R for Data Science

R for Data Science
Author: Hadley Wickham,Garrett Grolemund
Publsiher: "O'Reilly Media, Inc."
Total Pages: 492
Release: 2016-12-12
ISBN 10: 1491910364
ISBN 13: 9781491910368
Language: EN, FR, DE, ES & NL

R for Data Science Book Review:

"This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"--

Introduction to Data Science

Introduction to Data Science
Author: Rafael A. Irizarry
Publsiher: CRC Press
Total Pages: 713
Release: 2019-11-20
ISBN 10: 1000708039
ISBN 13: 9781000708035
Language: EN, FR, DE, ES & NL

Introduction to Data Science Book Review:

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.

Python Data Science Handbook

Python Data Science Handbook
Author: Jake VanderPlas
Publsiher: "O'Reilly Media, Inc."
Total Pages: 548
Release: 2016-11-21
ISBN 10: 1491912138
ISBN 13: 9781491912133
Language: EN, FR, DE, ES & NL

Python Data Science Handbook Book Review:

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

Data Science for Business

Data Science for Business
Author: Foster Provost,Tom Fawcett
Publsiher: "O'Reilly Media, Inc."
Total Pages: 414
Release: 2013-07-27
ISBN 10: 1449374298
ISBN 13: 9781449374297
Language: EN, FR, DE, ES & NL

Data Science for Business Book Review:

Annotation This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques.

Doing Data Science

Doing Data Science
Author: Cathy O'Neil,Rachel Schutt
Publsiher: "O'Reilly Media, Inc."
Total Pages: 408
Release: 2013-10-09
ISBN 10: 1449363903
ISBN 13: 9781449363901
Language: EN, FR, DE, ES & NL

Doing Data Science Book Review:

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 processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing 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.

Data Science

Data Science
Author: John D. Kelleher,Brendan Tierney
Publsiher: MIT Press
Total Pages: 280
Release: 2018-04-13
ISBN 10: 0262347032
ISBN 13: 9780262347037
Language: EN, FR, DE, ES & NL

Data Science Book Review:

A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

Getting Started with Data Science

Getting Started with Data Science
Author: Murtaza Haider
Publsiher: IBM Press
Total Pages: 400
Release: 2015-12-14
ISBN 10: 0133991237
ISBN 13: 9780133991239
Language: EN, FR, DE, ES & NL

Getting Started with Data Science Book Review:

Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon.

Build a Career in Data Science

Build a Career in Data Science
Author: Emily Robinson,Jacqueline Nolis
Publsiher: Manning Publications
Total Pages: 354
Release: 2020-03-24
ISBN 10: 1617296244
ISBN 13: 9781617296246
Language: EN, FR, DE, ES & NL

Build a Career in Data Science Book Review:

Summary You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology What are the keys to a data scientist’s long-term success? Blending your technical know-how with the right “soft skills” turns out to be a central ingredient of a rewarding career. About the book Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee. By following clear and simple instructions, you’ll learn to craft an amazing resume and ace your interviews. In this demanding, rapidly changing field, it can be challenging to keep projects on track, adapt to company needs, and manage tricky stakeholders. You’ll love the insights on how to handle expectations, deal with failures, and plan your career path in the stories from seasoned data scientists included in the book. What's inside Creating a portfolio of data science projects Assessing and negotiating an offer Leaving gracefully and moving up the ladder Interviews with professional data scientists About the reader For readers who want to begin or advance a data science career. About the author Emily Robinson is a data scientist at Warby Parker. Jacqueline Nolis is a data science consultant and mentor. Table of Contents: PART 1 - GETTING STARTED WITH DATA SCIENCE 1. What is data science? 2. Data science companies 3. Getting the skills 4. Building a portfolio PART 2 - FINDING YOUR DATA SCIENCE JOB 5. The search: Identifying the right job for you 6. The application: Résumés and cover letters 7. The interview: What to expect and how to handle it 8. The offer: Knowing what to accept PART 3 - SETTLING INTO DATA SCIENCE 9. The first months on the job 10. Making an effective analysis 11. Deploying a model into production 12. Working with stakeholders PART 4 - GROWING IN YOUR DATA SCIENCE ROLE 13. When your data science project fails 14. Joining the data science community 15. Leaving your job gracefully 16. Moving up the ladder

97 Things About Ethics Everyone in Data Science Should Know

97 Things About Ethics Everyone in Data Science Should Know
Author: Bill Franks
Publsiher: O'Reilly Media
Total Pages: 346
Release: 2020-08-06
ISBN 10: 149207263X
ISBN 13: 9781492072638
Language: EN, FR, DE, ES & NL

97 Things About Ethics Everyone in Data Science Should Know Book Review:

Most of the high-profile cases of real or perceived unethical activity in data science aren’t matters of bad intent. Rather, they occur because the ethics simply aren’t thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices. Articles include: Ethics Is Not a Binary Concept—Tim Wilson How to Approach Ethical Transparency—Rado Kotorov Unbiased ≠ Fair—Doug Hague Rules and Rationality—Christof Wolf Brenner The Truth About AI Bias—Cassie Kozyrkov Cautionary Ethics Tales—Sherrill Hayes Fairness in the Age of Algorithms—Anna Jacobson The Ethical Data Storyteller—Brent Dykes Introducing Ethicize™, the Fully AI-Driven Cloud-Based Ethics Solution!—Brian O’Neill Be Careful with "Decisions of the Heart"—Hugh Watson Understanding Passive Versus Proactive Ethics—Bill Schmarzo

30 Second Data Science

30 Second Data Science
Author: Liberty Vittert
Publsiher: 30 Second
Total Pages: 160
Release: 2020-09-29
ISBN 10: 0711259666
ISBN 13: 9780711259669
Language: EN, FR, DE, ES & NL

30 Second Data Science Book Review:

30-Second Data Science is the quickest way to discover how data is a driving force not just in the big issues, such as climate change and healthcare, but in our daily lives. Data science is an entirely new discipline that encompasses a new era of information, from finding criminals to predicting epidemics. But there's more to it than the vast quantities of information gathered by our computers, smartphones, and credit cards. Carefully compiled by experts in the field, 30-Second Data Science covers the basic statistical principles that drive the algorithms, how data affects us in every way--science, society, business, pleasure--along with the ethical quandaries and its future promise of a better world. Each 30-Second entry details a different facet of data science in just 300 words and one picture, showing how the concept of bringing together different types of data, and using powerful computer programs to find patterns no human eye could spot, is already transforming our world. Exploring key ideas and featuring biographies of the people behind them, 30-Second Data Science explains clearly and concisely all you need to know about data science, from basics to ethics.

The 9 Pitfalls of Data Science

The 9 Pitfalls of Data Science
Author: Jay Cordes
Publsiher: Oxford University Press, USA
Total Pages: 272
Release: 2019-07-08
ISBN 10: 0198844395
ISBN 13: 9780198844396
Language: EN, FR, DE, ES & NL

The 9 Pitfalls of Data Science Book Review:

Data science has never had more influence on the world. Large companies are now seeing the benefit of employing data scientists to interpret the vast amounts of data that now exists. However, the field is so new and is evolving so rapidly that the analysis produced can be haphazard at best. The 9 Pitfalls of Data Science shows us real-world examples of what can go wrong. Written to be an entertaining read, this invaluable guide investigates the all too common mistakes of data scientists - who can be plagued by lazy thinking, whims, hunches, and prejudices - and indicates how they have been at the root of many disasters, including the Great Recession. Gary Smith and Jay Cordes emphasise how scientific rigor and critical thinking skills are indispensable in this age of Big Data, as machines often find meaningless patterns that can lead to dangerous false conclusions. The 9 Pitfalls of Data Science is loaded with entertaining tales of both successful and misguided approaches to interpreting data, both grand successes and epic failures. These cautionary tales will not only help data scientists be more effective, but also help the public distinguish between good and bad data science.

Ethics and Data Science

Ethics and Data Science
Author: Mike Loukides,Hilary Mason,Dj Patil
Publsiher: Unknown
Total Pages: 40
Release: 2018-07-25
ISBN 10: 1492078220
ISBN 13: 9781492078227
Language: EN, FR, DE, ES & NL

Ethics and Data Science Book Review:

As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C's) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.

2019 6th Swiss Conference on Data Science SDS

2019 6th Swiss Conference on Data Science  SDS
Author: Melanie Geiger
Publsiher: Unknown
Total Pages: 329
Release: 2019
ISBN 10: 9781728131054
ISBN 13: 1728131057
Language: EN, FR, DE, ES & NL

2019 6th Swiss Conference on Data Science SDS Book Review:

Data Science and Big Data Analytics

Data Science and Big Data Analytics
Author: EMC Education Services
Publsiher: John Wiley & Sons
Total Pages: 432
Release: 2014-12-19
ISBN 10: 1118876229
ISBN 13: 9781118876220
Language: EN, FR, DE, ES & NL

Data Science and Big Data Analytics Book Review:

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are available at www.wiley.com/go/9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

Data Science from Scratch

Data Science from Scratch
Author: Joel Grus
Publsiher: "O'Reilly Media, Inc."
Total Pages: 330
Release: 2015-04-14
ISBN 10: 1491904402
ISBN 13: 9781491904404
Language: EN, FR, DE, ES & NL

Data Science from Scratch Book Review:

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

A Data Science Approach to Behavioural Change

A Data Science Approach to Behavioural Change
Author: Rodrigo Mazorra Blanco
Publsiher: Unknown
Total Pages: 329
Release: 2019
ISBN 10:
ISBN 13: OCLC:1112371908
Language: EN, FR, DE, ES & NL

A Data Science Approach to Behavioural Change Book Review:

JavaScript for Data Science

JavaScript for Data Science
Author: Maya Gans,Toby Hodges,Greg Wilson
Publsiher: CRC Press
Total Pages: 232
Release: 2020-02-03
ISBN 10: 1000028593
ISBN 13: 9781000028591
Language: EN, FR, DE, ES & NL

JavaScript for Data Science Book Review:

JavaScript is the native language of the Internet. Originally created to make web pages more dynamic, it is now used for software projects of all kinds, including scientific visualization and data services. However, most data scientists have little or no experience with JavaScript, and most introductions to the language are written for people who want to build shopping carts rather than share maps of coral reefs. This book will introduce you to JavaScript's power and idiosyncrasies and guide you through the key features of the language and its tools and libraries. The book places equal focus on client- and server-side programming, and shows readers how to create interactive web content, build and test data services, and visualize data in the browser. Topics include: The core features of modern JavaScript Creating templated web pages Making those pages interactive using React Data visualization using Vega-Lite Using Data-Forge to wrangle tabular data Building a data service with Express Unit testing with Mocha All of the material is covered by the Creative Commons Attribution-Noncommercial 4.0 International license (CC-BY-NC-4.0) and is included in the book's companion website at http://js4ds.org . Maya Gans is a freelance data scientist and front-end developer by way of quantitative biology. Toby Hodges is a bioinformatician turned community coordinator who works at the European Molecular Biology Laboratory. Greg Wilson co-founded Software Carpentry, and is now part of the education team at RStudio

Python for Data Science The Ultimate Beginner s Guide to Learn Data Science Analysis and Machine Learning from Scratch with Step by Step Exe

Python for Data Science  The Ultimate Beginner s Guide to Learn Data Science  Analysis  and Machine Learning from Scratch with Step by Step Exe
Author: John Russel
Publsiher: Python Programming
Total Pages: 140
Release: 2020-09-22
ISBN 10: 9781913922443
ISBN 13: 1913922448
Language: EN, FR, DE, ES & NL

Python for Data Science The Ultimate Beginner s Guide to Learn Data Science Analysis and Machine Learning from Scratch with Step by Step Exe Book Review:

Looking for methods that you can use to make yourself more competitive in your industry? Or are you worried about what your customers may think about your products or services? The truth is...In the modern world, data has become fundamental, and companies are finding new ways to use the insights provided by data to improve their bottom line and customer experience. This is sometimes a challenge. There is so much data that figuring out what steps to take, and what is found in that data is not always as easy as we would like. The good news is that working with data science can help you learn more about your customers and your industry, with the use of a simple coding language, and give you the insights and predictions that you need to see some great improvements with your business. And that's what you'll learn in Python for Data Science. Python for Data Science is going to spend some time looking at all of the neat things that we can do with data science, so you can finally beat out the competition and increase your bottom line, all while helping out the customer. You will learn: Why Data Science is so Important in our Fast-Paced World of Today 5 Practical Applications of Data Science Best Data Science Library that will help us to Get our Work Done with Python A Simple Technique to Set Up our Virtual Environment Proven Strategies to Get High-Quality Data Tips and Tricks to Get your Data Organized Data Analytics and Why It is so Important What Machine Learning is all About and How it Fit into your Data Science Projects Learning how data science works and how to complete your own data analysis in the process is going to be very important to the amount of success that you are going to see. Even if you're completely new to data science or you've done some programming before and are looking to switch to an exciting new career track in Data Science, Python for Data Science will teach you all the practical techniques used by real-world data scientists and analysts to solve problems. Would You Like to Know More? Get this Book Now to Master Python for Data Science!

Machine Learning

Machine Learning
Author: Kevin P. Murphy
Publsiher: MIT Press
Total Pages: 1067
Release: 2012-08-24
ISBN 10: 0262018020
ISBN 13: 9780262018029
Language: EN, FR, DE, ES & NL

Machine Learning Book Review:

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

R for Health Data Science

R for Health Data Science
Author: Ewen Harrison,Riinu Pius
Publsiher: CRC Press
Total Pages: 346
Release: 2020-12-31
ISBN 10: 1000226166
ISBN 13: 9781000226164
Language: EN, FR, DE, ES & NL

R for Health Data Science Book Review:

In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses. Features Provides an introduction to the fundamentals of R for healthcare professionals Highlights the most popular statistical approaches to health data science Written to be as accessible as possible with minimal mathematics Emphasises the importance of truly understanding the underlying data through the use of plots Includes numerous examples that can be adapted for your own data Helps you create publishable documents and collaborate across teams With this book, you are in safe hands – Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years’ combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms.