Are you tired of feeling like you're stuck in a dead-end job with no room for growth or advancement?
Are you ready to take your career to the next level and start making real money?
Look no further than "Mastering Data Analysis with Python."
This comprehensive guide is designed to teach you the skills you need to become a top-paying data analyst. With a focus on the powerful Python programming language, you'll learn how to collect, clean, and analyze data like a pro. But that's not all - you'll also discover how to use this data to make informed business decisions and drive real results.
KEY FEATURES
How to use Python's built-in libraries to manipulate and analyze data like a pro
Techniques for cleaning and prepping data for analysis
Advanced data visualization techniques to help you communicate your findings
How to use statistical methods to draw meaningful insights from your data
And much more!
WHO THIS BOOK IS FOR?
Data analysts and scientists who want to learn how to use Python for data analysis
Programmers who want to add data analysis skills to their repertoire
Anyone interested in exploring and visualizing data using Python
Students and professionals looking to improve their data analysis and visualization skills
Individuals interested in machine learning and artificial intelligence who need to learn data analysis fundamentals.
WHAT OTHER PEOPLE SAYS
But don't just take our word for it. Here's what some of our readers have had to say
"I've been working as a data analyst for a few years now, but this book taught me so many new techniques that I was able to immediately apply to my job and start making more money."
"I've always been interested in data analysis, but I didn't know where to start. This book is the perfect introduction to the field and has helped me land my dream job."
"I was able to use the skills I learned in this book to negotiate a raise and make an additional $100,000 per year!"
OUTCOMES
Gain proficiency in NumPy, Pandas, and Matplotlib
Learn to handle data effectively using Python
Develop the skills to perform exploratory data analysis and data visualization
Acquire the knowledge to build predictive models and perform statistical analysis
Learn to handle large datasets and work with real-world data
Master the skills to communicate data insights effectively
Gain confidence in using Python for data analysis and visualization
Author(s): Rajender Kumar
Publisher: Jamba Academy
Year: 2023
Language: English
Commentary: not true
Pages: 532
Found Typos & Broken Link
Support
Disclaimer
Acknowledgments
How to use this book?
Conventions Used in This Book
Get Code Examples Online
About the Author
Other Work By the Same Author
Who this book is for?
What are the requirements? (Pre-requisites)
Preface
Why Should You Read This Book?
Mastering Data Analysis with Python
1 Introduction to Data Analysis with Python
1.1 Understanding the basics of data analysis
1.2 Types of data
1.3 Source of data
1.4 Format of data
1.5 Benefits of Data Analysis
1.6 Data Analysis Use Cases
1.7 Summary
1.8 Test Your Knowledge
1.9 Answers
2 Getting Started with Python
2.1 Installing Python
2.2 Setting up Jupyter Notebook
2.3 Magic Commands in Jupyter
2.4 Installing Required Libraries
2.5 Basics of Python Language
2.6 Control Flow
2.7 Introduction to the Python data analysis libraries (NumPy, Pandas, Matplotlib)
2.8 Summary
2.9 Test Your Knowledge
2.10 Answers
3 Built-in Data Structures, Functions, and Files
3.1 Built-in Data Structures
3.2 Built-in Functions
3.3 Anonymous Functions
3.4 Defining a Function
3.5 Namespace and scope of a Function
3.6 Handling Files in Python
3.7 Exception Handling
3.8 Debugging Techniques
3.9 Best Practices for Writing Python Code
3.10 Summary
3.11 Test Yourr Knowledge
3.12 Answers
4 Data Wrangling
4.1 Introduction to Data Wrangling
4.2 Data Cleaning
4.3 Data transformation and reshaping
4.4 Data Validation
4.5 Time Series Analysis
4.6 Best Practices for Data Wrangling
4.7 Summary
4.8 Test Your knowledge
4.9 Answers
5 NumPy for Data Analysis
5.1 Introduction to NumPy and its data structures
5.2 manipulating NumPy arrays
5.3 Broadcasting
5.4 Mathematical operations and linear algebra with NumPy
5.5 Random Sampling & Probability Distributions
5.6 Use of Numpy in Data Analyis
5.7 Best Practices & Performance Tips for Using NumPy in Data Analysis
5.8 Summary
5.9 Test Your Knowledge
5.10 Answers
6 Pandas for Data Analysis
6.1 Introduction to Pandas and its Data Structures
6.2 Reading & Writing to Files Using Pandas
6.3 Basic DataFrame operations
6.4 Indexing and Selection
6.5 Data Cleaning and Transformation
6.6 Data Exploration and Visualization
6.7 Merging and Joining Data
6.8 Data Aggregation With Pandas
6.9 Advanced String Manipulation
6.10 Time Series Analysis Using Pandas
6.11 Best Practices for using Pandas in Data Analysis
6.12 Summary
6.13 Test Your Knowledge
6.14 Answers
7 Descriptive Statistics for Data Analysis
7.1 Descriptive Statistics
7.2 Measures of Central Tendency (Mean, Median, Mode)
7.3 Measures of Spread/Shape
7.4 Frequency Distributions
7.5 Box and Whisker Plots
7.6 Measures of Association
7.7 Real-world Applications of Descriptive Statistics
7.8 Best Practices for Descriptive Statistical Analysis
7.9 Summary
7.10 Test Your Knowledge
7.11 Answers
8 Data Exploration
8.1 Introduction to Data Exploration
8.2 Univariate Analysis
8.3 Bivariate Analysis
8.4 Multivariate Analysis
8.5 Identifying Patterns and Relationships
8.6 Best Practices for Data Exploration
8.7 Summary
8.8 Test Your Knowledge
8.9 Answers
9 Matplotlib for Data visualization
9.1 Matplotlib and its architecture
9.2 Plotting with Matplotlib
9.3 Customizing plots with Matplotlib
9.4 Working with multiple plots and subplots
9.5 Advanced plot types and features
9.6 Best practices for using Matplotlib
9.7 Summary
9.8 Test Your Knowledge
9.9 Answers
10 Data Visualization
10.1 Data Visualization & Its Importance
10.2 Types Of Data Visualization And When To Use Them
10.3 Advanced Data Visualization Techniques
10.4 Choosing The Right Visualization For Your Data
10.5 Data Storytelling And Communication
10.6 Customizing And Enhancing Plots To Effectively Communicate Insights
10.7 Real-World Examples Of Data Visualization In Industry And Research
10.8 Summary
10.9 Test Your Knowledge
10.10 Answers
11 Data Analysis in Business
11.1 Data Governance
11.2 Data Quality
11.3 Business Intelligence & Reporting
11.4 Applications of Data Analysis
11.5 Summary
11.6 Test Your Knowledge
11.7 Answers
A. Additional Resources for Further Learning
Books And Ebooks
Websites And Blogs
Community Forums And Groups
Online Courses and Certifications
Data Analysis Conferences and Meetups
Data Analysis Tools and Software
Cnoclusion
B. Insider Secrets for Success as A Data Analyst
Tips for Success in Data Analysis
Data Analysis Careers and Professional Resources
Find a Job as a Data Analyst
C. Glossary
A Humble Request for Feedback!
