Программирование в Python: краткий курс

Магистратура 2018/2019

Статус: Курс по выбору (Финансы)

Направление: 38.04.08. Финансы и кредит

Кто читает: Департамент экономики и финансов

Где читается: Факультет экономики, менеджмента и бизнес-информатики

Когда читается: 1-й курс, 3, 4 модуль

Формат изучения: с онлайн-курсом

Преподаватели: Паршаков Петр Андреевич

Прогр. обучения: Финансы

Язык: английский

Кредиты: 3

Full Syllabus

Abstract

The course introduces the basics of programming using Python. The key goal of this course is to provide students with theoretical knowledge and practical experience to be able to analyze data with Python. It covers topics about data types, import, manipulation, analysis and export of data. The course is blended. The online video lectures are provided by Wesleyan University and taught by Bill Boyd. The seminars are provided by lecturers of Higher School of Economics.

Learning Objectives

The course aims at providing students with competences in data analysis using Python.

Expected Learning Outcomes

Understand the main steps of data analysis.
Know basic syntax of Python programming language.
Be able to import and explore data using Python.
Transform and manipulate data using Python.
Apply different techniques to make preliminary analysis of data.
Be able to export data using Python.
Extract insights based on data analysis.

Course Contents

Beginning to Program in Python
Welcome and introduction. Introduction to the Spyder IDE. Arithmetic operations. Our first functions. Creating strings and using them in print statements. The "input" statement and combining strings. Using the "if" statement. Converting strings to numbers. Using the remainder operator. Introduction to loops - the "while" loop. The "for" loop; tracking down errors.
Working with Lists and Importing Libraries. The Random library
Working with Lists and Importing Libraries. The Random library. Lists, datatypes, libraries, the random library. Introduction to lists. Lists continued. Stepping through lists using loops. Introduction to datatypes. Converting datatypes. Working with lists of sublists. Lists continued. Introduction to libraries. The random library. More on Loops and Append.
Tuples, Data Dictionaries, Text and CSV Files
Using tuples and data dictionaries. Reading and writing files. Running Python Programs (Windows). Installing Environments on a Mac. Running Python Programs (Mac). Writing scripts in Python. Reading and writing CSV files.
Functional Values, Sorting, Formatting, Statistics, and a Menu Driven Database Program
Long strings, random library, building and sorting lists. Descriptive statistics. Formatting print statements. Starting the database application. Displaying the records. Adding and deleting records. Editing records. Saving records to a CSV file. Loading the records from the CSV file.

Assessment Elements

Self-study work
Exam

Interim Assessment

Interim assessment (4 module)
0.7 * Exam + 0.3 * Self-study work

Bibliography

Recommended Core Bibliography

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

Recommended Additional Bibliography

Fletcher, S., & Gardner, C. (2009). Financial Modelling in Python. Chichester, West Sussex: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=346409
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