Programming (Python)

2021/2022

Учебный год

ENG

Обучение ведется на английском языке

4

Кредиты

Кто читает:

Кафедра общего и стратегического менеджмента (Нижний Новгород) (Факультет менеджмента (Нижний Новгород))

Статус:

Курс обязательный

Когда читается:

1-й курс, 2 модуль

Преподаватель

Поршнев Александр Валерьевич

Course Syllabus

Abstract

This course will introduce the main data structures of the Python programming language applied in Data Analysis and Text Analytics. We will move to the basics of procedural programming and explore how we can use the Python built-in data structures such as lists, dictionaries, and tuples to perform simple natural language processing and data analysis.

Learning Objectives

• The course is primarily aimed at helping to start use the Python for Text Analysis and will allow them to plan, design, and conduct natural language processing. The course focuses on the following competencies: programming and natural language processing. The course contains three main topics – Getting started with Python, NLTK and Categorizing and Tagging.

Expected Learning Outcomes

• Ability to create a program on Python to made frequency analysis (words, n-grams, stems, normalized words, parts of speech, sentiment markers for the set of English and Russian texts.
• Ability to create a program on Python to made simple program using lists, dictionaries and strings.
• Ability to use NLTK library in your program on Python

Course Contents

• Categorizing and Tagging
• NLTK Library for text analytics
• Computing with Language: Texts and Words.

Assessment Elements

• Programming task
• Programming task 0

Interim Assessment

• 2021/2022 2nd module
  0.7 * Programming task + 0.3 * Programming task 0

Bibliography

Recommended Core Bibliography

• Wagner, W. (2010). Steven Bird, Ewan Klein and Edward Loper: Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit. Language Resources & Evaluation, 44(4), 421–424. https://doi.org/10.1007/s10579-010-9124-x

Recommended Additional Bibliography

• Beysolow, T. (2018). Applied Natural Language Processing with Python : Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1892182
• Bhavsar, K., Kumar, N., & Dangeti, P. (2017). Natural Language Processing with Python Cookbook : Over 60 Recipes to Implement Text Analytics Solutions Using Deep Learning Principles. Packt Publishing.
• Dipanjan Sarkar. (2019). Text Analytics with Python : A Practitioner’s Guide to Natural Language Processing: Vol. Second edition. Apress.
• Nirant Kasliwal. (2018). Natural Language Processing with Python Quick Start Guide : Going From a Python Developer to an Effective Natural Language Processing Engineer. Packt Publishing.
• Perkins, J. (2010). Python Text Processing with NLTK 2.0 Cookbook : Over 80 Practical Recipes for Using Python’s NLTK Suite of Libraries to Maximize Your Natural Language Processing Capabilities. Packt Publishing.