- Code
- CMP 404
- Name
- Advanced Program in Python
- Semester
- 2
- Lecture hours
- 3.00
- Seminar hours
- 1.00
- Laborator hours
- 0.00
- Credits
- 3.50
- ECTS
- 6.00
- Description
-
In this course we aim to provide students with basic knowledge of python programming languge and than jump into more advanced topics related to data science like classification algorithms, regression analysis clustering. Learn how to use relevant libraries for advanced scientific calculations.
- Objectives
-
Students can easily solve basic problems using python and know how to apply data science algorithms in different data sets.
- Java
- Tema
- 1
- Explanation of the basic concepts of python. Variables and data types. Declaring and manipulating data in python. Creating a working environment on personal computers by installing Anaconda and jupiter notebook programs. Practical introduction to python Programming fq 3-9 Udemy
- 2
- Using if and if else conditionals in python. Solving practical exercises using variables with different types of data and conditionals.
- 3
- Explaining the basic concepts of iteration and using loops in python. Using the for loop and solving practical exercises using the for loop.
- 4
- Explaining the basic concepts of iteration and using loops in python. Using the while loop and solving practice exercises using the while loop.
- 5
- String data type. Usage storage and string manipulation.
- 6
- Lists, declaring lists, creating a new list, printing data from a list. The application of various functions and methods that are applied to a list to perform specific tasks. Practical introduction to python Programming pg. 57- 62
- 7
- Difference between lists and strings. The same methods that are applied to strings and lists and what changes a list or a string from a certain method. More detailed information about lists. Practical introduction to python Programming pg. 65 - 72
- 8
- Midterm Exam
- 9
- Dictionaries. Structuring data using dictionaries. How to create a new dictionary, how to add data to a dictionary we have created. You can change and print the data. Practical introduction to python Programming pg. 99 - 104
- 10
- Linear Regression Basics of data classification using python. Applying linear regression to continuous data using a concrete dataset. Udemy
- 11
- Logistic Regression Applying logistic regression with categorical data using the necessary methods and libraries in python with a concrete dataset. Udemy
- 12
- Clustering with continuous data. Clustering or gathering data according to similarity by dividing them into certain groups. We apply clustering algorithms in python using continuous data. Udemy
- 13
- Clustering with continuous data. Clustering or gathering data according to similarity by dividing them into certain groups. We apply clustering algorithms in python using categorical data. Udemy.
- 14
- Project Presentation
- 15
- Review
- 16
- Final Exam
- 1
- Students can easily solve basic problems using python and know how to apply data science algorithms in different data sets.
- Quantity Percentage Total percent
- Midterms
- 1 30% 30%
- Quizzes
- 0 0% 0%
- Projects
- 0 0% 0%
- Term projects
- 0 0% 0%
- Laboratories
- 0 0% 0%
- Class participation
- 1 10% 10%
- Total term evaluation percent
- 40%
- Final exam percent
- 60%
- Total percent
- 100%
- Quantity Duration (hours) Total (hours)
- Course duration (including exam weeks)
- 16 4 64
- Off class study hours
- 14 6 84
- Duties
- 0 0 0
- Midterms
- 1 4 4
- Final exam
- 1 4 4
- Other
- 0 0 0
- Total workLoad
- 156
- Total workload / 25 (hours)
- 6.24
- ECTS
- 6.00