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A comprehensive overview of data science covering the analytics,
programming, and business skills necessary to mas
ter the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of
technical skills is simply very hard to find in one person. In addition, good data science is not just ro
te
application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book
provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, compu
ter science and software engineering are given ex
tensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world
applications. Visualization tools are reviewed, and their central importance in data science is highligh
ted. Classical statistics is addressed to help readers think critically about the in
terpretation of data and its common pitfalls. The clear communication of
technical results, which is perhaps the most undertrained of data science skills, is given its own chap
ter, and all topics are explained in the con
text of solving real-world data
problems. The book also features: Ex
tensive sample code and tuto
rials using Python™ along with its
technical libraries Core
technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world
problems Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presen
ted, it is done in an intuitive way to encourage critical thinking and creativity A wide variety of case studies from industry Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is
app
rop
ria
te for people who want to practice data science, but lack the required skill sets. This includes software
professionals who need to bet
ter understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presen
ted as such. This book is also an
app
rop
ria
te reference for researchers and entry-level gradua
te students who need to learn real-world analytics and expand their skill set. FIELD CADY is Principal Data Scientist at Maana, Inc. where he
applies Big Data tools to solve indust
rial
problems. He has a BS in Physics from Stanford University, an MS in
Applied Mathematics from the University of Washington, and an MS in Compu
ter Science from Carnegie Mellon University. Table of Con
tents Chap
ter 1 Introduction: Becoming a Unicorn Part I The Stuff You’ll Always Use Chap
ter 2 The Data Science Road Map Chap
ter 3
Programming Languages Chap
ter 4 Data Munging: String Manipulation, Regular Expressions, and Data Cleaning Chap
ter 5 Visualizations and Simple Metrics Chap
ter 6 Machine Learning Overview Chap
ter 7 In
terlude: Feature Extraction Ideas Chap
ter 8 Machine Learning Classification Chap
ter 9
Technical Communication and Documentation Part II Stuff You Still Need to Know Chap
ter 10 Unsupervised Learning: Clus
tering and Dimensionality Reduction Chap
ter 11 Regression Chap
ter 12 Data Encodings and File Formats Chap
ter 13 Big Data Chap
ter 14 Databases Chap
ter 15 Software Engineering Best Practices Chap
ter 16 Natural Language
Processing Chap
ter 17 Time Series Analysis Chap
ter 18
Probability Chap
ter 19 Statistics Chap
ter 20
Programming Language Concepts Chap
ter 21 Performance and Compu
ter Memory Part III Specialized or Advanced Topics Chap
ter 22 Compu
ter Memory and Data Structures Chap
ter 23 Maximum Likelihood Estimation and Optimization Chap
ter 24 Advanced Classifiers Chap
ter Datagrip激活2022.3 25 Stochastic Modeling Chap
ter 25a Parting Words: Your Future as Datagrip激活2022.3 a Data Scientist