Introduction

This tutorial provides an introduction to Natural Language Processing (NLP) for data science. NLP is a branch of artificial intelligence that gives machines the ability to read, understand, and derive meaning from human language in a valuable way. It's an essential tool in fields like machine learning, artificial intelligence, and data science.

By the end of this tutorial, you will have a basic understanding of NLP, its applications, and how it is used in various fields of data science.

Prerequisites:
- Basic understanding of Python programming language
- Familiarity with libraries like NLTK, Gensim, and SpaCy is helpful but not necessary

Step-by-Step Guide

NLP involves several core concepts that form the basis of understanding and manipulating human language. This includes Tokenization, Stemming, Lemmatization, POS (Part of Speech) Tagging, Named Entity Recognition (NER), and Text Classification.

Tokenization: It is the process of breaking down text into words, phrases, symbols, or other meaningful elements known as tokens.

Stemming: This involves reducing inflected (or sometimes derived) words to their word stem, base, or root form.

Lemmatization: Similar to stemming, but it brings context to the words. So it links words with similar meaning to one word.

POS Tagging: It is the task of marking up a word in a text as corresponding to a particular part of speech, based on its definition and its context.

NER: This is the process of locating named entities in the text and classifying them into predefined categories.

Text Classification: It involves assigning categories or classes to text according to its content.

Code Examples

We will use Python programming language for all the examples. Make sure you have Python and NLTK library installed.

Example 1: Tokenization

import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize

text = "Hello World. Welcome to NLP tutorial."
tokens = word_tokenize(text)
print(tokens)

In the above code, we import the necessary NLTK modules and tokenize a simple sentence. The expected output would be a list of tokens: ['Hello', 'World', '.', 'Welcome', 'to', 'NLP', 'tutorial', '.']

Example 2: POS Tagging

nltk.download('averaged_perceptron_tagger')
from nltk import pos_tag

text = word_tokenize("Hello World. Welcome to NLP tutorial.")
pos_tag(text)

Here we tag each token with a part of speech identifier. The output would be a list of tuples: [('Hello', 'NNP'), ('World', 'NNP'), ('.', '.'), ('Welcome', 'NNP'), ('to', 'TO'), ('NLP', 'NNP'), ('tutorial', 'NN'), ('.', '.')]

Summary

We have covered the basics of NLP, its applications, and how it is used in data science. We also explored some of the core NLP concepts and their Python code examples.

Practice Exercises

Exercise 1: Write a Python program to tokenize the following text and count the frequency of each word.
- Text: "Hello world. This is a test text for NLP tutorial."

Exercise 2: Write a Python program to perform POS tagging on the following text.
- Text: "The quick brown fox jumps over the lazy dog."

Exercise 3: Perform stemming and lemmatization on a text of your choice using NLTK.

Additional Resources

• NLTK Book: http://www.nltk.org/book/
• Natural Language Processing with Python: https://www.oreilly.com/library/view/natural-language-processing/9780596516499/
• Python Text Processing with NLTK 2.0 Cookbook: https://www.packtpub.com/product/python-text-processing-with-nltk-2-0-cookbook/9781849513609

Remember, practice is key in mastering NLP. Happy learning!