luumsk / nsu_python

Materials for the Python programming course at Novosibirsk State University

Python Programming

1. Course Description

This comprehensive Python programming course covers fundamental concepts, advanced topics, and practical applications. Students will gain a deep understanding of Python and its versatile applications in various domains.

2. Prerequisites

No prior programming experience required.

3. Syllabus

Week 1: Introduction and Placement Test (11.09-17.09)

Topics:

• Introduction to the Python programming course.
• Overview of course objectives and structure.

Activity:

• Placement quiz to assess students' prior knowledge.

Week 2: Python Basics and Data Structures I (18.09-24.09)

Lecture and lab:

• Review of Python basics.
• Loops and conditional statements.
• Data structures: Lists.
• Data structures: Tuples and Sets.

Graded Assignment 1

Week 3: Python Basics and Data Structures II (25.09-01.10)

Lecture and lab:

• Data structures: Dictionaries.
• Working with strings and regular expressions.
• Handling exceptions
• Read and write files.

Graded Assignment 2

Week 4: Object-Oriented Programming (OOP) and Modules (02.10-08.10)

Lecture and lab:

• Working with functions.
• Introduction to OOP basics.
• Classes, objects, methods, and attributes.
• Virtual environments for project isolation.
• Modules and packages in Python.

Mid-term exam (Multiple Choice Questions) to assess understanding.

Week 5: Data Manipulation with NumPy (09.10-15.10)

Lecture and lab:

• Introduction to NumPy for numerical computing.
• Creating NumPy arrays and basic array operations.
• Indexing, slicing, and reshaping NumPy arrays.
• Universal functions
• Broadcasting
• Boolean masking

Graded Assignment 3

Week 6: Data Analysis with Pandas (16.10-22.10)

Lecture and lab:

• Introduction to Pandas Series and DataFrames.
• Loading and exploring datasets using pandas.
• Data cleaning, filtering, and handling missing values.
• Data aggregation and summarization.
• Grouping and pivoting data.
• Applying functions and transformations.

Graded Assignment 4

Week 7: Data Visualization with Matplotlib and Seaborn (23.10-29.10)

Lecture and lab:

• Introduction to data visualization.
• Visualizing data using Pandas built-in capabilities.
• Creating static and interactive plots with Matplotlib.
• Enhancing visualizations with Seaborn.

Graded Assignment 5

Week 8: Evaluation (30.10-05.11)

Final coding exam

4. Assessment:

4.1. Placement test

• Date & Time: 17:00 on Monday, 11.09.2023
• Link
• The test consists of 30 multiple choice questions to assess your current understanding of Python.
• Results from the test are NOT counted towards your GPA.

4.2. Graded Assignments

• Date: Throughout the course
• Time: Assignments will be released on Thursday each week and the deadline for submission is 23:59 Thursday of the following week. So you'll have 1 week to submit your assignment.
• Penalty will be applied for late submission (minus 10% per lated day)
• Assignment grades accountts for 30% of your final grade.

4.3. Mid-term exam

• Date & Time: 10:50 GMT+7 on Thursday, 05.10.2023
• Mid-term exam consists of 30 multiple choice questions about topics that were discussed previously during the course.
• You will have 1 hour to finish your test.
• Mid-term exam accounts for 20% of your final grade.

4.4. Final coding exam

• Date & Time: 10:50 GMT+7 on Thursday, 02.11.2023
• Link
• Final coding exam consists of 5-20 coding exercises related to topics discussed during the course. The number of questions depending on the complex of the problem. More questions mean they are easier than less questions.
• Final coding exam accounts for 50% of your final grade.

5.Materials

• q&a: Questions occured during the class and their answers
• Python Doc
• Numpy Doc
• Pandas Doc
• Python Data Science Hanbook