Введение в Python для наук о данных

2021/2022

Учебный год

RUS

Обучение ведется на русском языке

3

Кредиты

Кто читает:

Департамент экономики и финансов

Статус:

Курс обязательный

Когда читается:

2-й курс, 2 модуль

Преподаватель

Собянин Кирилл Валентинович

Программа дисциплины

Аннотация

The course provides students with wide general overview of Python – a general-purpose programming language that is becoming ever more popular for data science. The focus is on the application of Python specifically for data science. The course is about ways to import, store and manipulate data, and helpful data science tools to conducting data analyses. The course is intended for students with little programming background. The learning process is facilitated with DataCamp platform.

Цель освоения дисциплины

• The main objective of the course is to provide students with the basic concepts of Python, its syntax, functions and packages to enable them to write scripts for data manipulation and analysis. The course develops skills of writing and running a code using Python. The course covers various variables types and their features, basic operators and statements, loops, as well as the main packages for data science: NumPy, Pandas, Matplotlib. At the end of the course, students should be able to write short scripts to import, prepare and analyze data.

Планируемые результаты обучения

• Know basic data types in Python.
• Know how to import data in Python.
• Know how to work in Jupyter Notebook.
• Know operators, how to clean and merge datasets.
• Know pandas library, the main methods for DataFrames.

Содержание учебной дисциплины

• Introduction to Python for Data Science
• Intermediate Python for Data Science
• Python DataFrames
• Importing Data in Python
• Environment for scientific programming in Python

Элементы контроля

• Self-study work
• Exam
  Final student assessment is a project, that is performed in a team of no more than 2 people. Each team uses provided dataset of collets their own data, define research question and apply one or a combination of the learnt methods of data analysis with Spreadsheets. As a result of the project each team write down the report and prepare working file. The grade for the exam includes the grade for the report, grade for the working file and the grade for answering questions.

Промежуточная аттестация

• 2021/2022 учебный год 2 модуль
  0.5 * Exam + 0.5 * Self-study work

Список литературы

Рекомендуемая основная литература

• Vanderplas, J. T. (2016). Python Data Science Handbook : Essential Tools for Working with Data (Vol. First edition). Sebastopol, CA: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1425081

Рекомендуемая дополнительная литература

• Seemon Thomas. (2014). Basic Statistics. [N.p.]: Alpha Science Internation Limited. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1663598