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. In this book, 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. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

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.

***** BUY NOW (Will soon return to 35.59) ******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning data science from scratch using Python? 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", Peters Morgan publish 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. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by Alain Kaufmann at https://aisciences.lpages.co/ai-science-l1/.To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Step By Step Guide and Visual Illustrations and Examples The 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 Target UsersThe 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 data science 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 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 Correctness 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: Is this book for me and do I need programming experience?A: if you want to smash Python for data science and machine learning, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK.Q: Can I have a refund if this book is not fitted 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. We will also be happy to help you if you send us an email at [email protected].

"This book is so good. I wish I'd written it." -- Tim Hopper (@tdhopper)"Highly recommended: a grand tour of computer science theory and practical software engineering, explored through the lens of 10 Fizz Buzz solutions in Python. Outstanding." -- Paco Nathan (@pacoid)"I'd never have thought a book about Fizz Buzz would make me a better programmer, but I was wrong. Joel in the course of 10 chapters does a broad survey of core Python concepts, software design and testing, mathematics, and more (including deep learning) using Fizz Buzz as the guiding example. It's that rare technical book that remains engaging, entertaining, and accessible." -- Binal Patel (@binalkp91)More real Python tips than any "Python tricks" book! From a Python beginner to an experienced ML practitioner, you're bound to learn something about the language and its application to a progressive level of algorithmic applications. Recommended for the anyone looking to "level up" their Python or problem solving skills! -- Tom Marthaler (@tmarthal)Fizz Buzz is the following (simple) problem: Print the numbers from 1 to 100, except that if the number is divisible by 3, instead print "fizz"; if the number is divisible by 5, instead print "buzz"; and if the number is divisible by 15, instead print "fizzbuzz".It originated as a children's game, but has since taken on a new life as a lowest-common-denominator litmus test for assessing computer programmers.If you are an experienced programmer, it is an extremely easy problem to solve. Because of this, it has taken on a third life as the prototypical bad interview problem. Everyone knows that it's the question you ask people to make sure that they're not completely incompetent as programmers. Accordingly, if your interviewer asks you to solve it, he's suggesting he thinks it possible that you're completely incompetent as a programmer. You would not be wrong to feel insulted!My association with this problem began in 2016, when I wrote a blog post called Fizz Buzz in Tensorflow, the (possibly fictional) story of one such insulted programmer who decided to show up his interviewer by approaching Fizz Buzz as a deep learning problem. This post went modestly viral, and ever since then I have been seen as a thought leader in the Fizz Buzz space.Accordingly, over the years I have come up with and/or collected various other stupid and/or clever ways of solving Fizz Buzz. I have not blogged about them, as I am not the sort of person who beats a joke to death, but occasionally I will tweet about them, and recently in response someone suggested that I write a book on "100 Ways of Writing Fizz Buzz in Python."Now, I could probably come up with 100 ways of solving Fizz Buzz, but most of them would not be very interesting. Luckily for you, I was able to come up with 10 that are interesting in various ways, each of which turned out to be a good launching-off point for (sometimes meandering) discussions of various aspects of coding, Python, Fizz Buzz, mathematics, software design, technical interviewing, and various other topics.Hence "Ten Essays on Fizz Buzz".In many ways this is a strange book. Its goal is not to teach you a specific field or a specific technology. I hope you will learn a lot from reading it, but it's not really a book that you'd read in order to learn anything in particular. Most technical books are about specific technical topics; this one sort of isn't.Nonetheless, it is a technical book. Each essay contains code that implements a different solution of Fizz Buzz. Each essay uses code to illustrate its ideas. Each essay represents my current best thinking about how to solve problems using code. If you have a coding job, you should feel no reluctance to expense this book to your employer

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms