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Data Analysis with Python

2018/2019
Учебный год
ENG
Обучение ведется на английском языке
2
Кредиты
Статус:
Курс по выбору
Когда читается:
1-й курс, 3 модуль

Преподаватель


Нагорный Олег Станиславович

Course Syllabus

Abstract

Python is one of the most popular and rapidly developing programming languages. A clear syntax which facilitates learning and a plethora of built-in and third-party libraries made Python especially popular among academics and researchers of all kinds. Python has already been the first-choice language in Machine Learning and Data Science for a while, but as far as Social Sciences are becoming more digitally-oriented it is getting in demand by sociologists, economists, linguists, and other social researchers. This course is created for students who want to learn how to solve real-world data-related problems with Python programming environment but have no experience in programming. The course syllabus covers most of Python functionality from basics syntax to the modern libraries for machine learning and data analysis.
Learning Objectives

Learning Objectives

  • Learn how to solve real-world data-related problems with Python programming environment
  • Being able to manage the complexity of the program using the techniques functional and object-oriented programming
Expected Learning Outcomes

Expected Learning Outcomes

  • Loading data from external sources (.txt, .csv, .json, .sav, etc.)
  • Being able to scrape data from the web
  • Data wrangling and processing
  • Being able to write Python programs covering basic need of data scientist, namel Data visualisation
  • Being able to write Python programs covering basic need of data scientist, namel Models building
Course Contents

Course Contents

  • Topic 1. Review of Python's history and features. Expressions: objects and operators
  • Topic 2. Statements and modules
  • Topic 3. Network requests and web scrapping
  • Topic 4. Errors Handling. Input/output. Programming paradigms: Functional and object-oriented programming.
  • Topic 5. Linear algebra with NumPy and Data munging with Pandas
  • Topic 6. Visualization
  • Topic 7. Machine learning with Scikit-learn and StatsModels
Assessment Elements

Assessment Elements

  • non-blocking Lab
    Students’ progress is monitored during the course by one lab (for that purpose, LMS of HSE will be employed). Lab will follow the seminars. Student should not only reach the goal of the task but also pay attention to the code style and appropriate visualization.
  • non-blocking Mid-term tests
    Each test will follow seminars
  • non-blocking Exam
    Final exam consist of a task on data analysis. The duration of the final exam is two academic hours.
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.5 * Exam + 0.3 * Lab + 0.2 * Mid-term tests
Bibliography

Bibliography

Recommended Core Bibliography

  • Bernard, J. (2016). Python Recipes Handbook : A Problem-Solution Approach. [United States]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174476
  • Gerrard, P. (2016). Lean Python : Learn Just Enough Python to Build Useful Tools. [Place of publication not identified]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1360162

Recommended Additional Bibliography

  • 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