Data Science from Scratch: Practical Guide with Python (Data Sciences) by Alain Kaufmann

Data Science from Scratch: Practical Guide with Python (Data Sciences)

Book Title: Data Science from Scratch: Practical Guide with Python (Data Sciences)

Publisher: CreateSpace Publishing

Author: Alain Kaufmann

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Alain Kaufmann with Data Science from Scratch: Practical Guide with Python (Data Sciences)

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Are you thinking of learning more about Data Science From scratch by using Python?

If you are looking for a practical book to help you understand data science step by step by using Python, then this is a good book for you.
In the past ten years, Data Science has quietly grown to include businesses and organizations world-wide. It is now being used by governments, geneticists, engineers, and even astronomers.
Technically, this includes machine translation, robotics, speech recognition, the digital economy, and search engines. In terms of research areas, Data Science has expanded to include the biological sciences, health care, medical informatics, the humanities, and social sciences. Data Science now influences economics, governments, and business and finance.
This book is written for helping you to grasp data science in the easiest way possible with a lot of practices and examples.

Several Visual Illustrations and Examples

Instead of tough math formulas, this book contains several graphs and images which detail all importants data science concepts and their applications.

This Is a Practical Guide Book

This book will help you explore exactly all data science techniques by using python. The book also will introduce the reader to the concepts of data science with the practical case studies and walk-through examples on which to practice.

This book takes a different approach that is based on providing simple examples of how each da science technique work, and building on those examples step by step to encompass the more complicated parts of the techniques.

You will build our data science Model by using Python

Target Users

The book designed for a variety of target audiences. The most suitable users would include:
  • Beginners who want to approach data science, but are too afraid of complex math to start

  • Newbies in computer science techniques and machine learning
  • Professionals in data science and social sciences
  • Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way
  • Students and academicians, especially those focusing on data science

What’s Inside This Great Book?

  • Introduction
  • Data Science Illuminated
  • A Basic Course in Python
  • Visualizing Data
  • Linear Algebra
  • Statistics
  • Hypothesis and Inference
  • Getting Data
  • Working around Data
  • Machine Learning
  • K-Nearest Neighbors
  • Naive Bayes
  • Simple Linear Regression
  • Logistic Regression
  • Naive Bayes
  • Decision Trees
  • Random Forests
  • Neural Networks
  • Clustering
  • Natural Language Processing
  • Network Analysis
  • Recommender Systems
  • Databases and SQL
  • Go Forth and Do Data Science