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Data Analysis Foundations with Python
Master Python and Data Analysis using NumPy, Pandas, Matplotlib, and Seaborn
Unleash Python for data analysis! This book equips you with the essential tools to clean, manipulate, and visualize data using Python libraries like Pandas and NumPy.
Improve your programming skills
What You'll Get from This Book
18 chapters spanning over 470 pages
More than 450 explanatories blocks of code
More than 48 practical exercises
7 Quizzes to test your knowledge
3 Practical "Real World" Projects
About thIS book
Master Data Analysis Basics with Python
Transform your raw data into valuable insights with "Data Analysis Foundations with Python." This comprehensive guide equips you with the essential skills and knowledge to navigate the world of data analysis, using Python as your powerful tool.
Through engaging explanations, practical exercises, and real-world examples, you'll gain a thorough understanding of fundamental concepts like:
- Data Cleaning and Manipulation: Learn how to prepare your data for analysis by cleaning inconsistencies, handling missing values, and transforming it into a usable format.
- Exploratory Data Analysis (EDA): Master the art of exploring your data, uncovering hidden patterns, and gaining valuable insights through techniques like visualization and summary statistics.
- Data Wrangling with Python Libraries: Discover powerful libraries like pandas and NumPy that streamline data manipulation tasks, enabling you to work with large datasets efficiently.
Become a Data Analyst with Python Fundamentals
Through engaging exercises and real-world projects, you'll delve into the essential building blocks of data analysis:
- Data Import and Exploration: Learn how to import data from various sources, explore its structure and content, and identify any potential issues.
- Data Visualization: Master the art of creating informative visualizations using libraries like Matplotlib and Seaborn, effectively communicating insights to both technical and non-technical audiences.
- Statistical Analysis with Python: Understand core statistical concepts and apply them to your data using Python libraries, enabling you to draw meaningful conclusions and make informed decisions.
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By the end of this journey, you'll be equipped with the foundational knowledge and practical skills to confidently approach data analysis challenges. Whether you're pursuing a career as a data analyst or simply seeking to extract valuable insights from your own data, this book empowers you to unlock the power of Python and become a data-driven individual.
Unlock the potential of your data and embark on a rewarding career path with "Data Analysis Foundations with Python." This practical guide provides you with the fundamental skills and knowledge necessary to become a data analyst using Python, one of the most popular languages in the data science field.
By mastering these crucial foundations, you'll be well-equipped to confidently approach any data analysis project. This book empowers you to not only understand the concepts but also apply them to real-world scenarios, setting you on the path to becoming a proficient data analyst.
Data analysis is fundamental in today’s data-driven world, influencing industries like finance, healthcare, marketing, and artificial intelligence. Due to its versatility and extensive libraries, Python has become the go-to language for data analysis. Data Analysis Foundations with Python provides a structured and hands-on approach to working with data. It is an essential resource for anyone looking to develop data-driven decision-making skills, whether for business intelligence, research, or AI applications.
This book teaches you how to efficiently clean, manipulate, visualize, and analyze data using Python’s most powerful libraries, including Pandas, NumPy, and Matplotlib. You will learn how to structure datasets, perform exploratory data analysis, and extract meaningful insights. With practical exercises and real-world case studies, you will develop the ability to handle real datasets confidently, equipping you with the skills to solve data-related problems in professional environments.
Unlike general Python programming books, Data Analysis Foundations with Python focuses on data manipulation and analytical techniques. It combines practical exercises, real-world datasets, and clear explanations to bridge the programming and analytical thinking gap. Whether you’re working with spreadsheets, databases, or large datasets, this book teaches you how to turn raw data into valuable insights using Python.
A basic understanding of Python is recommended, but no prior data analysis experience is required. The book starts with foundational concepts and gradually builds up to more advanced techniques, making it accessible for beginners and a valuable resource for those looking to solidify their data analysis skills.
Access to the Cuantum Technologies VIP customer service, with a dedicated team of developers ready to answer all your questions. A code repository with fully working examples and pre-tested, production-ready code. The Success University e-learning platform, where you can access additional resources and free video content to reinforce your learning. Regular updates and additional materials to stay updated with new advancements in data analysis and Python programming.
Table of contents
Chapter 1: Introduction to Data Analysis and Python
1.1 Importance of Data Analysis
1.2 Role of Python in Data Analysis
1.3 Overview of the Data Analysis Process
1.4 Practical Exercises for Chapter 1: Introduction to Data Analysis and Python
1.5 Chapter 1 Conclusion of Introduction to Data Analysis and Python
Chapter 2: Getting Started with Python
2.1 Installing Python
2.2 Your First Python Program
2.3 Variables and Data Types
2.4 Practical Exercises for Chapter 2: Getting Started with Python
2.5 Chapter 2 Conclusion of Getting Started with Python
Quiz for Part I: Setting the Stage
Multiple Choice Questions
True or False Questions
Answer Key for Quiz for Part I: Introduction to Data Analysis and Python
Chapter 3: Basic Python Programming
3.1 Control Structures
3.2 Functions and Modules
3.3 Python Scripting
3.4 Practical Exercises Chapter 3: Basic Python Programming
3.5 Chapter 3 Conclusion of Basic Python Programming
Chapter 4: Setting Up Your Data Analysis Environment
4.1 Installing Anaconda
4.2 Jupyter Notebook Basics
4.3 Git for Version Control
4.4 Practical Exercises Chapter 4: Setting Up Your Data Analysis Environment
4.5 Chapter 4 Conclusion of Setting Up Your Data Analysis Environment
Quiz for Part II: Python Basics for Data Analysis
Multiple-Choice Questions
True/False Questions
Answer Key for Quiz for Part II: Python Basics for Data Analysis
Chapter 5: NumPy Fundamentals
5.1 Arrays and Matrices
5.2 Basic Operations
5.3 Advanced NumPy Functions
5.4 Practical Exercises for Chapter 5: NumPy Fundamentals
5.5 Chapter 5 Conclusion of NumPy Fundamentals
Chapter 6: Data Manipulation with Pandas
6.1 DataFrames and Series
6.2 Data Wrangling
6.3 Handling Missing Data
6.4 Real-World Examples: Challenges and Pitfalls in Handling Missing Data
6.5 Practical Exercises Chapter 6: Data Manipulation with Pandas
Chapter 7: Data Visualization with Matplotlib and Seaborn
7.1 Basic Plotting with Matplotlib
7.2 Advanced Visualizations
7.3 Introduction to Seaborn
7.4 Practical Exercises - Chapter 7: Data Visualization with Matplotlib and Seaborn
7.5 Chapter 7 Conclusion of Data Visualization with Matplotlib and Seaborn
Chapter 8: Understanding EDA
8.1 Importance of EDA
8.2 Types of Data
8.3 Descriptive Statistics
8.4 Practical Exercises for Chapter 8: Understanding EDA
8.5 Chapter 8 Conclusion of Understanding EDA
Quiz for Part III: Core Libraries for Data Analysis
Chapter 5: NumPy Fundamentals
Chapter 6: Data Manipulation with Pandas
Chapter 7: Data Visualization with Matplotlib and Seaborn
Answer Key for Quiz for Part III: Core Libraries for Data Analysis
Chapter 9: Data Preprocessing
9.1 Data Cleaning
9.2 Feature Engineering
9.3 Data Transformation
9.4 Practical Exercises: Chapter 9: Data Preprocessing
9.5 Chapter 9 Conclusion
Chapter 10: Visual Exploratory Data Analysis
10.1 Univariate Analysis
10.2 Bivariate Analysis
10.3 Multivariate Analysis
10.4 Practical Exercises Chapter 10
10.5 Chapter 10 Conclusion of Data Preprocessing
Quiz for Part IV: Exploratory Data Analysis (EDA)
Questions of Quiz for Part IV: Exploratory Data Analysis (EDA)
Answers of Quiz for Part IV: Exploratory Data Analysis (EDA)
Project 1: Analyzing Customer Reviews
1.1 Data Collection
1.2: Data Cleaning
1.3: Data Visualization
1.4: Basic Sentiment Analysis
Chapter 11: Probability Theory
11.1 Basic Concepts
11.2: Probability Distributions
11.3: Specialized Probability Distributions
11.4 Bayesian Theory
11.5 Practical Exercises for Chapter 11: Probability Theory
Chapter 12: Hypothesis Testing
12.1 Null and Alternative Hypotheses
12.2 t-test and p-values
12.3 ANOVA (Analysis of Variance)
12.4 Practical Exercises Chapter 12: Hypothesis Testing
12.5 Chapter 12 Conclusion of Hypothesis Testing
Quiz for Part V: Statistical Foundations
Chapter 11: Probability Theory
Chapter 12: Hypothesis Testing
Answers of Quiz for Part V: Statistical Foundations
Chapter 13: Introduction to Machine Learning
13.1 Types of Machine Learning
13.2 Basic Algorithms
13.3 Model Evaluation
13.4 Practical Exercises Chapter 13: Introduction to Machine Learning
13.5 Chapter 13 Conclusion of Introduction to Machine Learning
Chapter 14: Supervised Learning
14.1 Linear Regression
14.2 Types of Classification Algorithms
14.3 Decision Trees
14.4 Practical Exercises Chapter 14: Supervised Learning
14.5 Chapter 14 Conclusion of Supervised Learning
Chapter 15: Unsupervised Learning
15.1 Clustering
15.2 Principal Component Analysis (PCA)
15.3 Anomaly Detection
15.4 Practical Exercises Chapter 15: Unsupervised Learning
15.5 Chapter 15 Conclusion of Unsupervised Learning
Quiz Part VI: Machine Learning Basics
Chapter 13: Introduction to Machine Learning
Chapter 14: Supervised Learning
Chapter 15: Unsupervised Learning
Answer Key of Quiz Part VI: Machine Learning Basics
Project 2: Predicting House Prices
Problem Statement
Data Collection and Preprocessing
Feature Engineering
Model Building and Evaluation
Chapter 16: Case Study 1: Sales Data Analysis
16.1 Problem Definition
16.2 EDA and Visualization
16.3 Predictive Modeling
16.4 Practical Exercises: Sales Data Analysis
16.5 Chapter 16 Conclusion of Sales Data Analysis
Chapter 17: Case Study 2: Social Media Sentiment Analysis
17.1 Data Collection
17.2 Text Preprocessing
17.3 Sentiment Analysis
17.4 Practical Exercises of Chapter 17: Case Study 2: Social Media Sentiment Analysis
17.5 Chapter 17 Conclusion of Social Media Sentiment Analysis
Quiz Part VII: Case Studies
Chapter 16: Case Study 1 - Sales Data Analysis
Chapter 17: Case Study 2 - Social Media Sentiment Analysis
Answers of Quiz Part VII: Case Studies
Project 3: Capstone Project: Building a Recommender System
Problem Statement
Data Collection and Preprocessing
Model Building
Evaluation and Deployment
Chapter 18: Best Practices and Tips
18.1 Code Organization
18.2 Documentation
Reviews
What our readers are saying about this book
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Review from Amazon
This book is not one-time read! This is a study material, and it is very well written and understandable for beginners. I highly recomend this book to anyone who wants to start learning about Python data analysis.
Review from Amazon
This series of books is on the cutting edge and is a must for anyone who wants to delve into the world of AI. The price is a steep, but the information gained was worth it for me.
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