
| 4 | Visualizing Data | Chapter 3 |
| 5 | Vectors, Matrices, Review of Statistical Concepts for Data Science | Chapter 4-5 |
| 6 | Conditional Probability, Bayes’s Theorem, Normal Distribution | Chapter 6 |
| 7 | Hypothesis and Inference | Chapter 7 |
| 8 | Midterm | |
| 9 | Gradient Descent | Chapter 8 |
| 10 | Working with Data | Chaper 9-10 |
| 11 | Introduction to Machine Learning Concepts | Chapter 11 |
| 12 | Linear and Logistic Regression | Chapter 14-15 |
| 13 | Neural Networks | Chapter 18 |
| 14 | Clustering and Recommender System | Chapter 19, 22 |
| 15 | Final Exam | |
| 16 | Final exam |
Sources
| Course Book | 1. Data Science from Scratch: First Principles with Python, By Joel Grus , 2015 ISBN: 978-1-491-90142-7 |
|---|---|
| Other Sources | 2. Computational and Inferential Thinking: The Foundations of Data Science, 2nd Edit By Ani Adhikari, John DeNero and David Wagner,on, |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | 1 | 35 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 25 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 3 | 100 |
| Percentage of Semester Work | |
|---|---|
| Percentage of Final Work | 100 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Gains adequate knowledge in mathematics, science, and subjects specific to the software engineering discipline; acquires the ability to apply theoretical and practical knowledge of these areas to complex engineering problems. | X | ||||
| 2 | Gains the ability to identify, define, formulate, and solve complex engineering problems; selects and applies proper analysis and modeling techniques for this purpose. | X | ||||
| 3 | Develops the ability to design a complex system, process, device, or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose. | |||||
| 4 | Demonstrates the ability to select, and utilize modern techniques and tools essential for the analysis and determination of complex problems in software engineering applications; uses information technologies effectively. | X | ||||
| 5 | Develops the ability to design experiments, gather data, analyze, and interpret results for the investigation of complex engineering problems or research topics specific to the software engineering discipline. | X | ||||
| 6 | Demonstrates the ability to work effectively both individually and in disciplinary and interdisciplinary teams in fields related to software engineering. | |||||
| 7 | Demonstrates the ability to communicate effectively in Turkish, both orally and in writing; to write effective reports and understand written reports, to prepare design and production reports, to deliver effective presentations, and to give and receive clear and understandable instructions. | |||||
| 8 | Gains knowledge of at least one foreign language; acquires the ability to write effective reports and understand written reports, prepare design and production reports, deliver effective presentations, and give and receive clear and understandable instructions. | |||||
| 9 | Acquires an awareness of the necessity of lifelong learning; the ability to access information, follow developments in science and technology, and continuously improve oneself. | |||||
| 10 | Acts in accordance with ethical principles and possesses knowledge of professional and ethical responsibilities. | X | ||||
| 11 | Knows the standards used in software engineering practices. | |||||
| 12 | Knows about business practices such as project management, risk management and change management. | |||||
| 13 | Gains awareness about entrepreneurship and innovation. | |||||
| 14 | Gains knowledge on sustainable development. | |||||
| 15 | Has knowledge about the universal and societal impacts of software engineering practices on health, environment, and safety, as well as the contemporary issues reflected in the field of engineering. | |||||
| 16 | Acquires awareness of the legal consequences of engineering solutions. | X | ||||
| 17 | Applies knowledge and skills in identifying user needs, developing user-focused solutions and improving user experience. | X | ||||
| 18 | Gains the ability to apply engineering approaches in the development of software systems by carrying out analysis, design, implementation, verification, validation, and maintenance processes. | X | ||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | |||
| Project | 3 | 5 | 15 |
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 5 | 5 |
| Prepration of Final Exams/Final Jury | 1 | 8 | 8 |
| Total Workload | 124 | ||