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Магистратура 2021/2022

Python для науки о данных и искусственного интеллекта

Статус: Курс адаптационный (Бизнес-аналитика и системы больших данных)
Направление: 38.04.05. Бизнес-информатика
Когда читается: 1-й курс, 1 модуль
Формат изучения: с онлайн-курсом
Охват аудитории: для всех кампусов НИУ ВШЭ
Прогр. обучения: Бизнес-аналитика и системы больших данных
Язык: английский
Кредиты: 3

Course Syllabus

Abstract

Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries. During this MOOC bridging course (https://www.coursera.org/learn/python-for-applied-data-science-ai) you will learn Python fundamentals, including data structures and data analysis, complete hands-on exercises throughout the course modules, and create a final project to demonstrate your new skills. By the end of this course, you’ll feel comfortable creating basic programs, working with data, and solving real-world problems in Python.
Learning Objectives

Learning Objectives

  • Become familiar with key Python functions, objects, and classes
  • Learn about Python fundamentals, Python data structures, and working with data in Python
Expected Learning Outcomes

Expected Learning Outcomes

  • Student builds programs in Python.
  • Students uses key Python functions, objects, classes, and data structures for working with data in Python.
  • Student uses concepts of conditions and branching in Python programming.
  • Student can read and write files in Python.
Course Contents

Course Contents

  • Python Basics
    This module teaches the basics of Python and begins by exploring some of the different data types such as integers, real numbers, and strings. Continue with the module and learn how to use expressions in mathematical operations, store values in variables, and the many different ways to manipulate strings.
  • Python Data Structures
    This module begins a journey into Python data structures by explaining the use of lists and tuples and how they are able to store collections of data in a single variable. Next learn about dictionaries and how they function by storing data in pairs of keys and values, and end with Python sets to learn how this type of collection can appear in any order and will only contain unique elements.
  • Python Programming Fundamentals
    This module discusses Python fundamentals and begins with the concepts of conditions and branching. Continue through the module and learn how to implement loops to iterate over sequences, create functions to perform a specific task, perform exception handling to catch errors, and how classes are needed to create objects.
  • Working with Data in Python
    This module explains the basics of working with data in Python and begins the path with learning how to read and write files. Continue the module and uncover the best Python libraries that will aid in data manipulation and mathematical operations.
Assessment Elements

Assessment Elements

  • non-blocking Coursera Course Certificate or Written test
    Students who do not have a certificate of Coursera course will have to pass the written exam (test).
  • non-blocking Final written test
    The final score is rounded by dropping the fractional part.
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.5 * Coursera Course Certificate or Written test + 0.5 * Final written test
Bibliography

Bibliography

Recommended Core Bibliography

  • Eric Matthes. (2019). Python Crash Course, 2nd Edition : A Hands-On, Project-Based Introduction to Programming: Vol. 2nd edition. No Starch Press.

Recommended Additional Bibliography

  • Dr. Ossama Embarak. (2018). Data Analysis and Visualization Using Python : Analyze Data to Create Visualizations for BI Systems. Apress.
  • Плас Дж. Вандер. Python для сложных задач: наука о данных и машинное обучение. - Санкт-Петербург : Питер, 2018. - 576 с. - ISBN 978-5-496-03068-7. - URL: https://ibooks.ru/bookshelf/356721/reading (дата обращения: 12.10.2020). - Текст: электронный.