ECTS - Introduction to Data Science and Machine Learning
Introduction to Data Science and Machine Learning (MATH340) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Introduction to Data Science and Machine Learning | MATH340 | 6. Semester | 4 | 0 | 0 | 4 | 6 |
| Pre-requisite Course(s) |
|---|
| CMPE102 |
| Course Language | English |
|---|---|
| Course Type | Compulsory Departmental Courses |
| Course Level | Bachelor’s Degree (First Cycle) |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| Course Objectives | The objective of this course is to introduce the fundamental concepts of data science and machine learning. One of the main focuses is to provide the ability for data manipulation, analysis and utilizing real-life data for applications. Moreover, the course aims to develop required skills to understand machine learning algorithms. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Python programming language for data science and machine learning. Data scraping, manipulation and visualization. Review of statistical concepts, linear algebra and probability theory for data science. Introduction to Machine Learning. Prediction: Regression, Classification, and elustering.Describing Supervised/Unsupervised learning algorithms. Basics of Neural networks. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introdcution to Data Science, Python Programming for Data Science | Chapter 1, 2 |
| 2 | Python Programming for Data Science | Chapter 2 + Lecture Notes for Applications (LNA) |
| 3 | Python Programming for Data Science | Chapter 2 + LNA |
| 4 | Visualizing Data, Review of Linear Algebra for Data Science | Chapter 3, 4 |
| 5 | Review of Statistics and Probability for Data Science | Chapter 5, 6 |
| 6 | Hypothesis and Inference | Chapter 7 + LNA |
| 7 | Gradient Descent | Chapter 8 + LNA |
| 8 | Midterm | |
| 9 | Getting data, Working with Data | Chapter 9, 10 |
| 10 | Introduction to Machine Learning | Chapter 11 + LNA |
| 11 | k-Nearest Neighbors. Naive Bayes | Chapter 12, 13 |
| 12 | Linear and Logistic Regression | Chapter 14, 16 |
| 13 | Decision Trees | Chapter 17 + LNA |
| 14 | Neural Networks | Chapter 18 + LNA |
| 15 | Clustering | Chapter 19 + LNA |
| 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. Introduction to Machine Learning with Python, A Guide for Data Scientists by Andreas C. Müller and Sarah Guido, O'Reilly Media, Inc, October 2016 |
| 3. VanderPlas, J., 2016. Python data science handbook: Essential tools for working with data. " O'Reilly Media, Inc.". |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | 2 | 15 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 30 |
| Final Exam/Final Jury | 1 | 40 |
| Toplam | 4 | 85 |
| Percentage of Semester Work | 60 |
|---|---|
| Percentage of Final Work | 40 |
| 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 the ability to apply scientific knowledge gained in the undergraduate education and to expand and extend knowledge in the same or in a different area | X | ||||
| 2 | Can apply gained knowledge and problem solving abilities in inter-disciplinary research | X | ||||
| 3 | Has the ability to work independently within research area, to state the problem, to develop solution techniques, to solve the problem, to evaluate the obtained results and to apply them when necessary | X | ||||
| 4 | Takes responsibility individually and as a team member to improve systematic approaches to produce solutions in unexpected complicated situations related to the area of study | X | ||||
| 5 | Can develop strategies, implement plans and principles on the area of study and can evaluate obtained results within the framework | X | ||||
| 6 | Can develop and extend the knowledge in the area and to use them with scientific, social and ethical responsibility | X | ||||
| 7 | Has the ability to follow recent developments within the area of research, to support research with scientific arguments and data, to communicate the information on the area of expertise in a systematically by means of written report and oral/visual presentation | X | ||||
| 8 | To have an oral and written communication ability in at least one of the common foreign languages ("European Language Portfolio Global Scale", Level B2) | X | ||||
| 9 | Has software and hardware knowledge in the area of expertise, and has proficient information and communication technology knowledge | X | ||||
| 10 | Follows scientific, cultural, and ethical criteria in collecting, interpreting and announcing data in the research area and has the ability to teach. | X | ||||
| 11 | Has professional ethical consciousness and responsibility which takes into account the universal and social dimensions in the process of data collection, interpretation, implementation and declaration of results in mathematics and its applications. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 4 | 64 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 14 | 4 | 56 |
| Presentation/Seminar Prepration | |||
| Project | 2 | 5 | 10 |
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 8 | 8 |
| Prepration of Final Exams/Final Jury | 1 | 12 | 12 |
| Total Workload | 150 | ||