Data Science plays a very vital role in shaping up the process of transitioning data into information and into knowledge. As business enterprises, organizations, governments, IT companies, and service providers are keenly becoming data-driven, the role and responsibility of data scientists are bound to go up significantly. Python is emerging as the leading programming language for Data Science projects. The aim of the book is to clearly explain how Python simplifies and speeds up the realization of next-generation Data Science applications.
Data Science (DS) is a fast-emerging field of study and research. It leverages integrated data analytics (big, fast, and streaming analytics) platforms and Artificial Intelligence (AI) (machine and deep learning (ML/DL), computer vision (CV), and natural language processing (NLP)) algorithms extensively to extract actionable insights out of burgeoning data volumes in time.
Due to the ready availability of several libraries for facilitating the development of data science services, Python is turning out the programming language of choice for data science. The following libraries are enabling data science applications and are made available in Python:
1. NumPy: This is a library that makes a variety of mathematical and statistical operations easier and faster. This is also the basis for many features of the Pandas library.
2. Pandas: Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. This is one of the gamechangers for the tremendous success of data science projects.
3. Matplotlib: This is a visualization library that makes it quick and easy to generate charts from data.
4. Scikit-Learn: This is the most popular library for machine learning (ML) work in Python.
The book starts with a couple of chapters on data science and machine learning (ML) topics. Thereafter, the chapters are focusing on the fundamental and foundational aspects of Python programming language. All kinds of language constructs are accentuated and articulated for the benefit of programmers with all the practical details. There are dedicated chapters for producing machine learning applications. The gist of the book is to clearly explain how Python simplifies and speeds up the realization of next-generation data science applications. All the specific libraries towards data science are given the required thrust in order to empower our esteemed readers with all the right and relevant information. This book is being prepared with the intention of empowering data scientists with all the vital details about programming using the Python language.
Author(s): A. Suresh, N.Malarvizhi, Pethuru Raj
Publisher: Arcler Press
Year: 2022
Language: English
Pages: 346
Cover
Title Page
Copyright
ABOUT THE AUTHORS
TABLE OF CONTENTS
List of Figures
List of Tables
List of Abbreviations
Preface
Chapter 1 The Distinctions of Python Language
1.1. Introduction
1.2. Web Application Development
1.3. Game Development
1.4. Artificial Intelligence (AI) Applications
1.5. Graphical User Interfaces (GUIS)
1.6. Computer Vision (CV) Applications
1.7. Audio And Video Applications
1.8. Knowledge Visualization Applications
1.9. Scientific and Numeric Applications
1.10. IoT and CPS Applications
1.11. Data Analytics
1.12. Python For Blockchain Apps
1.13. Conclusion
Chapter 2 Demystifying the Data Science Paradigm
2.1. Introduction
2.2. Briefing Data Analysis
2.3. Entering Into Data Science (DS)
2.4. The Lifecycle of a Data Science (DS) Project
2.5. The Prominent Use Cases of Data Science (DS)
2.6. Machine Learning (Ml) Algorithms
2.7. Key Machine Learning (Ml) Algorithms
2.8. Ensemble Learning Algorithms
2.9. Steps to Build a Random Forest (RF)
2.10. Time Series Forecasting
2.11. Time Series Forecasting Methods
2.12. Time Series Forecasting Applications
2.13. Clustering Algorithms
2.14. Case Study: Diabetes Prevention
2.15. Conclusion
Chapter 3 Python for Data Analysis
3.1. Python for Data Analysis
3.2. Python Libraries
3.3. Scientific Libraries in Python-Numpy, Scipy, Matplotlib, and Pandas
3.4. Machine Learning (Ml)
3.5. Machine Learning (Ml) With Internet of Things (IoT)
3.6. Machine Learning (Ml) Application With IoT
3.7. Algorithm
3.8. Building Blocks of Algorithms (Instructions/Statements, State, Control Flow, Functions)
3.9. Notation (Pseudocode, Flow Chart, Programming Language)
3.10. Algorithmic Problem Solving
3.11. Flow of Control
3.12. Illustrative Program
Chapter 4 Python Programming: An Introduction
4.1. Introduction to Python
4.2. Downloading and Installing Python 3.6.2
4.3. Python Interpreter and Interactive Mode
4.4. Values and Types: Int, Float, Boolean, String, and List
4.5. Variables
4.6. Keywords
4.7. Statements and Expressions
4.8. Comments
4.9. Input and Output
4.10. Operators
Chapter 5 Functions
5.1. Function Definition
5.2. Built-In Functions
5.3. Math Functions
5.4. User Defined Function
5.5. Function Prototypes
5.6. Return Statement
5.7. Modules
Chapter 6 Control Structures
6.1. Boolean Values
6.2. Conditional Statements
6.3. Iteration/Control Statements
6.4. Loop Control Statements
6.5. Fruitful Functions
6.6. Local and Global Scope
6.7. Function Composition
6.8. Recursion
Chapter 7 Strings
7.1. String Definition
7.2. Operations On String
7.3. String Methods
7.4. String Module
7.5. List As Array
7.6. Searching
Chapter 8 Lists
8.1. Lists
8.2. List Operations
8.3. List Slices
8.4. List Methods
8.5. List Loop
8.6. Mutability
8.7. List Aliasing
8.8. Cloning Lists
8.9. List Parameters
8.10. Deleting List Elements
8.11. Python Functions For List Operations
8.12. List Comprehension
Chapter 9 Tuples
9.1. Tuples
9.2. Tuple Methods
9.3. Other Tuple Operations
9.4. Tuples As Return Values
9.5. Built-In Functions With Tuple
9.6. Variable-Length Argument Tuples
9.7. Comparing Tuples
Chapter 10 Dictionaries
10.1. Dictionaries
10.2. Built-In Dictionary Functions and Methods
10.3. Access, Update, and Add Elements in Dictionary
10.4. Delete or Remove Elements From a Dictionary
10.5. Sorting a Dictionary
10.6. Iterating Through a Dictionary
10.7. Reverse Lookup
10.8. Inverting a Dictionary
10.9. Memoization (MEMOS)
Chapter 11 Files
11.1. Files
11.2. Errors and Exception
Chapter 12 Modules and Packages
12.1. Modules
12.2. Packages
Chapter 13 Classes in Python
13.1. Introducing the Concept of Classes in Python
13.2. Object
13.3. Methods
13.4. Inheritance
13.5. Encapsulation
13.6. Polymorphism
Index
Back Cover
