Data science aims to find patterns within data and use the patterns to make data-driven decisions. Accordingly, data scientists leverage statistics, research, analysis, and machine learning principles to uncover patterns from raw data.
Before data scientists can create a meaningful impact for their organizations, however, they must become well-versed in the fundamental principles of data science. Then, data experts can pursue specialties within the realm of data science. The following books can help with both of these goals.
Jump to:
- Data Science from Scratch
- Data Science for Dummies
- Designing Data-Intensive Applications
- Introduction to Machine Learning with Python
- Practical Statistics for Data Scientists
- Data Science and Big Data Analytics
- R for Data Science
- Big Data: A Revolution That Will Transform How We Live, Work and Think
- Practical Data Science with R
Data Science from Scratch
Data Science from Scratch focuses on helping data scientists learn more math and statistics. Beginners can also develop hacking skills and exposure to natural language processing used with artificial intelligence applications.
Data Science for Dummies
The word “dummies” in the title should not sway experienced data scientists away from this book. Data Science for Dummies covers an expansive range of data science topics, including big data and data engineering. Any data scientist interested in programming languages can learn basic concepts with this book.
Designing Data-Intensive Applications
Designing Data-Intensive Applications introduces readers to the pros and cons of the available technologies for processing and storing data. Data scientists with software engineering backgrounds can become more valuable to their organizations by using data to its fullest capability in modern applications.
Introduction to Machine Learning with Python
Introduction to Machine Learning with Python: A Guide for Data Scientists introduces machine learning solutions using Python. Readers will gain a basic understanding of machine learning (ML) concepts. This book is a good starting point for beginners who want to learn ML programming and experienced data scientists wanting to expand their skills.
Practical Statistics for Data Scientists
Practical Statistics for Data Scientists tells us why data analysis is a crucial first step in data science. This book demonstrates how a data scientist can handle a large amount of data using random sampling to eliminate bias and generate a higher-quality dataset.
Data Science and Big Data Analytics
Data Science and Big Data Analytics focuses on big data and its importance in a digital world. The book describes the data analytics lifecycle in detail and provides easy-to-understand visuals. The author also covers clustering, regression, and association rules.
R for Data Science
R for Data Science describes the messiness of raw data and how it is processed. Readers will begin to understand the transformation of data and the processes involved in transforming data into meaningful information. This book also offers a way for interested users to learn R via an online course.
Big Data: A Revolution That Will Transform How We Live, Work and Think
Big Data: A Revolution That Will Transform How We Live, Work and Think explains how businesses use data and the information shared over the internet. Additionally, it covers how security measures are put in place to avoid breaches and misuse of data.
Practical Data Science with R
Practical Data Science with R is considered a medium-level book that exposes the reader to basic and advanced data science principles. This book also explains the role of statistics; it focuses on the how and why rather than only describing how things are done.
How data scientists impact businesses
Data scientists use statistical methods, algorithm development, and other processes to generate meaningful data and derive business insights. The daily tasks of a data scientist include the following:
- Asking questions to frame the business problem
- Collecting data that address the business problem
- Cleaning and organizing the data
- Modeling and presenting data so others can understand the information
Some of these tasks may require focused training, which is why it’s important to invest in the right educational materials. Whether you’re looking to learn more about data science in a general sense or dive deeper into a specific topic under the data science umbrella, the books on this list provide a good starting point.
