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Introduction to Python for Data Science

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс обязательный
Когда читается:
2-й курс, 1, 2 модуль

Преподаватели

Course Syllabus

Abstract

Python is an interpreted high-level general-purpose programming language. It has a set of powerful libraries for data analysis. It is a simple language for beginners to learn, though it is powerful enough for writing large applications. This 2-module course is an introduction to the Python programming language and data science. The average time to complete this course depends on student background. To complete the course, students are supposed to have mathematical skills at the high school level. Students’ academic performance is evaluated using programming assignments: homework and classwork. Also there is one mid-semester exam and final exam. The examples and problems used in this course cover such areas as text processing, HTML and data analytics. This course does not provide lectures and students must finish corresponding week on Coursera course https://www.coursera.org/learn/python-kak-inostrannyj (In Russian) before seminar class.
Learning Objectives

Learning Objectives

  • teach students how to create basic scripts, understand data types, statements and logical expressions; create own functions and use libraries.
Expected Learning Outcomes

Expected Learning Outcomes

  • Student can explain basic principles of Python programming language
  • Student can read and understand simple scripts.
  • Student can create scripts for data analysis
Course Contents

Course Contents

  • Basic of Python programming
  • Boolean data type and IF conditions
  • WHILE loops
  • Lists and FOR loops
  • Methods
  • Dictionaries
  • Nested data structures. Sorting
  • Functions
  • Additional chapters: pandas
  • Text files and tables
  • Scraping: collection of links from website
  • Additional chapters: re
  • Additional chapters: graphs
Assessment Elements

Assessment Elements

  • non-blocking Mid-semester exam
  • non-blocking Homework
  • non-blocking Classwork
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.15 * Classwork + 0.4 * Exam + 0.25 * Homework + 0.2 * Mid-semester exam
Bibliography

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

  • 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