Book Image

Data Analysis Foundations with Python

By : Cuantum Technologies LLC
Book Image

Data Analysis Foundations with Python

By: Cuantum Technologies LLC

Overview of this book

Embark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently. Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling. To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.

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Table of Contents (37 chapters)
Free Chapter
1
Code Blocks Resource
Code Blocks Resource
2
Premium Customer Support
Premium Customer Support
3
Who we are
Who we are
Our Philosophy
Our Expertise
4
Introduction
Introduction
5
Who is This Book For?
Who is This Book For?
Beginners and Students
Career Changers
Professionals in Data-Adjacent Roles
Aspiring Data Scientists and AI Engineers
Educators and Trainers
6
How to Use This Book
How to Use This Book
Start at the Beginning
Work Through the Exercises
Take the Quizzes
Participate in Projects
Utilize Additional Resources
Collaborate and Share
Experiment and Explore
7
Acknowledgments
Acknowledgments
8
Chapter 1: Introduction to Data Analysis and Python
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
Practical Exercises for Chapter 1
Conclusion for Chapter 1
9
Quiz for Part I: Introduction to Data Analysis and Python
Quiz for Part I: Introduction to Data Analysis and Python
10
Chapter 2: Getting Started with Python
Chapter 2: Getting Started with Python
2.1 Installing Python
2.2 Your First Python Program
2.3 Variables and Data Types
Practical Exercises for Chapter 2
Chapter 2 Conclusion
11
Chapter 3: Basic Python Programming
Chapter 3: Basic Python Programming
3.1 Control Structures
3.2 Functions and Modules
3.3 Python Scripting
Practical Exercises Chapter 3
Chapter 3 Conclusion
12
Chapter 4: Setting Up Your Data Analysis Environment
Chapter 4: Setting Up Your Data Analysis Environment
4.1 Installing Anaconda
4.2 Jupyter Notebook Basics
4.3 Git for Version Control
Practical Exercises Chapter 4
Chapter 4 Conclusion
13
Quiz for Part II: Python Basics for Data Analysis
Quiz for Part II: Python Basics for Data Analysis
14
Chapter 5: NumPy Fundamentals
Chapter 5: NumPy Fundamentals
5.1 Arrays and Matrices
5.2 Basic Operations
5.3 Advanced NumPy Functions
Practical Exercises for Chapter 5
Chapter 5 Conclusion
15
Chapter 6: Data Manipulation with Pandas
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
Practical Exercises Chapter 6
Chapter 6 Conclusion
16
Chapter 7: Data Visualization with Matplotlib and Seaborn
Chapter 7: Data Visualization with Matplotlib and Seaborn
7.1 Basic Plotting with Matplotlib
7.2 Advanced Visualizations
7.3 Introduction to Seaborn
Practical Exercises - Chapter 7
Chapter 7 Conclusion
17
Quiz for Part III: Core Libraries for Data Analysis
Quiz for Part III: Core Libraries for Data Analysis
18
Chapter 8: Understanding EDA
Chapter 8: Understanding EDA
8.1 Importance of EDA
8.2 Types of Data
8.3 Descriptive Statistics
Practical Exercises for Chapter 8
Chapter 8 Conclusion
19
Chapter 9: Data Preprocessing
Chapter 9: Data Preprocessing
9.1 Data Cleaning
9.2 Feature Engineering
9.3 Data Transformation
Practical Exercises: Chapter 9
Chapter 9 Conclusion
20
Chapter 10: Visual Exploratory Data Analysis
Chapter 10: Visual Exploratory Data Analysis
10.1 Univariate Analysis
10.2 Bivariate Analysis
10.3 Multivariate Analysis
Practical Exercises Chapter 10
Chapter 10 Conclusion
21
Quiz for Part IV: Exploratory Data Analysis (EDA)
Quiz for Part IV: Exploratory Data Analysis (EDA)
22
Project 1: Analyzing Customer Reviews
Project 1: Analyzing Customer Reviews
1.1 Data Collection
1.2: Data Cleaning
1.3: Data Visualization
1.4: Basic Sentiment Analysis
23
Chapter 11: Probability Theory
Chapter 11: Probability Theory
11.1 Basic Concepts
11.2: Probability Distributions
11.3: Specialized Probability Distributions
11.4 Bayesian Theory
Practical Exercises for Chapter 11
Chapter 11 Conclusion
24
Chapter 12: Hypothesis Testing
Chapter 12: Hypothesis Testing
12.1 Null and Alternative Hypotheses
12.2 t-test and p-values
12.3 ANOVA (Analysis of Variance)
Practical Exercises Chapter 12
Chapter 12 Conclusion
25
Quiz for Part V: Statistical Foundations
Quiz for Part V: Statistical Foundations
26
Chapter 13: Introduction to Machine Learning
Chapter 13: Introduction to Machine Learning
13.1 Types of Machine Learning
13.2 Basic Algorithms
13.3 Model Evaluation
Practical Exercises Chapter 13
Chapter 13 Conclusion
27
Chapter 14: Supervised Learning
Chapter 14: Supervised Learning
14.1 Linear Regression
14.2 Types of Classification Algorithms
14.3 Decision Trees
Practical Exercises Chapter 14
Chapter Conclusion
28
Chapter 15: Unsupervised Learning
Chapter 15: Unsupervised Learning
15.1 Clustering
15.2 Principal Component Analysis (PCA)
15.3 Anomaly Detection
Practical Exercises Chapter 15
Chapter 15 Conclusion
29
Quiz Part VI: Machine Learning Basics
Quiz Part VI: Machine Learning Basics
30
Project 2: Predicting House Prices
Project 2: Predicting House Prices
Problem Statement
Data Collection and Preprocessing
Feature Engineering
Model Building and Evaluation
31
Chapter 16: Case Study 1: Sales Data Analysis
Chapter 16: Case Study 1: Sales Data Analysis
16.1 Problem Definition
16.2 EDA and Visualization
16.3 Predictive Modeling
Practical Exercises: Sales Data Analysis
Chapter 16 Conclusion
32
Chapter 17: Case Study 2: Social Media Sentiment Analysis
Chapter 17: Case Study 2: Social Media Sentiment Analysis
17.1 Data Collection
17.2 Text Preprocessing
17.3 Sentiment Analysis
Practical Exercises
Chapter 17 Conclusion
33
Quiz Part VII: Case Studies
Quiz Part VII: Case Studies
34
Project 3: Capstone Project: Building a Recommender System
Project 3: Capstone Project: Building a Recommender System
Problem Statement
Data Collection and Preprocessing
Model Building
Evaluation and Deployment
35
Chapter 18: Best Practices and Tips
Chapter 18: Best Practices and Tips
18.1 Code Organization
18.2 Documentation
36
Conclusion
Conclusion
37
Know more about us
Know more about us

14.2 Types of Classification Algorithms

14.2.1. Logistic Regression

Despite its name, logistic regression is widely used for classification problems, where the goal is to assign input data to one of several categories. It is particularly well-suited for binary classification, where there are only two possible categories. Logistic regression works by modeling the probability of an input belonging to a particular category, given its features. This probability function is known as the logistic function, and it maps any input to a value between 0 and 1. The decision boundary between the two categories is then determined by a threshold value.

One of the key advantages of logistic regression is that it is relatively easy to interpret. The coefficients of the model represent the effect that each feature has on the probability of the input belonging to a particular category. This can be useful in understanding the underlying relationships between the features and the target variable, and can...

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