Master
2021/2022
Data Analysis in Python
Type:
Compulsory course (Economics and Economic Policy)
Area of studies:
Economics
Delivered by:
Department of Applied Economics
Where:
Faculty of Economic Sciences
When:
1 year, 1-3 module
Mode of studies:
distance learning
Open to:
students of one campus
Instructors:
Konstantin Lvovich Polyakov
Master’s programme:
Economics and Economic policy
Language:
English
ECTS credits:
3
Contact hours:
4
Course Syllabus
Abstract
This course will introduce fundamental programming concepts including data structures, networked application program interfaces, and databases, using the Python programming language. In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas and so on. The course is completely based on the specialization courses https://www.coursera.org/specializations/python#about: 1. Programming for Everybody (Getting Started with Python) https://www.coursera.org/learn/python?specialization=python 2. Data structures in Python https://www.coursera.org/learn/python-data?specialization=python 3. Using Python to Access Web Data https://www.coursera.org/learn/python-network-data?specialization=python 4. Using Databases with Python https://www.coursera.org/learn/python-databases?specialization=python 5. Capstone: Retrieving, Processing, and Visualizing Data with Python https://www.coursera.org/learn/python-data-visualization?specialization=python
Learning Objectives
- Learn how to get and analyze data using Python
- This course will take you from the basics of Python to exploring many different types of data.
Expected Learning Outcomes
- Accessing New Data Sources (Project)
- Basic Structured Query Language
- Building a Search Engine in Python
- Data Models and Relational SQL
- Databases and Visualization
- Definition of Functions in Python
- Exploring Data Sources (Project)
- Files processing in Python
- Installing and Using Python
- JSON and the REST Architecture
- Loops and Iteration in Python
- Many-to-Many Relationships in SQL
- Networks and Sockets in Python
- Object Oriented Programming in Python
- Programs that Surf the Web
- Regular Expressions in Python
- Spidering and Modeling Email Data
- Strings processing in Python
- The concept "Conditional Code"
- The concept of "dictionary" in Python
- The concept of "list" in Python
- The concept of "tuple" in Python
- Variables and Expressions
- Visualizing Email Data in Python
- Visualizing new Data Sources (Project)
- Web Services and XML in Python
Course Contents
- Programming for Everybody (Getting Started with Python)
- Data structures in Python
- Using Python to Access Web Data
- Using Databases with Python
- Retrieving, Processing, and Visualizing Data with Python
Assessment Elements
- The Final Exam
- Independent work
- Exploring Data Sources (Project)see https://www.coursera.org/learn/python-data-visualization?specialization=python
- Accessing New Data Sources (Project)see https://www.coursera.org/learn/python-data-visualization?specialization=python
- Visualizing new Data Sources (Project)see https://www.coursera.org/learn/python-data-visualization?specialization=python
Bibliography
Recommended Core Bibliography
- Severance, C. (2016). Python for Everybody : Exploring Data Using Python 3. Place of publication not identified: Severance, Charles. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsotl&AN=edsotl.OTLid0000336
Recommended Additional Bibliography
- Fabrizio Romano. (2018). Learn Python Programming : The No-nonsense, Beginner’s Guide to Programming, Data Science, and Web Development with Python 3.7, 2nd Edition: Vol. 2nd ed. Packt Publishing.