From Raw Data to Real Insight
Build Data Products from Python and Machine LearningBy Victor Fontaine
Length7h 42m
About this audiobook
data science python pandas matplotlib data visualization. Are you ready to move beyond theory and start applying Python to real analytics work? From Raw Data to Real Insight teaches you to clean, explore, and visualize data using pandas and Matplotlib—answering questions that matter. Whether you're a beginner or a pro, this hands-on tech book guides you through practical projects that transform messy datasets into clear, actionable insights. No fluff, just real-world coding and problem-solving.
In this book, you’ll learn to:
• Master pandas for data cleaning, transformation, and aggregation
• Create compelling visualizations with Matplotlib that tell a story
• Explore datasets to uncover patterns and answer key business questions
• Build end-to-end analytics workflows from raw data to final report
• Apply Python to real-world scenarios like sales analysis, customer segmentation, and more
Victor Fontaine brings years of experience in data analytics and Python instruction, making complex concepts accessible through clear explanations and reproducible code. Each chapter builds on the last, ensuring you gain confidence and competence.
If you liked books by [placeholder] or [placeholder], you’ll love this practical, project-driven approach. Stop reading about data science—start doing it. Get your copy today and turn raw data into real insight.
This hands-on Python guide is written to be used at the keyboard: every concept is paired with something you can run, adapt, and keep. You move from first principles to real, working results, with the common errors and fixes called out along the way so you are never stuck for long.
Audiobook details
GenreTechnology
Length7 hrs 42 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateSep 30, 2026
LanguageEnglish
Table of contents
1Preface
2Chapter 1 — The Data Science Workflow and Your First Project Setup
3Chapter 2 — Python Fundamentals for Data Wrangling
4Chapter 3 — Data Acquisition: Loading from Files, APIs, and Databases
5Chapter 4 — Data Cleaning and Transformation with Pandas
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6Chapter 5 — Exploratory Data Analysis and Descriptive Statistics
7Chapter 6 — Visualizing Data for Insights
8Chapter 7 — Probability and Inferential Statistics for Decision Making
9Chapter 8 — Introduction to Machine Learning and the Modeling Pipeline
10Chapter 9 — Feature Engineering for Predictive Performance
11Chapter 10 — Model Evaluation and Cross‑Validation
12Chapter 11 — Hyperparameter Tuning and Ensemble Methods
13Chapter 12 — Advanced Models: Decision Trees, Random Forests, and SVM
14Chapter 13 — Deep Learning Basics with TensorFlow and Keras
15Chapter 14 — Model Interpretation and Explainability
16Chapter 15 — Deploying Your Model with Flask and Streamlit
17Chapter 16 — Working with Big Data: SQL, PySpark, and Cloud Tools
18Chapter 17 — Putting It All Together: The Complete Churn Prediction System
19Chapter 18 — Final Project and Next Steps in Python Data Science
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