Data Science using Python: With Python Testing

What is Data Science?
Data science is an interdisciplinary field encompassing scientific methods, processes, and systems to extract knowledge or insights from data in various forms, either structured or unstructured. It draws principles from mathematics, statistics, information science, computer science, machine learning, visualization, data mining, and predictive analytics. However, it is fundamentally grounded in mathematics.
This book explains and applies the fundamentals of data science crucial for technical professionals such as DBAs and developers who are making career moves toward practicing data science. It is an example-driven book providing complete Python coding examples to complement and clarify data science concepts and enrich the learning experience.

How to Use This Book?
If you are already using Python for data science, just browse this book’s table of contents. You will probably find a bunch of things that you wish you knew how to do in Python. If so, feel free to turn directly to that chapter and get to work. Each lesson is, as much as possible, self-contained.
This book is more a workbook than a textbook.

If you aren’t using Python for data analysis, begin at the beginning. If you work your way through the whole workbook, you should have a better of the idea of how to use Python for data science when you are done.
If you know nothing at all about data science, this workbook might not be the place to start. However, give it a try and see how it works for you.
Table of Contents
1.Data Science Introduction
2.Why Python?
3.ETL with Python (Unstructured Data)
4.Reading and Writing Data in Python
5.Exceptions in Python
6.Debugging SyntaxErrors in Python
7.Semantic Errors in Python
8.Python Testing Best Practices
9.Writing Automated Tests for Python
10.Organizing Data in Python
11.Writing a Test Suite in Python
12.Machine Learning in Action
13.Getting Data Into and Out of Python
14.Calculating and Removing Outliers in Python
15.Python Data Analytics
16.Building Chatbots using Python
17.Common Design Patterns in Python

Getting Started with Data Science
Data science is not a single science as much as it is a collection of various scientific disciplines integrated for the purpose of analyzing data. These disciplines include various statistical and mathematical techniques, including:
1.Computer science
2.Data engineering
3.Visualization
4.Domain-specific knowledge and approaches

Why Used Python in Data Science?
•Python has several features that make it well suited for learning and doing data science. It’s free, relatively simple to code, easy to understand, and has many useful libraries to facilitate data science problem-solving. It also allows quick prototyping of virtually any data science scenario and demonstration of data science concepts in a clear, easy to understand manner.
•The goal of this Book is not to teach Python as a whole, but present, explain, and clarify fundamental features of the language (such as logic, data structures, and libraries) that help prototype, apply, and/or solve data science problems.

About the Author
Narendra Mohan Mittal is the Founder and Chairman of Thesis Scientist Pvt. Ltd. and he is working in the field of Data Science/big data/machine learning/deep learning space. He has more than 10 years in Research in the Big Data, Data Science and Machine learning.