Learn the techniques and math you need to start making sense of your data
* Enhance your knowledge of coding with data science theory for practical insight into data science and analysis
* More than just a math class, learn how to perform real-world data science tasks with R and Python
* Create actionable insights and transform raw data into tangible value
BOOK more » DESCRIPTION
Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking―and answering―complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas.
With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.
WHAT YOU WILL LEARN
* Get to know the five most important steps of data science
* Use your data intelligently and learn how to handle it with care
* Bridge the gap between mathematics and programming
* Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results
* Build and evaluate baseline machine learning models
* Explore the most effective metrics to determine the success of your machine learning models
* Create data visualizations that communicate actionable insights
* Read and apply machine learning concepts to your problems and make actual predictions
ABOUT THE AUTHOR
Sinan Ozdemir is a data scientist, startup founder, and educator living in the San Francisco Bay Area with his dog, Charlie; cat, Euclid; and bearded dragon, Fiero. He spent his academic career studying pure mathematics at Johns Hopkins University before transitioning to education. He spent several years conducting lectures on data science at Johns Hopkins University and at the General Assembly before founding his own start-up, Legion Analytics, which uses artificial intelligence and data science to power enterprise sales teams.
After completing the Fellowship at the Y Combinator accelerator, Sinan has spent most of his days working on his fast-growing company, while creating educational material for data science.
TABLE OF CONTENTS
1. How to Sound Like a Data Scientist
2. Types of Data
3. The Five Steps of Data Science
4. Basic Mathematics
5. Impossible or Improbable – A Gentle Introduction to Probability
6. Advanced Probability
7. Basic Statistics
8. Advanced Statistics
9. Communicating Data
10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
11. Predictions Don't Grow on Trees – or Do They?
12. Beyond the Essentials
13. Case Studies « less
The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches more » "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science.
From the reviews:Langtangen … does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. … Summing Up: Highly recommended.
F. H. Wild III, Choice, Vol. 47 (8), April 2010
Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.”
John D. Cook, The Mathematical Association of America, September 2011
This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science.
Alex Small, IEEE, CiSE Vol. 14 (2), March/April 2012
“This fourth edition is a wonderful, inclusive textbook that covers pretty much everything one needs to know to go from zero to fairly sophisticated scientific programming in Python…”
Joan Horvath, Computing Reviews, March 2015 « less
Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts
ABOUT THIS BOOK
* Grasp the major methods of predictive modeling and move beyond black box thinking to a deeper level of understanding
* Leverage the flexibility and modularity of R to experiment with a range of different techniques and data types
* Packed with practical advice and tips explaining more » important concepts and best practices to help you understand quickly and easily
WHO THIS BOOK IS FOR
This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed to be a reference for experienced professionals wanting to brush up on the details of a particular type of predictive model. Mastering Predictive Analytics with R assumes familiarity with only the fundamentals of R, such as the main data types, simple functions, and how to move data around. No prior experience with machine learning or predictive modeling is assumed, however you should have a basic understanding of statistics and calculus at a high school level.
WHAT YOU WILL LEARN
* Master the steps involved in the predictive modeling process
* Learn how to classify predictive models and distinguish which models are suitable for a particular problem
* Understand how and why each predictive model works
* Recognize the assumptions, strengths, and weaknesses of a predictive model, and that there is no best model for every problem
* Select appropriate metrics to assess the performance of different types of predictive model
* Diagnose performance and accuracy problems when they arise and learn how to deal with them
* Grow your expertise in using R and its diverse range of packages
R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems.
This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real world data sets.
By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics. « less
1,001 Practice Problems
1001 Calculus Practice Problems For Dummies takes you beyond the instruction and guidance offered in Calculus For Dummies, giving you 1001 opportunities to practice solving problems from the major topics in your calculus course. Plus, an online component provides you with a collection of calculus problems more » presented in multiple-choice format to further help you test your skills as you go.
The practice problems in 1001 Calculus Practice Problems For Dummies range in areas of difficulty and style, providing you with the practice help you need to score high at exam time. « less
Stump’s Programming Language Foundations is a short concise text that covers semantics, equally weighting operational and denotational semantics for several different programming paradigms: imperative, concurrent, and functional.Programming Language Foundations provides: an even coverage of denotational, more » operational an axiomatic semantics; extensions to concurrent and non-deterministic versions; operational semantics for untyped lambda calculus; functional programming; type systems; and coverage of emerging topics and modern research directions. « less
This engaging book presents the essential mathematics needed to describe, simulate, and render a 3D world. Reflecting both academic and in-the-trenches practical experience, the authors teach you how to describe objects and their positions, orientations, and trajectories in 3D using mathematics. The more » text provides an introduction to mathematics for game designers, including the fundamentals of coordinate spaces, vectors, and matrices. It also covers orientation in three dimensions, calculus and dynamics, graphics, and parametric curves. « less
A Tutorial with R and BUGS
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students more » or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods.
-Accessible, including the basics of essential concepts of probability and random sampling
-Examples with R programming language and BUGS software
-Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis).
-Coverage of experiment planning
-R and BUGS computer programming code on website
-Exercises have explicit purposes and guidelines for accomplishment « less
Illustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.
Divided more » into three parts, the book begins with a short introduction to cryptography and a background chapter on elementary number theory and algebra. It then moves on to algorithms, with each chapter in this section dedicated to a single topic and often illustrated with simple cryptographic applications. The final part addresses more sophisticated cryptographic applications, including LFSR-based stream ciphers and index calculus methods.
Accounting for the impact of current computer architectures, this book explores the algorithmic and implementation aspects of cryptanalysis methods. It can serve as a handbook of algorithmic methods for cryptographers as well as a textbook for undergraduate and graduate courses on cryptanalysis and cryptography. « less