- 1. Practical statistics for data scientists
- 2. Bayesian Reasoning and Machine Learning
- 3. The Elements of Statistical Learning
- 4. Probability and Statistics for Data Science
- 5. Statistics for data scientists
- 6. OpenIntro Statistics
- 7. Statistics for Data Science
- 8. Computer age statistical inference
- 9. Think Stats
- Conclusion
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Statistics is a crucial foundation of data science. Data scientists use various statistical tools, such as linear regression, support vector machines, and classification methods, to organize, analyze, and contextualize data effectively. These tools enable them to derive meaningful insights and make informed decisions based on the data.
Statistical concepts such as sampling, randomization, distribution, and bias are integral to data science. These principles are frequently applied and highly useful for data scientists. Aspiring data scientists are strongly encouraged to obtain R certification, as statistical aptitude is essential in this field. R programming is widely regarded as the most appropriate language for data science due to its robust capabilities in performing statistical analysis and its versatility in creating applications and software to execute these analyses reliably.
A successful data science career relies not only on theoretical and practical knowledge but also on continuous learning and networking. Building comprehensive expertise involves interacting with peers, mentors, and industry experts. Utilizing diverse resources such as books, blogs, and articles is crucial. There is always more to learn and an abundance of knowledge sources available, making ongoing education essential for staying current in the field.
This article reviews books that are widely regarded as practical, resourceful, and valuable for anyone looking to establish a solid foundation in statistics as a basis for their data science career.
1. Practical statistics for data scientists
Author: Peter C. Bruce, Andrew Bruce & Peter Gedeck
Best for: A beginner who requires a background in statistics about data science alone.
This book bridges the gap between data science and statistics in a very practical guide. It handles statistics from the perspective of data science. If you already have a background in R programming and the basics of statistics, you may want to hone some data exploration, random sampling, regression, classification, and machine learning techniques for better and more informed data handling and ultimately deeper insights from data.
2. Bayesian Reasoning and Machine Learning
Author: David Barber
Best for: Final year undergraduate or Master’s students without a strong background in calculus and linear algebra.
Bayesian Reasoning and Machine Learning is a book with broad coverage that has done well to present machine learning through the bayesian perspective. This is rare because there are not many ML books presented with a statistical approach, let alone a Bayesian approach. Barber comprehensively unfolds the concepts of Bayesian reasoning using graphic illustrations allowing the reader to learn how to present random variables alongside their dependencies. This Bayesian approach of using graphical models in data representation is general enough to accommodate various algorithms and approaches. Each chapter caps off with an exercise to test the reader’s understanding.
3. The Elements of Statistical Learning
Author: Jerome H Friedman, Robert Tishbirani & Trevor Hastie
Best for: Beginners and intermediate data scientists looking to enhance their data representation skills.
Hailed as the bible of machine and statistical learning, this book covers key elements and concepts in data science comprehensively. These include data mining, machine learning, bioinformatics, neural networks, support vector machines, classification trees, and boosting. It gives a very balanced blend of statistics and data science, in that the reader does not feel too inclined to either data science or statistical knowledge. It also profoundly elaborates various concepts and tools in statistics, and how they integrate into the field of data science.
4. Probability and Statistics for Data Science
Author: Norman Matloff
Best for: Students, and practicing data scientists who learn about statistics and probability concepts later in their upper graduate level and not early enough.
This book is an introduction of probability and statistics concepts to both students and graduates of data science and is a great resource to indulge in ahead of advanced statistics. It comes loaded with real data sets for practical data analysis with R programming and includes several data science applications such as random graph models, linear and logistic regression, neural networks, and more. However, it is important to have a background in matrix algebra, R programming, and calculus before using this valuable resource.
5. Statistics for data scientists
Author: Maurits Kaptein & Edwin Van Den Heuvel
Best for: Data science students interested in statistical data analysis for big data and streaming data
This book covers an exhaustive introduction to data analysis, applying a reusable R code to solve real-world problems using real datasets. With a strong emphasis on probability and statistical principles, this book’s focus on the not-so-often covered bootstrapping and Bayes statistical analysis methods specifically for big data and streaming data making it a great resource in an era where big data and streaming data analysis carries the day for most businesses.
6. OpenIntro Statistics
Author: David M. Diez, Mine Cetinkaya-Rundel & Christopher D Barr
Best for: Both students and employees in the data science field seek to get a strong foundation in statistics that they can build on later.
This is one of the three statistics textbooks approved by the American Institute of Mathematics for use in Mathematics undergraduate degree courses. It is an open-source book that covers the foundational elements of statistics like inference, probability, and regression, in a way that is easily understandable allowing for both self-and instructor-led study. Also coming up with case studies to bring out concepts in a real-world setting makes this a great resource.
7. Statistics for Data Science
Author: James Miller
Best for: Anyone who wants to get a concrete statistics background before pursuing data science, though it has proved quite useful to professionals in the field.
This is a very comprehensive statistics Book that covers everything that its title highlights which is: “Leveraging the power of statistics for data analysis, classification, regression, machine learning, and neural networks.” It is very detailed and it has been hailed as a complete course guidebook and foundation for data science. Build your knowledge on implementing statistics such as linear regression, boosting, model assessment, and neural networks in data science processes like cleaning, mining, and analysis with a basis on R programming.
8. Computer age statistical inference
Author: Bradley Efron & Trevor Hastie
Best for: Not a coursebook but a book for all who want to appreciate the evolution journey of statistics and data analysis.
This book gives a historical account of the development of statistics and data analysis since the end of the 19th century, the latest invention of data; big data, data science, and machine learning. To project the future of data analysis. It captures classical inferential theories as well as contemporary statistical analysis techniques like The Markov chain Monte Carlo, logistic regression, Bootstrap, survival analysis, random forests, and much more.
9. Think Stats
Author: Allen B Downey
Best for: Data scientists who wish to learn computational data analysis using Python programming.
This book introduces beginners to computational statistical analysis with Python programming. It specifically covers concepts of probability and statistics like distributions and visualization. In the end, you will have mastered how to write and test code, generate samples, and process data right from collecting/importing, cleaning, generating statistics, analysis, and visualizing data. Some programming experience in Python is thus required to use this book since it is based on the Python library for probability distributions.
Conclusion
If you aspire to become an effective data scientist working for top companies and commanding a premium salary, experiential knowledge is certainly not complete without reference to statistical books like the ones we have reviewed in this article. Statistics is the basis of data science. As such, a strong foundation in mathematics and statistics sets you on the right path to achieving your data science career aspirations. Most of the books we have reviewed in this article are intended for beginners and students yet they have proved to be a great resource even for refined data scientists who occasionally would need to refresh foundational statistical analysis knowledge.
