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Data science from scratch : first principles with Python / by Joel Grus.

By: Grus, JoelMaterial type: TextTextLanguage: English Publication details: Mumbai : Shroff Publishers and Distributors Pvt. Ltd.. 2019. Edition: 2nd edDescription: xvii, 384 p. : ill. ; 23 cmISBN: 9789352138326; 9781492041139Subject(s): COMPUTERS Programming Languages PythonDDC classification: 005.133 Online resources: WorldCat Details
Contents:
Table of contents Introduction A crash course in Python Visualizing data Linear algebra Statistics Probability Hypothesis and inference Gradient descent Getting data Working with data Machine learning k-Nearest neighbors Naive bayes Simple linear regression Multiple regression Logistic regression Decision trees Neural networks Deep learning Clustering Natural language processing Network analysis Recommender systems Databases and SQL MapReduce Data ethics Go forth and do data science
Summary: 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
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
Text Text Dr. S. R. Lasker Library, EWU
Reserve Section 		Non-fiction 005.133 GRD 2019 (Browse shelf(Opens below)) C-1 Not For Loan 31639
Text Text Dr. S. R. Lasker Library, EWU
Circulation Section 		Non-fiction 005.133 GRD 2019 (Browse shelf(Opens below)) C-2 Available 31640
Total holds: 0

Includes bibliographical references and index

Table of contents Introduction
A crash course in Python
Visualizing data
Linear algebra
Statistics
Probability
Hypothesis and inference
Gradient descent
Getting data
Working with data
Machine learning
k-Nearest neighbors
Naive bayes
Simple linear regression
Multiple regression
Logistic regression
Decision trees
Neural networks
Deep learning
Clustering
Natural language processing
Network analysis
Recommender systems
Databases and SQL
MapReduce
Data ethics
Go forth and do data science

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

Computer Science & Engineering Computer Science & Engineering

Sagar Shahanawaz

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