# Practical Statistics For Data Scientists

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## Practical Statistics for Data Scientists

Author | : Peter Bruce,Andrew Bruce,Peter Gedeck |

Publsiher | : O'Reilly Media |

Total Pages | : 350 |

Release | : 2020-06-09 |

ISBN | : 9781492072942 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Practical Statistics for Data Scientists Book Excerpt:**

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide--now including examples in Python as well as R--explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning

## Statistics for Data Scientists

Author | : Bri Bruce, Of,Andrew Bruce |

Publsiher | : O'Reilly Media |

Total Pages | : 250 |

Release | : 2016-10 |

ISBN | : 9781491952962 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Statistics for Data Scientists Book Excerpt:**

A key component of data science is statistics and machine learning, but only a small proportion of data scientists are actually trained as statisticians. This concise guide illustrates how to apply statistical concepts essential to data science, with advice on how to avoid their misuse. Many courses and books teach basic statistics, but rarely from a data science perspective. And while many data science resources incorporate statistical methods, they typically lack a deep statistical perspective. This quick reference book bridges that gap in an accessible, readable format.

## Practical Statistics for Data Scientists

Author | : Peter Bruce,Andrew Bruce,Peter Gedeck |

Publsiher | : O'Reilly Media |

Total Pages | : 363 |

Release | : 2020-04-10 |

ISBN | : 1492072915 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Practical Statistics for Data Scientists Book Excerpt:**

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that "learn" from data Unsupervised learning methods for extracting meaning from unlabeled data

## Practical Statistics for Data Scientists 2nd Edition

Author | : Peter Bruce |

Publsiher | : Unknown |

Total Pages | : 93 |

Release | : 2020 |

ISBN | : 1928374650XXX |

Category | : Electronic Book |

Language | : EN, FR, DE, ES & NL |

**Practical Statistics for Data Scientists 2nd Edition Book Excerpt:**

Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.

## Practical Data Science with R

Author | : John Mount,Nina Zumel |

Publsiher | : Simon and Schuster |

Total Pages | : 568 |

Release | : 2019-11-17 |

ISBN | : 1638352747 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Practical Data Science with R Book Excerpt:**

This invaluable addition to any data scientist's library shows you how to apply the R programming language and useful statistical techniques to everyday business situations as well as how to effectively present results to audiences of all levels. To answer the ever-increasing demand for machine learning and analysis, this new edition boasts additional R tools, modeling techniques, and more. Practical Data Science with R, Second Edition takes a practice-oriented approach to explaining basic principles in the ever-expanding field of data science. You'll jump right to real-world use cases as you apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

## Statistics

Author | : John Slavio |

Publsiher | : Unknown |

Total Pages | : 88 |

Release | : 2019-07-24 |

ISBN | : 9781922300232 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**Statistics Book Excerpt:**

This book is a great reference for you to get started with statistics.

## Foundations of Statistics for Data Scientists

Author | : ALAN. KATERI AGRESTI (MARIA.),Maria Kateri |

Publsiher | : CRC Press |

Total Pages | : 488 |

Release | : 2024-09-15 |

ISBN | : 9780367748432 |

Category | : Electronic Book |

Language | : EN, FR, DE, ES & NL |

**Foundations of Statistics for Data Scientists Book Excerpt:**

Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python. The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises. Alan Agresti, Distinguished Professor Emeritus at the University of Florida, is the author of seven books, including Categorical Data Analysis (Wiley) and Statistics: The Art and Science of Learning from Data (Pearson), and has presented short courses in 35 countries. His awards include an honorary doctorate from De Montfort University (UK) and the Statistician of the Year from the American Statistical Association (Chicago chapter). Maria Kateri, Professor of Statistics and Data Science at the RWTH Aachen University, authored the monograph Contingency Table Analysis: Methods and Implementation Using R (Birkhäuser/Springer) and a textbook on mathematics for economists (in German). She has a long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, and Business Administration and Engineering. "The main goal of this textbook is to present foundational statistical methods and theory that are relevant in the field of data science. The authors depart from the typical approaches taken by many conventional mathematical statistics textbooks by placing more emphasis on providing the students with intuitive and practical interpretations of those methods with the aid of R programming codes...I find its particular strength to be its intuitive presentation of statistical theory and methods without getting bogged down in mathematical details that are perhaps less useful to the practitioners" (Mintaek Lee, Boise State University) "The aspects of this manuscript that I find appealing: 1. The use of real data. 2. The use of R but with the option to use Python. 3. A good mix of theory and practice. 4. The text is well-written with good exercises. 5. The coverage of topics (e.g. Bayesian methods and clustering) that are not usually part of a course in statistics at the level of this book." (Jason M. Graham, University of Scranton)

## Advancing into Analytics

Author | : George Mount |

Publsiher | : "O'Reilly Media, Inc." |

Total Pages | : 262 |

Release | : 2021-01-22 |

ISBN | : 1492094293 |

Category | : Electronic Book |

Language | : EN, FR, DE, ES & NL |

**Advancing into Analytics Book Excerpt:**

Data analytics may seem daunting, but if you're an experienced Excel user, you have a unique head start. With this hands-on guide, intermediate Excel users will gain a solid understanding of analytics and the data stack. By the time you complete this book, you'll be able to conduct exploratory data analysis and hypothesis testing using a programming language. Exploring and testing relationships are core to analytics. By using the tools and frameworks in this book, you'll be well positioned to continue learning more advanced data analysis techniques. Author George Mount, founder and CEO of Stringfest Analytics, demonstrates key statistical concepts with spreadsheets, then pivots your existing knowledge about data manipulation into R and Python programming. This practical book guides you through: Foundations of analytics in Excel: Use Excel to test relationships between variables and build compelling demonstrations of important concepts in statistics and analytics From Excel to R: Cleanly transfer what you've learned about working with data from Excel to R From Excel to Python: Learn how to pivot your Excel data chops into Python and conduct a complete data analysis

## Practical Data Science with Python

Author | : Nathan George |

Publsiher | : Packt Publishing Ltd |

Total Pages | : 620 |

Release | : 2021-09-30 |

ISBN | : 1801076650 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Practical Data Science with Python Book Excerpt:**

Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.

## Practical Statistics for the Analytical Scientist

Author | : S. L. R. Ellison,Trevor J. Farrant,Vicki Barwick |

Publsiher | : Royal Society of Chemistry |

Total Pages | : 283 |

Release | : 2009 |

ISBN | : 0854041311 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**Practical Statistics for the Analytical Scientist Book Excerpt:**

Analytical chemists must use a range of statistical tools in their treatment of experimental data to obtain reliable results. Practical Statistics for the Analytical Scientist is a manual designed to help them negotiate the daunting specialist terminology and symbols. Prepared in conjunction with the Department of Trade and Industry's Valid Analytical Measurement (VAM) programme, this volume covers the basic statistics needed in the laboratory. It describes the statistical procedures that are most likely to be required including summary and descriptive statistics, calibration, outlier testing, analysis of variance and basic quality control procedures. To improve understanding, many examples provide the user with material for consolidation and practice. The fully worked answers are given both to check the correct application of the procedures and to provide a template for future problems. Practical Statistics for the Analytical Scientist will be welcomed by practising analytical chemists as an important reference for day to day statistics in analytical chemistry.

## Quantitative Economics with R

Author | : Vikram Dayal |

Publsiher | : Springer Nature |

Total Pages | : 326 |

Release | : 2020-02-03 |

ISBN | : 9811520356 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**Quantitative Economics with R Book Excerpt:**

This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.

## Practical Statistics for Environmental and Biological Scientists

Author | : John Townend |

Publsiher | : John Wiley & Sons |

Total Pages | : 272 |

Release | : 2013-04-30 |

ISBN | : 1118687418 |

Category | : Science |

Language | : EN, FR, DE, ES & NL |

**Practical Statistics for Environmental and Biological Scientists Book Excerpt:**

All students and researchers in environmental and biologicalsciences require statistical methods at some stage of their work.Many have a preconception that statistics are difficult andunpleasant and find that the textbooks available are difficult tounderstand. Practical Statistics for Environmental and BiologicalScientists provides a concise, user-friendly, non-technicalintroduction to statistics. The book covers planning and designingan experiment, how to analyse and present data, and the limitationsand assumptions of each statistical method. The text does not referto a specific computer package but descriptions of how to carry outthe tests and interpret the results are based on the approachesused by most of the commonly used packages, e.g. Excel, MINITAB andSPSS. Formulae are kept to a minimum and relevant examples areincluded throughout the text.

## Practical Data Science with Jupyter

Author | : Prateek Gupta |

Publsiher | : BPB Publications |

Total Pages | : 360 |

Release | : 2021-03-01 |

ISBN | : 9389898064 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Practical Data Science with Jupyter Book Excerpt:**

Solve business problems with data-driven techniques and easy-to-follow Python examples KEY FEATURES ● Essential coverage on statistics and data science techniques. ● Exposure to Jupyter, PyCharm, and use of GitHub. ● Real use-cases, best practices, and smart techniques on the use of data science for data applications. DESCRIPTION This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms. WHAT YOU WILL LEARN ● Rapid understanding of Python concepts for data science applications. ● Understand and practice how to run data analysis with data science techniques and algorithms. ● Learn feature engineering, dealing with different datasets, and most trending machine learning algorithms. ● Become self-sufficient to perform data science tasks with the best tools and techniques. WHO THIS BOOK IS FOR This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples. TABLE OF CONTENTS 1. Data Science Fundamentals 2. Installing Software and System Setup 3. Lists and Dictionaries 4. Package, Function, and Loop 5. NumPy Foundation 6. Pandas and DataFrame 7. Interacting with Databases 8. Thinking Statistically in Data Science 9. How to Import Data in Python? 10. Cleaning of Imported Data 11. Data Visualization 12. Data Pre-processing 13. Supervised Machine Learning 14. Unsupervised Machine Learning 15. Handling Time-Series Data 16. Time-Series Methods 17. Case Study-1 18. Case Study-2 19. Case Study-3 20. Case Study-4 21. Python Virtual Environment 22. Introduction to An Advanced Algorithm - CatBoost 23. Revision of All Chapters’ Learning

## Data Science for Business Professionals

Author | : Probyto Data Science and Consulting Pvt. Ltd. |

Publsiher | : BPB Publications |

Total Pages | : 368 |

Release | : 2020-05-06 |

ISBN | : 9389423287 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Data Science for Business Professionals Book Excerpt:**

Primer into the multidisciplinary world of Data Science KEY FEATURES - Explore and use the key concepts of Statistics required to solve data science problems - Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app - Learn how to build Data Science solutions with GCP and AWS DESCRIPTION The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset. WHAT WILL YOU LEARN - Understand the multi-disciplinary nature of Data Science - Get familiar with the key concepts in Mathematics and Statistics - Explore a few key ML algorithms and their use cases - Learn how to implement the basics of Data Pipelines - Get an overview of Cloud Computing & DevOps - Learn how to create visualizations using Tableau WHO THIS BOOK IS FOR This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science. TABLE OF CONTENTS 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business Intelligence 16. Data Visualization Tools 17. Industry Use Case 1 – FormAssist 18. Industry Use Case 2 – PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments

## Practical Statistics

Author | : Charles Felton Pidgin |

Publsiher | : Unknown |

Total Pages | : 201 |

Release | : 1888 |

ISBN | : 1928374650XXX |

Category | : Statistics |

Language | : EN, FR, DE, ES & NL |

**Practical Statistics Book Excerpt:**

## The Data Science Handbook

Author | : Field Cady |

Publsiher | : John Wiley & Sons |

Total Pages | : 40 |

Release | : 2017-02-28 |

ISBN | : 1119092949 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**The Data Science Handbook Book Excerpt:**

A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master 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 rote 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, computer science and software engineering are given extensive 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 highlighted. Classical statistics is addressed to help readers think critically about the interpretation 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 chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials 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 presented, 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 appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.

## Practical Statistics a Handbook for the Use of the Statistician at Work

Author | : Charles Felton Pidgin |

Publsiher | : Unknown |

Total Pages | : 201 |

Release | : 1888 |

ISBN | : 1928374650XXX |

Category | : Electronic Book |

Language | : EN, FR, DE, ES & NL |

**Practical Statistics a Handbook for the Use of the Statistician at Work Book Excerpt:**

## Practical Statistics for Field Biology

Author | : Jim Fowler,Lou Cohen,Phil Jarvis |

Publsiher | : John Wiley & Sons |

Total Pages | : 272 |

Release | : 2013-06-20 |

ISBN | : 1118685644 |

Category | : Science |

Language | : EN, FR, DE, ES & NL |

**Practical Statistics for Field Biology Book Excerpt:**

Provides an excellent introductory text for students on the principles and methods of statistical analysis in the life sciences, helping them choose and analyse statistical tests for their own problems and present their findings. An understanding of statistical principles and methods is essential for any scientist but is particularly important for those in the life sciences. The field biologist faces very particular problems and challenges with statistics as "real-life" situations such as collecting insects with a sweep net or counting seagulls on a cliff face can hardly be expected to be as reliable or controllable as a laboratory-based experiment. Acknowledging the peculiarites of field-based data and its interpretation, this book provides a superb introduction to statistical analysis helping students relate to their particular and often diverse data with confidence and ease. To enhance the usefulness of this book, the new edition incorporates the more advanced method of multivariate analysis, introducing the nature of multivariate problems and describing the the techniques of principal components analysis, cluster analysis and discriminant analysis which are all applied to biological examples. An appendix detailing the statistical computing packages available has also been included. It will be extremely useful to undergraduates studying ecology, biology, and earth and environmental sciences and of interest to postgraduates who are not familiar with the application of multiavirate techniques and practising field biologists working in these areas.

## Targeted Learning in Data Science

Author | : Mark J. van der Laan,Sherri Rose |

Publsiher | : Springer |

Total Pages | : 640 |

Release | : 2018-03-28 |

ISBN | : 3319653040 |

Category | : Mathematics |

Language | : EN, FR, DE, ES & NL |

**Targeted Learning in Data Science Book Excerpt:**

This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.

## Data Science with Java Practical Methods for Scientists and Engineers

Author | : Michael R. Brzustowicz |

Publsiher | : O'Reilly Media |

Total Pages | : 300 |

Release | : 2016-04-25 |

ISBN | : 9781491934111 |

Category | : Computers |

Language | : EN, FR, DE, ES & NL |

**Data Science with Java Practical Methods for Scientists and Engineers Book Excerpt:**

A good data scientist knows how to do something really well, but a great data scientist can do "something of everything." From raw data all the way to shining in front of C-level executives, a great data scientist has the skills to architect data systems, build applications, perform modeling and machine learning and wrap up the results in a clear (and quickly iterable) manner. From data models to ETL to databases to distributed algorithms and learning, this book has you covered. While many resources for Java (and data science) exist, none of them combine the two, and especially not at a level where sophisticated concepts are demonstrated clearly and in simplest terms. Data Science with Java marries the two in a practical way. Learn an extremely practical set of tools for creating enterprise grade data science applications Get past the intimidating barrier to machine learning and statistics—and learn how useful object-oriented code can be