book

Data Analysis Foundations with Python

Name: Data Analysis Foundations with Python
Author: Cuantum Technologies LLC
ISBN: 9781836209072

by Cuantum Technologies LLC

June 2024

Beginner to intermediate

551 pages

9h 55m

English

Packt Publishing

Read now

Unlock full access

Code Blocks Resource
Premium Customer Support
Who we are
Our PhilosophyOur Expertise
Introduction
Who is This Book For?
Beginners and StudentsCareer ChangersProfessionals in Data-Adjacent RolesAspiring Data Scientists and AI EngineersEducators and Trainers
How to Use This Book
Start at the BeginningWork Through the ExercisesTake the QuizzesParticipate in ProjectsUtilize Additional ResourcesCollaborate and ShareExperiment and Explore
Acknowledgments
Chapter 1: Introduction to Data Analysis and Python
1.1 Importance of Data Analysis1.1.1 Informed Decision-Making1.1.2 Identifying Trends1.1.3 Enhancing Efficiency1.1.4 Resource Allocation1.1.5 Customer Satisfaction1.1.6 Social Impact1.1.7 Innovation and Competitiveness1.2 Role of Python in Data Analysis1.2.1 User-Friendly Syntax1.2.2 Rich Ecosystem of Libraries1.2.3 Community Support1.2.4 Integration and Interoperability1.2.5 Scalability1.2.6 Real-world Applications1.2.7 Versatility Across Domains1.2.8 Strong Support for Data Science Operations1.2.9 Open Source Advantage1.2.10 Easy to Learn, Hard to Master1.2.11 Cross-platform Compatibility1.2.12 Future-Proofing Your Skillset1.2.13 The Ethical Aspect1.3 Overview of the Data Analysis Process1.3.1 Define the Problem or Question1.3.2 Data Collection1.3.3 Data Cleaning and Preprocessing1.3.4 Exploratory Data Analysis (EDA)1.3.5 Data Modeling1.3.6 Evaluate and Interpret Results1.3.7 Communicate Findings1.3.8 Common Challenges and Pitfalls1.3.9 The Complexity of Real-world Data1.3.10 Selection Bias1.3.11 Overfitting and Underfitting
Practical Exercises for Chapter 1
Exercise 1: Define a Data Analysis ProblemExercise 2: Data Collection with PythonExercise 3: Basic Data Cleaning with PandasExercise 4: Create a Basic PlotExercise 5: Evaluate a Simple Model
Conclusion for Chapter 1
Quiz for Part I: Introduction to Data Analysis and Python

Chapter 2: Getting Started with Python
2.1 Installing Python2.1.1 For Windows Users:2.1.2 For Mac Users:2.1.3 For Linux Users:2.1.4 Test Your Installation2.2 Your First Python Program2.2.1 A Simple Print Function2.2.2 Variables and Basic Arithmetic2.2.3 Using Python's Interactive Mode2.3 Variables and Data Types2.3.1 What is a Variable?2.3.2 Data Types in Python2.3.3 Declaring and Using Variables2.3.4 Type Conversion2.3.5 Variable Naming Conventions and Best Practices
Practical Exercises for Chapter 2
Exercise 1: Install PythonExercise 2: Your First Python ScriptExercise 3: Working with VariablesExercise 4: Type ConversionExercise 5: Explore Data TypesExercise 6: Variable Naming
Chapter 2 Conclusion
Chapter 3: Basic Python Programming
3.1 Control Structures3.1.1 If, Elif, and Else Statements3.1.2 For Loops3.1.3 While Loops3.1.4 Nested Control Structures3.2 Functions and Modules3.2.1 Functions3.2.2 Parameters and Arguments3.2.3 Return Statement3.2.4 Modules3.2.5 Creating Your Own Module
3.2.6 Lambda Functions
3.2.7 Function Decorators3.2.8 Working with Third-Party Modules3.3 Python Scripting3.3.1 Writing Your First Python Script3.3.2 Script Execution and Command-Line Arguments3.3.3 Automating Tasks3.3.4 Debugging Scripts3.3.5 Scheduling Python Scripts3.3.6 Script Logging3.3.7 Packaging Your Scripts
Practical Exercises Chapter 3
Exercise 1: Your First ScriptExercise 2: Command-Line ArgumentsExercise 3: CSV File ReaderExercise 4: Simple Task AutomationExercise 5: Debugging PracticeExercise 6: Script Logging
Chapter 3 Conclusion
Chapter 4: Setting Up Your Data Analysis Environment
4.1 Installing Anaconda4.1.1 For Windows Users:4.1.2 For macOS Users:4.1.3 For Linux Users:4.1.4 Troubleshooting and Tips
4.2 Jupyter Notebook Basics
4.2.1 Launching Jupyter Notebook4.2.2 The Notebook Interface4.2.3 Writing and Running Code4.2.4 Markdown and Annotations4.2.5 Saving and Exporting4.2.6 Advanced Features of Jupyter Notebook4.3 Git for Version Control4.3.1 Why Use Git?4.3.2 Installing Git4.3.3 Basic Git Commands4.3.4 Git Best Practices for Data Analysis
Practical Exercises Chapter 4
Exercise 4.1: Installing AnacondaExercise 4.2: Jupyter Notebook BasicsExercise 4.3: Git for Version Control
Chapter 4 Conclusion
Quiz for Part II: Python Basics for Data Analysis
Chapter 5: NumPy Fundamentals
5.1 Arrays and Matrices5.1.1 Additional Operations on Arrays5.2 Basic Operations5.2.1 Arithmetic Operations5.2.2 Aggregation Functions5.2.3 Boolean Operations5.2.4 Vectorization5.3 Advanced NumPy Functions5.3.1 Aggregation Functions5.3.2 Indexing and Slicing5.3.3 Broadcasting with Advanced Operations5.3.4 Logical Operations5.3.5 Handling Missing Data5.3.6 Reshaping Arrays
Practical Exercises for Chapter 5
Exercise 1: Create an ArrayExercise 2: Array ArithmeticExercise 3: Handling Missing DataExercise 4: Advanced NumPy Functions
Chapter 5 Conclusion
Chapter 6: Data Manipulation with Pandas
6.1 DataFrames and Series6.1.1 DataFrame6.1.2 Series6.1.3 DataFrame vs Series6.1.4 DataFrame Methods and Attributes6.1.5 Series Methods and Attributes6.1.6 Changing Data Types6.2 Data Wrangling6.2.1 Reading Data from Various Sources6.2.2 Handling Missing Values6.2.3 Data Transformation6.2.4 Data Aggregation6.2.5 Merging and Joining DataFrames6.2.6 Applying Functions6.2.7 Pivot Tables and Cross-Tabulation6.2.8 String Manipulation6.2.9 Time Series Operations6.3 Handling Missing Data6.3.1 Detecting Missing Data6.3.2 Handling Missing Values6.3.3 Advanced Strategies6.4 Real-World Examples: Challenges and Pitfalls in Handling Missing Data6.4.1 Case Study 1: Healthcare Data6.4.2 Case Study 2: Financial Data6.4.3 Challenges and Pitfalls:
Practical Exercises Chapter 6
Exercise 1: Creating DataFramesExercise 2: Missing Data HandlingExercise 3: Data Wrangling
Chapter 6 Conclusion
Chapter 7: Data Visualization with Matplotlib and Seaborn
7.1 Basic Plotting with Matplotlib7.1.1 Installing Matplotlib7.1.2 Your First Plot7.1.3 Customizing Your Plot7.1.4 Subplots7.1.5 Legends and Annotations7.1.6 Error Bars7.2 Advanced Visualizations7.2.1 Customizing Plot Styles7.2.2 3D Plots7.2.3 Seaborn's Beauty7.2.4 Heatmaps7.2.5 Creating Interactive Visualizations7.2.6 Exporting Your Visualizations7.2.7 Performance Tips for Large Datasets7.3 Introduction to Seaborn7.3.1 Installation7.3.2 Basic Plotting with Seaborn7.3.3 Categorical Plots7.3.4 Styling and Themes7.3.5 Seaborn for Exploratory Data Analysis7.3.6 Facet Grids7.3.7 Joint Plots7.3.8 Customizing Styles
Practical Exercises - Chapter 7
Exercise 1: Basic Line PlotExercise 2: Bar Chart with SeabornExercise 3: Scatter Plot MatrixExercise 4: Advanced Plot - HeatmapExercise 5: Customize Your Plot
Chapter 7 Conclusion
Quiz for Part III: Core Libraries for Data Analysis
Chapter 8: Understanding EDA
8.1 Importance of EDA8.1.1 Why is EDA Crucial?8.1.2 Code Example: Simple EDA using Pandas8.1.3 Importance in Big Data8.1.4 Human Element8.1.5 Risk Mitigation8.1.6 Examples from Different Domains8.1.7 Comparing Datasets8.1.8 Code Snippets for Visual EDA8.2 Types of Data8.2.1 Numerical Data8.2.2 Categorical Data8.2.3 Textual Data8.2.4 Time-Series Data8.2.5 Multivariate Data8.2.6 Geospatial Data8.3 Descriptive Statistics8.3.1 What Are Descriptive Statistics?8.3.2 Measures of Central Tendency8.3.3 Measures of Variability8.3.4 Why Is It Useful?8.3.6 Example: Analyzing Customer Reviews8.3.7 Skewness and Kurtosis
Practical Exercises for Chapter 8
Exercise 1: Understanding the Importance of EDAExercise 2: Identifying Types of DataExercise 3: Calculating Descriptive StatisticsExercise 4: Understanding Skewness and Kurtosis
Chapter 8 Conclusion
Chapter 9: Data Preprocessing
9.1 Data Cleaning9.1.1 Types of 'Unclean' Data9.1.2 Handling Missing Data9.1.3 Dealing with Duplicate Data9.1.4 Data Standardization9.1.5 Outliers Detection9.1.6 Dealing with Imbalanced Data9.1.7 Column Renaming9.1.8 Encoding Categorical Variables9.1.9 Logging the Changes9.2 Feature Engineering9.2.1 What is Feature Engineering?9.2.2 Types of Feature Engineering9.2.3 Key Considerations9.2.4 Feature Importance9.3 Data Transformation9.3.1 Why Data Transformation?9.3.2 Types of Data Transformation9.3.3 Inverse Transformation
Practical Exercises: Chapter 9
Exercise 9.1: Data CleaningExercise 9.2: Feature EngineeringExercise 9.3: Data Transformation
Chapter 9 Conclusion
Chapter 10: Visual Exploratory Data Analysis
10.1 Univariate Analysis10.1.1 Histograms10.1.2 Box Plots10.1.3 Count Plots for Categorical Data10.1.4 Descriptive Statistics alongside Visuals10.1.5 Kernel Density Plot10.1.6 Violin Plot10.1.7 Data Skewness and Kurtosis10.2 Bivariate Analysis10.2.1 Scatter Plots10.2.2 Correlation Coefficient10.2.3 Line Plots10.2.4 Heatmaps10.2.5 Pairplots10.2.6 Statistical Significance in Bivariate Analysis10.2.7 Handling Categorical Variables in Bivariate Analysis10.2.8 Real-world Applications of Bivariate Analysis10.3 Multivariate Analysis10.3.1 What is Multivariate Analysis?10.3.2 Types of Multivariate Analysis10.3.3 Example: Principal Component Analysis (PCA)10.3.4 Example: Cluster Analysis10.3.5 Real-world Applications of Multivariate Analysis10.3.6 Heatmaps for Correlation Matrices10.3.7 Example using Multiple Regression Analysis10.3.8 Cautionary Points10.3.9 Other Dimensionality Reduction Techniques
Practical Exercises Chapter 10
Exercise 1: Univariate Analysis with HistogramsExercise 2: Bivariate Analysis with Scatter PlotExercise 3: Multivariate Analysis using Heatmap
Chapter 10 Conclusion
Quiz for Part IV: Exploratory Data Analysis (EDA)
Project 1: Analyzing Customer Reviews
1.1 Data Collection1.1.1 Web Scraping with BeautifulSoup1.1.2 Using APIs1.2: Data Cleaning1.2.1 Removing Duplicates1.2.2 Handling Missing Values1.2.4 Outliers and Anomalies1.3: Data Visualization1.3.1 Distribution of Ratings1.3.2 Word Cloud for Reviews1.3.3 Sentiment Analysis1.3.4 Time-Series Analysis1.4: Basic Sentiment Analysis1.4.1 TextBlob for Sentiment Analysis1.4.2 Visualizing TextBlob Results1.4.3 Comparing TextBlob Sentiments with Ratings
Chapter 11: Probability Theory
11.1 Basic Concepts11.1.1 Probability of an Event11.1.2 Python Example: Dice Roll11.1.3 Complementary Events11.1.4 Independent and Dependent Events11.1.5 Conditional Probability11.1.6 Python Example: Complementary Events11.2: Probability Distributions11.2.1 What is a Probability Distribution?11.2.2 Types of Probability Distributions11.2.3 Python Example: Plotting a Normal Distribution11.2.4 Why are Probability Distributions Important?11.2.5 Skewness11.2.6 Kurtosis11.2.7 Python Example: Calculating Skewness and Kurtosis11.3: Specialized Probability Distributions11.3.1 Exponential Distribution11.3.2 Poisson Distribution11.3.3 Beta Distribution11.3.4 Gamma Distribution11.3.5 Log-Normal Distribution11.3.6 Weibull Distribution11.4 Bayesian Theory11.4.1 Basics of Bayesian Theory11.4.2 Example: Diagnostic Test11.4.3 Bayesian Networks
Practical Exercises for Chapter 11
Exercise 1: Roll the DieExercise 2: Bayesian Inference for a Coin TossExercise 3: Bayesian Disease Diagnosis
Chapter 11 Conclusion
Chapter 12: Hypothesis Testing
12.1 Null and Alternative Hypotheses12.1.1 P-values and Significance Level12.1.2 Type I and Type II Errors12.2 t-test and p-values12.2.1 What is a t-test?12.2.2 Types of t-tests12.2.3 Understanding p-values12.2.4 Paired t-tests12.2.5 Assumptions behind t-tests12.2.6 Multiple Comparisons and the Bonferroni Correction12.3 ANOVA (Analysis of Variance)12.3.1 What is ANOVA?12.3.2 Why Use ANOVA?12.3.3 One-way ANOVA13.3.4 Example: One-way ANOVA in Python12.3.5 Two-way ANOVA12.3.6 Repeated Measures ANOVA12.3.7 Assumptions of ANOVA
Practical Exercises Chapter 12
Exercise 1: Conducting a t-testExercise 2: Performing One-Way ANOVAExercise 3: Post-Hoc Analysis
Chapter 12 Conclusion
Quiz for Part V: Statistical Foundations
Chapter 13: Introduction to Machine Learning
13.1 Types of Machine Learning13.1.1 Supervised Learning13.1.2 Unsupervised Learning13.1.3 Reinforcement Learning13.1.4 Semi-Supervised Learning13.1.5 Multi-Instance Learning13.1.6 Ensemble Learning13.1.7 Meta-Learning13.2 Basic Algorithms13.2.1 Linear Regression13.2.2 Logistic Regression13.2.3 Decision Trees13.2.4 k-Nearest Neighbors (k-NN)13.2.5 Support Vector Machines (SVM)13.3 Model Evaluation13.3.1 Accuracy13.3.2 Confusion Matrix13.3.3 Precision, Recall, and F1-Score13.3.4 ROC and AUC13.3.5 Mean Absolute Error (MAE) and Mean Squared Error (MSE) for Regression
Practical Exercises Chapter 13
Exercise 13.1: Types of Machine LearningExercise 13.2: Implement a Basic AlgorithmExercise 13.3: Model Evaluation
Chapter 13 Conclusion
Chapter 14: Supervised Learning
14.1 Linear Regression14.1.1 Assumptions of Linear Regression14.1.2 Regularization14.1.3 Polynomial Regression14.1.4 Interpreting Coefficients14.2 Types of Classification Algorithms14.2.1. Logistic Regression14.2.2. K-Nearest Neighbors (KNN)14.2.3. Decision Trees14.2.4. Support Vector Machine (SVM)14.2.5. Random Forest14.2.6 Pros and Cons14.2.7 Ensemble Methods14.3 Decision Trees14.3.1 How Decision Trees Work14.3.2 Hyperparameter Tuning14.3.3 Feature Importance14.3.4 Pruning Decision Trees
Practical Exercises Chapter 14
Exercise 1: Implementing Simple Linear RegressionExercise 2: Classify Iris Species Using k-NNExercise 3: Decision Tree Classifier for Breast Cancer Data
Chapter Conclusion
Chapter 15: Unsupervised Learning
15.1 Clustering15.1.1 What is Clustering?15.1.2 Types of Clustering15.1.3 K-Means Clustering15.1.4 Evaluating the Number of Clusters: Elbow Method15.1.5 Handling Imbalanced Clusters15.1.6 Cluster Validity Indices15.1.7 Mixed-type Data15.2 Principal Component Analysis (PCA)15.2.1 Why Use PCA?15.2.2 Mathematical Background15.2.3 Implementing PCA with Python15.2.4 Interpretation15.2.5 Limitations15.2.6 Feature Importance and Explained Variance15.2.7 When Not to Use PCA?15.2.8 Practical Applications15.3 Anomaly Detection15.3.1 What is Anomaly Detection?15.3.2 Types of Anomalies15.3.3 Algorithms for Anomaly Detection15.3.4 Pros and Cons15.3.5 When to Use Anomaly Detection15.3.6 Hyperparameter Tuning in Anomaly Detection15.3.7 Evaluation Metrics
Practical Exercises Chapter 15
Exercise 1: K-means ClusteringExercise 2: Principal Component Analysis (PCA)Exercise 3: Anomaly Detection with Isolation Forest
Chapter 15 Conclusion
Quiz Part VI: Machine Learning Basics
Project 2: Predicting House Prices
Problem StatementInstalling Necessary LibrariesData Collection and PreprocessingData CollectionData PreprocessingHandling Missing ValuesData EncodingFeature ScalingFeature EngineeringCreating Polynomial FeaturesInteraction TermsCategorical Feature EngineeringTemporal FeaturesFeature TransformationModel Building and EvaluationData SplittingModel SelectionModel EvaluationFine-TuningExporting the Trained Model
Chapter 16: Case Study 1: Sales Data Analysis
16.1 Problem Definition16.1.1 What are we trying to solve?16.1.2 Python Code: Setting up the Environment16.2 EDA and Visualization16.2.1 Importing the Data16.2.2 Data Cleaning16.2.3 Basic Statistical Insights16.2.4 Data Visualization16.3 Predictive Modeling16.3.1 Preprocessing for Predictive Modeling16.3.2 Model Selection and Training16.3.3 Model Evaluation16.3.4 Making Future PredictionsPractical Exercises: Sales Data AnalysisExercise 1: Data ExplorationExercise 2: Data VisualizationExercise 3: Simple Predictive ModelingExercise 4: Advanced
Chapter 16 Conclusion
Chapter 17: Case Study 2: Social Media Sentiment Analysis
17.1 Data Collection17.2 Text Preprocessing17.2.1 Cleaning Tweets17.2.2 Tokenization17.2.3 Stopwords Removal17.3 Sentiment Analysis17.3.1 Naive Bayes ClassifierPractical ExercisesExercise 1: Data CollectionExercise 2: Text PreprocessingExercise 3: Sentiment Analysis with Naive Bayes
Chapter 17 Conclusion
Quiz Part VII: Case Studies
Project 3: Capstone Project: Building a Recommender System
Problem StatementObjectiveWhy this Problem?Evaluation MetricsData RequirementsData Collection and PreprocessingData CollectionData PreprocessingModel BuildingInstallation and Importing LibrariesPreparing Data for the ModelBuilding the SVD ModelMaking PredictionsEvaluation and DeploymentModel EvaluationDeployment ConsiderationsContinuous Monitoring
Chapter 18: Best Practices and Tips
18.1 Code Organization18.1.1 Folder Structure18.1.2 File Naming18.1.3 Code Comments and Documentation18.1.4 Consistent Formatting18.2 Documentation18.2.1. Code Comments18.2.2. README File18.2.3. Documentation Generation Tools18.2.4. In-line Documentation
Conclusion
Know more about us

Content preview from Data Analysis Foundations with Python

Quiz for Part II: Python Basics for Data Analysis

Multiple-Choice Questions

1. Which Python distribution offers a unified platform for data analysis?

[ ] PIP
[ ] PyPy
[ ] Anaconda
[ ] CPython

2. In a Jupyter Notebook, how do you run a cell?

[ ] Alt + Enter
[ ] Shift + Enter
[ ] Ctrl + Enter
[ ] Enter + Enter

3. Which of the following control structures allows you to execute a block of code multiple times?

[ ] If-Else
[ ] Loop
[ ] Function
[ ] Module

4. What is the primary role of Git?

[ ] Data Visualization
[ ] Web Development
[ ] Version Control
[ ] Data Analysis

5. Which Python data type is mutable?

[ ] String
[ ] Tuple
[ ] List
[ ] Integer

True/False Questions

1. You can only install Python packages through Anaconda.

[ ] ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781836209072

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Data Analysis Foundations with Python

by Cuantum Technologies LLC

Quiz for Part II: Python Basics for Data Analysis

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.