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 | Acquires skills to use the advanced theoretical and applied knowledge obtained at the mathematics bachelors program to do further academic and scientific research in both mathematics-based graduate programs and public or private sectors. | X | ||||
| 2 | Transplants and applies the theoretical and applicable knowledge gained in their field to the secondary education by using suitable tools and devices. | X | ||||
| 3 | Acquires the skill of choosing, using and improving problem solving techniques which are needed for modeling and solving current problems in mathematics or related fields by using the obtained knowledge and skills. | X | ||||
| 4 | Acquires analytical thinking and uses time effectively in the process of deduction. | X | ||||
| 5 | Acquires basic software knowledge necessary to work in the computer science related fields and together with the skills to use information technologies effectively. | X | ||||
| 6 | Obtains the ability to collect data, to analyze, interpret and use statistical methods necessary in decision making processes. | X | ||||
| 7 | Acquires the level of knowledge to be able to work in the mathematics and related fields and keeps professional knowledge and skills up-to-date with awareness in the importance of lifelong learning. | X | ||||
| 8 | Takes responsibility in mathematics related areas and has the ability to work affectively either individually or as a member of a team. | X | ||||
| 9 | Has proficiency in English language and has the ability to communicate with colleagues and to follow the innovations in mathematics and related fields. | X | ||||
| 10 | Has the ability to communicate ideas with peers supported by qualitative and quantitative data. | X | ||||
| 11 | Has professional and 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. | X | ||||
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 | ||