2023/2024
Python для финансистов
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:
Маго-лего
Кто читает:
Школа финансов
Когда читается:
1, 2 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Васильев Глеб Альбертович
Язык:
английский
Кредиты:
6
Контактные часы:
56
Course Syllabus
Abstract
This is an introductory course on programming in Python, one of the most popular data-centric programming languages widely used across industries and in the academic environment. The increased demand for decision making based on insights from data results in an increased demand for qualified experts with a strong data analysis skillset. With this in mind, starting from language fundamentals, we will concentrate on practical approaches to solving basic problems, from collecting and importing data to generating reports. The main goal of the course is to provide the students with programming toolbox, form competence in basic Python as well as data-related Python libraries, and also prepare the students for studying more advanced topics and conducting rigorous empirical analyses on their own.
Learning Objectives
- The course is aimed at developing basic Python programming skills necessary for data analysis. Upon completion, students will be able to use Python in their analytical work and complete all the essential steps of data engineering and analysis, from gathering, loading, and transforming data to building simple models and generating reports.
Expected Learning Outcomes
- Be able to write Python code
- Be able to import data, including typical financial data
- Be able to transform data and merge multiple datasets
- Be able to draw basic plots
- Be able to present the results of data analysis in Jupyter notebooks.
Course Contents
- Introduction to Python
- Data Manipulation With Pandas
- Intermediate Data Manipulation With Pandas
- Importing Data in Python
- Working with dates and times in Python. Strings in Python
- Visualizing Data With Matplotlib and Seaborn
- Exploratory Data Analysis in Python. Cleaning Data
- Writing Functions
- Basic Web Scraping in Python
- Basic Predictive Modelling Toolbox
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
- G. Nair, V. (2014). Getting Started with Beautiful Soup. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=691839
- Romano, F. (2015). Learning Python. Birmingham: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1133614