Data Science From Scratch With Python
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Data Science from Scratch
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
Data Science from Scratch with Python
Author: Peter Morgan
language: en
Publisher: Createspace Independent Publishing Platform
Release Date: 2018-08-21
***** BUY NOW (will soon return to 24.77 $) ***** MONEY BACK GUARANTEE BY AMAZON (See Below FAQ) *****Are you thinking of learning data science from scratch using Python? (For Beginners)If you are looking for a complete step-by-step guide to data science using Python from scratch, this book is for you. After his great success with his first book "Data Analysis from Scratch with Python," Peter Morgan publishes his second book focusing now in data science and machine learning. It is considered by practitioners as the easiest guide ever written in this domain. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. Readers are advised to adopt a hands on approach, which would lead to better mental representations. Step by Step Guide and Visual Illustrations and ExamplesThe Book give complete instructions for manipulating, processing, cleaning, modeling and crunching datasets in Python. This is a hands-on guide with practical case studies of data analysis problems effectively. You will learn, pandas, NumPy, IPython, and Jupiter in the Process. Target Users Beginners who want to approach data science, but are too afraid of complex math to start Newbies in computer science techniques and data science 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 Book? Part 1: Data Science Fundamentals, Concepts and Algorithms Introduction Statistics Probability Bayes' Theorem and Naïve Bayes Algorithm Asking the Right Question Data Acquisition Data Preparation Data Exploration Data Modelling Data Presentation Supervised Learning Algorithms Unsupervised Learning Algorithms Semi-supervised Learning Algorithms Reinforcement Learning Algorithms Overfitting and Underfitting The Bias-Variance Trade-off Feature Extraction and Selection Part 2: Data Science in Practice Overview of Python Programming Language Python Data Science Tools Jupyter Notebook Numerical Python (Numpy) Pandas Scientific Python (Scipy) Matplotlib Scikit-Learn K-Nearest Neighbors Naive Bayes Simple and Multiple Linear Regression Logistic Regression GLM models Decision Trees and Random forest Perceptrons Backpropagation Clustering Natural Language Processing Frequently Asked Questions Q: Does this book include everything I need to become a data science expert?A: Unfortunately, no. This book is designed for readers taking their first steps in data science and machine learning using Python and further learning will be required beyond this book to master all aspects. Q: Can I have a refund if this book doesn't fit for me?A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform.***** MONEY BACK GUARANTEE BY AMAZON ***** Editorial Reviews "This is a fantastic book on Python-based data science, data analysis, machine learning, Reinforcement learning and deep learning. As a data scientist with more than 10 years, Peter has had long experience in data science and give in this book the key elements.." - Lei Xia, Data Scientist Expert at Facebook
Data Science from Scratch
This is a first-principles-based, practical introduction to the fundamentals of data science aimed at the mathematically-comfortable reader with some programming skills. The book covers: The important parts of Python to know The important parts of Math / Probability / Statistics to know The basics of data science How commonly-used data science techniques work (learning by implementing them) What is Map-Reduce and how to do it in Python Other applications such as NLP, Network Analysis, and more