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 | 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 | Has adequate knowledge in mathematics, science, and computer engineering-specific subjects; uses theoretical and practical knowledge in these areas to solve complex engineering problems. | |||||
| 2 | Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose. | X | ||||
| 3 | Designs a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions; applies modern design methods for this purpose. | X | ||||
| 4 | Develops, selects, and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; uses information technologies effectively. | X | ||||
| 5 | Designs experiments, conducts experiments, collects data, analyzes and interprets results for the investigation of complex engineering problems or research topics specific to the discipline of computer engineering. | X | ||||
| 6 | Works effectively in disciplinary and multidisciplinary teams; gains the ability to work individually. | |||||
| 7 | Communicates effectively in Turkish, both orally and in writing; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions. | |||||
| 8 | Knows at least one foreign language; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions. | |||||
| 9 | Has awareness of the necessity of lifelong learning; accesses information, follows developments in science and technology, and continuously improves oneself. | |||||
| 10 | Acts in accordance with ethical principles and has awareness of professional and ethical responsibility. | |||||
| 11 | Has knowledge about the standards used in computer engineering applications. | |||||
| 12 | Has knowledge about workplace practices such as project management, risk management, and change management. | |||||
| 13 | Gains awareness about entrepreneurship and innovation. | |||||
| 14 | Has knowledge about sustainable development. | |||||
| 15 | Has knowledge about the health, environmental, and safety impacts of computer engineering applications in universal and societal dimensions and the contemporary issues reflected in the field of engineering. | |||||
| 16 | Gains awareness of the legal consequences of engineering solutions. | X | ||||
| 17 | Analyzes, designs, and expresses numerical computation and digital representation systems. | |||||
| 18 | Uses programming languages and appropriate computer engineering concepts to solve computational problems. | |||||
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 | ||