At the end of this course, the students; 1) Know the introduction to artificial intelligence and its basic concepts, application areas, effects on personal, social and business world, 2) Know the subject areas of data science, big data, data mining, machine learning, basic components, differences from each other, application purposes and methods, 3) Know the structure, working model and features of the Python programming language, 4) Know Python and data science software development environments (Anaconda, Jupiter, Google Colab, Spyder etc.) and their installations, 5) Know variables, simple data types, assignments and arithmetic operations in Python Programming Language, 6) Know the use of control and loop statements in Python Programming Language 7) Know error-exception handling in Python Programming Language, 8) Know data structures (Tuple, List, Dictionary, Set etc.) in Python Programming Language, 9) Know the use of functions in Python Programming Language, functional analysis and design of software, 10) Know the use of classes in Python Programming Language, 11) Know the analysis and design of software in line with the object-oriented approach and Model-View-Controlor (MVC) template in Python Programming Language, 12) Know the main stages of data science projects, 13) Know the stages of data collection, data cleaning and processing, determination of data properties, exploratory data analysis, design and use of data models and algorithms, 14) Know Supervised Machine Learning models/algorithms, 15) Know Unsupervised Machine Learning models/algorithms, 16) Have the knowledge and skills to develop a term project at the application level.
MODE OF DELIVERY
Face to face
PRE-REQUISITES OF THE COURSE
No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
None
COURSE DEFINITION
The course has two main objectives:
a. To acquire basic programming knowledge and skills necessary to develop data science, machine learning, corporate application and web software by learning the structure and components of the Python programming language,
b. To perform introductory data science and machine learning applications using the acquired programming knowledge and skills.
COURSE CONTENTS
WEEK
TOPICS
1st Week
Course Description, Introduction to Artificial Intelligence and Its Basic Concepts, Application Areas, Effects on Personal, Social and Business World Key Topics: Data Science, Big Data, Data Mining, Machine Learning and Its Fundamentals, Python Software Development Environments and Installations (Anaconda, Google Colab, Information about Jupiter, Spyder etc.)
2nd Week
Python Programming Language Writing Rules and Differences (Space, Cell (Cell) Concept, Variables, Simple Data Types (Integer, Floating Point, String, Boolean, Date, etc.), Expressions, Assignments and Arithmetic Operations, Data Structures in Python, Ready Packages and Usage Areas in Python
3rd Week
Control and Loop Statements (IF, IF-Else, Switch, For Loop, While Loop etc.), Data Handling with For and While Statements, Error-Exception Handling
4th Week
Use of Functions, Functional Analysis and Design of Python Software
5th Week
Object Oriented Programming and Basic Concepts, Classes and Their Usage, Organizing Methods of Python Projects Source Codes (Functional, NYP, Module, Package Concepts, Organizing Python Projects Using Model-View-Controller (MVC) Template in line with NYP Approach
6th Week
Data Structures and Usages in Python (List, Tuple, Dicitionary, Set etc.)
7th Week
General Stages of Data Science and Machine Learning Project Management, Defining and Initiating the Project (Needs analysis and definition of the problem, Determining the project objectives, Preparation of the Project initiation document), Execution of the Project (Data Science Steps (Specification of needs, Identifying the data sources and obtaining the data))
8th Week
A R A S I N A V H A F T A S I
9th Week
Exploratory Data Analysis (EDA), Data Preparation (cleaning, merging, transformation), Python Libraries for Data Visualization (Seaborn, Matplotlib etc.), Python Libraries for Data Processing and Computing (Pandas, Numpy, Scipy, etc.)
Unsupervised Machine Learning, K-Means Learning Model etc.
13th Week
An overview
14th Week
Presentation and Evaluation of Term Projects
RECOMENDED OR REQUIRED READING
Pramod Singh, (2019). Machine Learning with PySpark, APress Media, NY, USA. Puneet Mathur, (2019). Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance. , APress Media, NY, USA. Andreas C. Müller, Sarah Guido (Author) (2016). Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media. Oliver Theobald (2018). Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning From Scratch). Independent Publisher. Mueller, J.P., Massaron, L.(2016). Machine Learning For Dummies, John Wiley & Sons, Inc., USA. Oliver Theobald (2018). Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning From Scratch).
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS
Projectct-Supported Traditional Instructional Method
ASSESSMENT METHODS AND CRITERIA
Quantity
Percentage(%)
Mid-term
1
40
Project
1
20
Other
1
10
Total(%)
70
Contribution of In-term Studies to Overall Grade(%)
70
Contribution of Final Examination to Overall Grade(%)
30
Total(%)
100
ECTS WORKLOAD
Activities
Number
Hours
Workload
Midterm exam
1
1,5
1,5
Preparation for Quiz
0
1
0
Individual or group work
14
1
14
Preparation for Final exam
1
22
22
Course hours
13
2
26
Preparation for Midterm exam
1
14
14
Laboratory (including preparation)
14
2
28
Final exam
1
1,5
1,5
Homework
Presentation (including preperation)
1
3
3
Project
1
24
24
Report writing
1
23
23
Total Workload
157
Total Workload / 30
5,23
ECTS Credits of the Course
5
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)
