Download this EPUB book: Data Analysis with Python: Introducing NumPy, Pandas, Matplotlib, and Essential Elements of Python Programming 1st Edition, by Rituraj Dixit
An Absolute Beginner’s Guide to Learning Data Analysis Using Python, a Demanding Skill for Today
Key Features
● Hands-on learning experience of Python's fundamentals.
● Covers various examples of how to code end-to-end data analysis with easy illustrations.
● An excellent starting point to begin your data analysis journey with Python programming.
Description
In an effort to provide content for beginners, the book ‘Data Analysis with Python’ provides a concrete first step in learning data analysis. Written by a data professional with decades of experience, this book provides a solid foundation in data analysis and numerous data science processes. In doing so, readers become familiar with common Python libraries and straightforward scripting techniques.
Python and many of its well-known data analysis libraries, such as Pandas, NumPy, and Matplotlib, are utilized throughout this book to carry out various operations typical of data analysis projects.
Following an introduction to Python programming fundamentals, the book combines well-known numerical calculation and statistical libraries to demonstrate the fundamentals of programming, accompanied by many practical examples. This book provides a solid groundwork for data analysis by teaching Python programming as well as Python's built-in data analysis capabilities.
What you will learn
● Learn the fundamentals of core Python programming for data analysis.
● Master Python's most demanding data analysis and visualization libraries, including Pandas, NumPy, and Matplotlib.
● Refresh your step-by-step data analysis process with live examples.
● Extend your expertise to include real-time data analysis and the creation of simple Python scripts.
● Work with external files such as Excel, CSV, and others to clean them up for further analysis.
Who this book is for
This book is intended to help and teach college students and data professionals about Python's data analysis capabilities while also allowing them to work with Python tools.
Before diving into this book, working knowledge of Python is a definite plus.
Table of Contents
1. Introducing Python
2. Environment Setup for Development
3. Operators and Built-in Data Types
4. Conditional Expressions in Python
5. Loops in Python
6. Functions and Modules in Python
7. Working with Files I/O in Python
8. Introducing Data Analysis
9. Introducing Pandas
10. Introduction to NumPy
11. Introduction to Matplotlib
12. Connecting Dots Step by step Data Analysis Hands-on Use Case
Data is the fuel in the current information age. Data analysis is quickly becoming a popular topic due to the rapid growth and collection of data.
To comprehend data insights and uncover hidden patterns, we require a data analyst who can collect, understand, and analyze data that helps make data-driven decisions.
This book is the first step in learning data analysis for students. This book lays the groundwork for an absolute beginner in the field of Python Data Analysis. Because Python is the language of choice for data analysts and data scientists, this book covers the essential Python tools for data analysis.
For each topic, there are various hands-on examples in this book. This book's content covers the fundamentals of core Python programming, as well as Python's widely used data analysis libraries such as Pandas and NumPy, and the data visualization library matplotlib. It also includes the fundamental concepts and process flow of Data Analysis, as well as a real-time use case to give you an idea of how to solve real-time Data analysis problems.
This book is divided into 12 chapters. They will cover Python basics, Data Analysis, and Python Libraries for Data Analysis. Following are the details of the chapter's content.
Chapter 1 covers the introduction to Python; in this chapter, we will get information about the history of Python and its evaluation. Also, learn Python's various features and versions 1. x, 2. x, and 3. x. We discussed the real-time use cases of Python.
Chapter 2 covers the installation of Python and other Data Analysis Libraries in order to set up a Data Analysis environment.
Chapter 3 starts with the Python programming building blocks such as Variable in Python, Operators, Number, String, Boolean data types, Lists, Tuples, Sets, and Dictionaries. All the programing concepts have been explained with hands-on examples.
Chapter 4 will explore another essential programming construct, how to write conditional statements in Python. In this chapter, we will learn how to write the conditional instructions in Python using if…else, elif, and nested if. All the programing concepts have been explained with hands-on examples.
Chapter 5 covers the concepts of loops in Python. This chapter has a good explanation with appropriate hands-on examples for the while loop, for loop, and nested loops.
Chapter 6 will have content about the functions and modules in Python. It explained how to write the functions in Python and how to use them. Also, this chapter has information about the Python modules and other essential concepts of functional programming like lambda function, map(), reduce(), and filter() functions.
Chapter 7 will cover how to work with file I/O in Python. How to read and write on the external files with various modes and to save the data on file. All concepts have been explained with hands-on examples.
Chapter 8 covers the Introduction to Data Analysis fundamental concepts. This chapter discusses the data analysis concepts, why we need that, and the steps involved in performing a data analysis task. This chapter covers all the basic foundations we need to understand the real-time data analysis problem and the steps to solve the data analysis problem.
Chapter 9 covers the introduction to Pandas Library, a famous and vastly used Data Analysis Library. This chapter has a detailed explanation of features and methods provided by this Library with rich hands-on examples.
Chapter 10 covers the introduction to NumPy Library, a famous and vastly used Numerical Data Analysis Library. This chapter has a detailed explanation of features and methods provided by this Library with rich hands-on examples.
Chapter 11 covers the introduction to Matplotlib Library, a famous and vastly used Data Visualization Library. Data Visualization is a significant part of the Data Analysis process; it is always important to present the Data Analysis results or summaries with an appropriate visual graph or plot. This chapter has a detailed explanation of features and methods provided by this Library with rich hands-on examples of various types of graph plots.
Chapter 12 includes a data analysis use case with a given data set. This chapter has explained one data analysis problem statement and performed an end-to-end data analysis task with a step-by-step explanation to answer the questions mentioned in the problem statement so that learners can clearly understand how to analyze data in real-time.
About the Author
Rituraj Dixit is a seasoned software engineer who has been actively involved with developing solutions and architecting in the ETL, DWH, Big Data, Data on Cloud, and Data Science space for over a decade. He has worked with global clients and successfully delivered projects involving cutting-edge technologies such as Big Data, Data Science, Machine Learning, AI, and others.
He is passionate about sharing his experience and knowledge and has trained newcomers and professionals across the globe. Currently, he is Associated as a Technical Lead with Cognizant Technology Solutions, Singapore.
About the book:
Publisher : BPB Publications; 1st edition (August 20, 2022)
Publication date : August 20, 2022
Language : English
File : EPUB, 343P
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