Прикладной анализ данных в Python

Магистратура 2018/2019

Лучший по критерию «Полезность курса для Вашей будущей карьеры»

Лучший по критерию «Полезность курса для расширения кругозора и разностороннего развития»

Лучший по критерию «Новизна полученных знаний»

Статус: Курс по выбору (Современный социальный анализ)

Направление: 39.04.01. Социология

Кто читает: Департамент социологии

Где читается: Санкт-Петербургская школа социальных наук

Когда читается: 1-й курс, 3 модуль

Формат изучения: без онлайн-курса

Преподаватели: Нагорный Олег Станиславович

Прогр. обучения: Современный социальный анализ

Язык: английский

Кредиты: 2

Контактные часы: 28

Full Syllabus

Abstract

Python is one of the most popular and rapidly developing programming languages. A clear syntax which facilitates learning and a plethora of built-in and third-party libraries made Python especially popular among academics and researchers of all kinds. Python has already been the first-choice language in Machine Learning and Data Science for a while, but as far as Social Sciences are becoming more digitally-oriented it is getting in demand by sociologists, economists, linguists, and other social researchers. This course is created for students who want to learn how to solve real-world data-related problems with Python programming environment but have no experience in programming. The course syllabus covers most of Python functionality from basics syntax to the modern libraries for machine learning and data analysis.

Learning Objectives

Learn how to solve real-world data-related problems with Python programming environment
Being able to manage the complexity of the program using the techniques functional and object-oriented programming

Expected Learning Outcomes

Loading data from external sources (.txt, .csv, .json, .sav, etc.)
Being able to scrape data from the web
Data wrangling and processing
Being able to write Python programs covering basic need of data scientist, namel Data visualisation
Being able to write Python programs covering basic need of data scientist, namel Models building

Course Contents

Topic 1. Review of Python's history and features. Expressions: objects and operators
Topic 2. Statements and modules
Topic 3. Network requests and web scrapping
Topic 4. Errors Handling. Input/output. Programming paradigms: Functional and object-oriented programming.
Topic 5. Linear algebra with NumPy and Data munging with Pandas
Topic 6. Visualization
Topic 7. Machine learning with Scikit-learn and StatsModels

Assessment Elements

Lab
Students’ progress is monitored during the course by one lab (for that purpose, LMS of HSE will be employed). Lab will follow the seminars. Student should not only reach the goal of the task but also pay attention to the code style and appropriate visualization.
Mid-term tests
Each test will follow seminars
Exam
Final exam consist of a task on data analysis. The duration of the final exam is two academic hours.

Interim Assessment

Interim assessment (3 module)
0.5 * Exam + 0.3 * Lab + 0.2 * Mid-term tests

Bibliography

Recommended Core Bibliography

Bernard, J. (2016). Python Recipes Handbook : A Problem-Solution Approach. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174476
Gerrard, P. (2016). Lean Python : Learn Just Enough Python to Build Useful Tools. [Place of publication not identified]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1360162

Recommended Additional Bibliography

Hetland, M. L. (2017). Beginning Python : From Novice to Professional (Vol. Third edition). New York: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174463

Course Syllabus