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Data Science Crash Course for Beginners

Release Date: 2021/03/01
ISBN: 9781801811255
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This course lays the groundwork for further study into data science with Python for those students with little to no experience

Key Features

  • Crash course in Python programming to build or refresh any gaps in prerequisite knowledge
  • Real-world projects for hands-on practice in various data science tasks
  • Access to all codes and datasets free to view online

Book Description

Data science is here to stay. The tremendous growth in the volume, velocity, and variety of data has a substantial impact on every aspect of a business. While data continues to grow exponentially, accuracy remains a problem. This is where data scientists play a decisive role.

A data scientist analyzes data, discovers new insights, paints a picture, and creates a vision. And a competent data scientist will provide a business with the competitive edge it needs and to address pressing business problems.

Data Science Crash Course for Beginners with Python presents you with a hands-on approach to learn data science fast. This book presents you with the tools and packages you need to kick-start data science projects to resolve problems of a practical nature. Special emphasis is laid on the main stages of a data science pipeline—data acquisition, data preparation, exploratory data analysis, data modeling and evaluation, and interpretation of the results.

The author simplifies your learning by providing detailed, guided instructions through everything. The step-by-step description of the installation of the software you need to implement the various data science techniques in this book is guaranteed to make your learning easier. So, right from the beginning, you can experiment with the practical aspects of data science. By the end of this course, you will have a solid grasp on the essential concepts of data science and its most fundamental implementations, laying the groundwork for your next steps no matter your chosen direction.

The code bundle for this course is available at https://www.aispublishing.net/book-data-science-01

What you will learn

  • Consider Natural Language Processing and decision making in data science
  • Install Python and libraries for data science
  • Review Python for data science
  • Study data acquisition
  • Practice data preparation (preprocessing)
  • Perform exploratory data analysis
  • Explore data modeling and evaluation using machine learning
  • Interpret data and report your findings
  • Successfully complete several data science projects

Who this book is for

This book is specifically designed for beginners in data science looking to build foundational tools and skills quickly, utilizing the Python programming language. No prior experience is required.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. How to contact us
  5. About the Publisher
  6. AI Publishing is Looking for Authors Like You
  7. Table of Contents
  8. Preface
    1. Who Is This Book For?
    2. How to Use This Book?
  9. About the Author
  10. Get in Touch with Us
  11. Download the Color Images
  12. Chapter 1: Introduction to Data Science and Decision Making
    1. 1.1. Introduction
    2. Applications of Data Science
    3. What Is This Book About?
    4. 1.2. Python and Data Science
    5. 1.3. The Data Science Pipeline
    6. 1.4. Overview of the Contents
    7. 1.5. Exercises
  13. Chapter 2: Python Installation and Libraries for Data Science
    1. 2.1. Introduction
    2. 2.2. Installation and Setup
    3. 2.3. Datasets
    4. 2.4. Python Libraries for Data Science
    5. 2.5. Exercise Questions
  14. Chapter 3: Review of Python for Data Science
    1. 3.1. Introduction
    2. 3.2. Working with Numbers and Logic
    3. 3.3. String Operations
    4. 3.4. Dealing with Conditional Statements & Iterations
    5. 3.5. Creation and Use of Python Functions
    6. 3.6. Data Storage
    7. 3.7. Exercise Questions
  15. Chapter 4: Data Acquisition
    1. 4.1. Introduction
    2. 4.2. Types of Data
    3. 4.3. Loading Data into Memory
    4. 4.4. Sampling Data
    5. 4.5. Reading from Files
    6. 4.6. Getting Data from the Web
    7. 4.7. Exercise Questions
  16. Chapter 5: Data Preparation (Preprocessing)
    1. 5.1. Introduction
    2. 5.2. Pandas for Data Preparation
    3. 5.3. Pandas Data Structures
    4. 5.4. Putting Data Together
    5. 5.5. Data Transformation
    6. 5.6. Selection of Data
    7. 5.7. Exercise Questions
  17. Chapter 6: Exploratory Data Analysis
    1. 6.1. Introduction
    2. 6.2. Revealing Structure of Data
    3. 6.3. Plots and Charts
    4. 6.4. Testing Assumptions about Data
    5. 6.5. Selecting Important Features/Variables
    6. 6.6. Exercise Questions
  18. Chapter 7: Data Modeling and Evaluation using Machine Learning
    1. 7.1. Introduction
    2. 7.2. Important Statistics for Data Science
    3. 7.3. Data Distributions
    4. 7.4. Basic Machine Learning Terminology
    5. 7.5. Supervised Learning: Regression
    6. 7.6. Supervised Learning: Classification
    7. 7.7. Unsupervised Learning
    8. 7.8. Evaluating Performance of the Trained Model
    9. 7.9. Exercise Questions
  19. Chapter 8: Interpretation and Reporting of Findings
    1. 8.1. Introduction
    2. 8.2. Confusion Matrix
    3. 8.3. Receiver Operating Characteristics (ROC) Curve
    4. 8.4. Precision-Recall Curve
    5. 8.5. Regression Metrics
    6. 8.6. Exercise Questions
  20. Chapter 9: Data Science Projects
    1. 9.1. Regression
    2. 9.2. Classification
    3. 9.3. Face Recognition
  21. Chapter 10: Key Insights and Further Avenues
    1. 10.1. Key Insights
    2. 10.2. Data Science Resources
    3. 10.3. Challenges
  22. Conclusions
  23. Answers to Exercise Questions
  24. Back Cover
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