What is the worst software for data science
8 books on data science for beginners
In recent years, public interest in data science has skyrocketed. What was once considered a rather exotic discipline is now a topic everywhere in news, politics, international law and social networks. Data literacy is becoming an important requirement in all kinds of industries, and customers use data points to access huge business intelligence systems on a daily basis.
Whether you just want to stay up to date on the ubiquitous topic of data or want to get started as an expert in data science and data literacy, this article has a few books that can help you get started in the world of data science.
1. "The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists" by Carl Shan, William Chen, Henry Wang, and Max Song
Author: Carl Shan, William Chen, Henry Wang and Max Song
Website:The Data Science Handbook | Amazon
Those who need reliable information are often best advised to get it first hand - from professionals in the respective field. So what could be more obvious than interviewing 25 leading industry experts? "The Data Science Handbook" contains interviews with outstanding data scientists - from former US data protection officers to team leaders in large companies and up-and-coming data scientists who develop their own programs - and thus offers unique insights into the subject.
The selection of interviews guides newcomers through the industry and provides them with practical data advice, learning experiences, career tips and success strategies for the world of data science. The technical aspects of the subject do not play a major role, nor does the book claim to be a comprehensive guide. It is a real treasure trove of practical recommendations and findings.
2. "Doing Data Science: Straight Talk from the Frontline" by Cathy O'Neil and Rachel Schutt
Author: Cathy O'Neil and Rachel Schutt
Website:O'Reilly | Amazon
“Doing Data Science” does not hesitate. The book is based on New York's Columbia University's introductory course in data science and is aimed at beginners who are new to the subject. That is why the data science consultant Cathy O'Neil and the course instructor Rachel Schutt have teamed up to make the content of this course accessible to the general public.
The book not only contains informative lectures on the topic, but also presents practical examples in a clear way using relevant case studies and code. Algorithms, methods, models and data visualization are dealt with. The result is a practical technical reference work.
3. "Data Science - what is that actually ?! Machine learning algorithms clearly explained ”by Annalyn Ng and Kenneth Soo
Author: Annalyn Ng and Kenneth Soo
Data science has a lot to do with mathematics and can therefore be difficult to access or even daunting. “Data Science - what is that actually ?!” offers an easy-to-understand, less math-heavy introduction to data science and algorithms that takes away the horror of the topic.
Each chapter is dedicated to a particularly useful algorithm and roughly explains how it works as well as real applications in practice. Clear graphics make it easier to understand the underlying processes. The work is rounded off by overviews with the most important advantages and disadvantages of each algorithm as well as a practical glossary of common data science terms.
4. "The Art of Data Science" by Roger D. Peng and Elizabeth Matsui
Author: Roger D. Peng and Elizabeth Matsui
"The Art of Data Science" deals with how valuable data insights can be obtained from practically any data source. The focus is on the process of data analysis and filtering in order to get to the bottom of the data message. Building on their own experience, the authors guide newcomers and managers step by step through data science and the associated analysis processes.
Both authors have led data projects and led teams of analysts themselves. Based on their own professional practice, they explain which methods reliably lead to success and which pitfalls data projects can be fatal.
5. "Data Science for Dummies" by Lillian Pierson
Author: Lillian Pierson
The "Dummies" series has long been known for introducing laypeople to topics they are unfamiliar with in a generally understandable way. This is exactly what the volume “Data Science for Dummies” has set itself the goal of. It focuses primarily on the business aspects of data science and thus serves as an introduction for everyone who wants to work in this field. To help you get started, it gives a comprehensive overview of the discipline. Readers get to know the most important terms from the field of big data and learn how data science can be applied in everyday life.
The book also touches on related topics, including data engineering, programming languages such as R and Python, machine learning, algorithms, artificial intelligence, and data visualization techniques. So, if you're curious about data science, or just want to be able to explain the basics of what data science is to your parents, this book is a great place to start.
6. "Big Data for Dummies" by Judith Hurwitz, Alan Nugent, Fern Halper and Marcia Kaufman
Author: Judith Hurwitz, Alan Nugent, Fern Halper and Marcia Kaufman
Speaking of data science and the “Dummies” series: The book series also offers an introduction to the topic of big data and its significance. The book answers the central question “What is Big Data?” And illuminates it from both a technical and a business perspective. It shows how big data is used in business intelligence applications and can help analysts investigate and solve problems.
In addition, the book contains technical advice, for example on how to organize and support the data collected or how to adapt methods and tools for data analysis for your purposes. Big Data for Dummies is useful when you want to understand your data, make sense of it, and put it to practical use in a business environment.
7. "Data Jujitsu: The Art of Turning Data into Product" by DJ Patil
Author: DJ Patil
If you could ask just one person for advice on data science, the former chief data scientist of the United States Office of Science and Technology Policy - the agency that advises the US president on science and technology policy - certainly wouldn't be the worst Contact Person. DJ Patil is considered to be the inventor of the term “data science”, and in “Data Jujitsu” he presents data science as a kind of problem-solving mentality.
He goes into various problems in data-driven industries and emphasizes that there is a difference between problems that are difficult to solve and really unsolvable problems. Complex problems can be solved by breaking them down into simpler parts and then examining them with data analysis methods. "Data Jujitsu" gives numerous examples and offers recommendations on how you can harness the power of data for yourself.
8. "Big Data: A Revolution That Will Transform How We Live, Work, and Think" by Viktor Mayer-Schönberger and Kenneth Cukier
Author: Viktor Mayer-Schönberger and Kenneth Cukier
Big data has become a long-term topic in the news. Data companies are gaining more and more influence, hackers are attacking bank and personal data, political debates are raging, and new data protection laws are coming into force. This book discusses the impact data has on nearly every area of our lives - in our private and business lives, as well as at government levels and in individual disciplines of science.
Mayer-Schönberger and Cukier explain how algorithms, simply by analyzing our online behavior, can reveal things about us that we have previously kept secret. Online retailers can recommend products or predict purchase patterns based on our surfing behavior, social networks serve political prejudices and act as echo chambers. Even our love life is affected, because dating apps also use data. Anyone who tries to curb the flow of personal data must also ensure that their data does not fall into the wrong hands. This book covers the frightening, fascinating, or simply highly interesting methods that our data will use to shape our lives not only in the future, but already today.
Disclaimer: The publications featured in this article and the positions contained therein do not represent or benefit from the official position of Tableau. This page is in no way related to affiliate link programs. This article is for informational purposes only; The above information about products and publications is only made available to enable readers to make independent, informed decisions.
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