Analytics of financial business processes

2024/2025

Учебный год

ENG

Обучение ведется на английском языке

Статус:

Курс по выбору

Когда читается:

1-й курс, 4 модуль

Course Syllabus

Abstract

Nowadays, organizations use databases and corporate warehouses to store and track information about their operations, customers, etc. An important skill of an analyst is the ability to extract this data, aggregate and visualize it. This course is dedicated to mastering modern data analysis tools: SQL and Python libraries (Numpy, Pandas, Matplotlib, etc.). It aims to develop practical skills in working with data, including exploratory analysis and preparing datasets for building ML models. The course comprises 16 hours of lectures and 16 hours of seminars.

Learning Objectives

• Introduction to relational database management systems with MS SQL Server as an example
• Learning solid basis of SQL querying
• Exploring Window Functions
• Acquiring bases in Pandas and Numpy for data transformation, including Effective Pandas, as well as simple data visualization

Expected Learning Outcomes

• Understanding of the specifics of storing and interacting with data in relational DBMSs
• Ability to write SQL queries in a relational DBMS
• Knowledge of the basic principles and methods of working with data in Python using the Numpy and Pandas libraries

Course Contents

• Lecture 1. MS SQL Server and SQL foundations.
• Lecture 2. Single-Table Queries, Data types and built-in functions
• Lecture 3. Table Joins and Set operators.
• Lecture 4. Subqueries and table expressions.
• Lecture 5. Window functions.
• Lecture 6. Crash course into Pandas and Numpy
• Lecture 7. Effective Pandas.
• Lecture 8. Data visualization in Python.

Assessment Elements

• In class test 1
• Exam
• In class test 2

Interim Assessment

• 2024/2025 4th module
  0.5 * Exam + 0.25 * In class test 1 + 0.25 * In class test 2

Bibliography

Recommended Core Bibliography

• McKinney, W. (2012). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython. Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=495822
• McKinney, W. (2018). Python for Data Analysis : Data Wrangling with Pandas, NumPy, and IPython (Vol. Second edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1605925

Recommended Additional Bibliography

• Date, C. J. (2015). SQL and Relational Theory : How to Write Accurate SQL Code (Vol. Third edition). Sebastopol, CA: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1099367

Authors

• SOLOVEVA EKATERINA EVGENEVNA