Want To Learn Data Science? Check Out These Data Science Books

In this article, I share the 12 best data science books in 2024.

Whether you’d like to land a job as a data scientist or you want to further your data science career by learning new skills, I’ve included the most up-to-date data science books for beginners and experienced professionals.

In 2024 and beyond, data science remains essential for modern businesses that want to unlock valuable insights from their data while improving efficiency and creating innovative solutions. 

With the ability to add tremendous value, data science remains a highly lucrative field, with the Bureau of Labor Statistics reporting a median salary in excess of $100,000 for data scientists.

So, if you’re ready, let’s review some of the best data science books available in 2024 to help you learn the skills you need to excel as a data scientist. 

How To Choose The Best Data Science Book in 2024?

When looking for the best book to learn data science, we considered the following criteria and recommend you use these as well:

• Author credentials: We looked for authors with extensive experience in data science to ensure they have the necessary expertise to provide you with the knowledge you need.
• Level of experience: We looked for data science books for a range of skill levels, including beginner-friendly books and options or experienced data science professionals.
• Publish Date: Being one of the older programming languages, we looked for a mixture of recent publications and classics that are still relevant for data scientists in 2024.
• Reviews from previous readers: We evaluated first-person reviews from our community and from sites like Amazon to gain valuable insights into each book’s strengths and weaknesses.
• Preferred learning style: Some data science books are more hands-on with practical examples, while others take a more theoretical approach, so we included a range of options to help you find one to match your preferred learning style.

Whichever data science book you choose, we’d also recommend pairing it with one of the world-class AI courses offered by Stanford. With access to thought leaders like Andrew Ng, these courses are an excellent way to complement data science skills with AI and ML.

Best Data Science Books for Beginners

1. Data Science from Scratch: First Principles with Python

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Why we chose this book

If you're starting your journey into data science, Data Science from Scratch by Joel Grus is really an excellent starting point, especially for beginners who want to leverage Python for data science or if you're taking a data science course.

It's also nice that the author has a solid resume, having been a research engineer at the Allen Institute for Artificial Intelligence and a software engineer at Google.

For me, this book stands out for its clear explanation of the fundamentals of data science and its hands-on approach using Python. Of course, you could get into the debate of whether Python or R is better for data science, but let's roll with it and use Python!

I also appreciate how Grus breaks down complex ideas into digestible, easy-to-understand segments. Expect to start with the basics of Python, which is ideal if you're new to the language, before diving into the intricacies of data science. I like this, as it helps form a solid foundation for beginners.

It's also great that each chapter builds upon the last, introducing topics such as statistics, data wrangling, machine learning, and more, all tailored towards practical applications. What I really like is that the 2nd edition focuses on updated techniques and tools, reflecting the latest trends and practices in data science.

Overall, this book is a fantastic starting point for anyone aspiring to understand and apply data science concepts from the ground up. It's a comprehensive guide that not only teaches you the technical skills but also helps you develop the analytical thinking necessary for a data scientist.

Features

• Written by Joel Grus, a seasoned data scientist with real-world experience.
• Python crash course included to get you up to speed.
• Provides a hands-on approach to learning data science with Python.
• Covers fundamental concepts like statistics, machine learning, and data analysis.
• Includes practical examples and exercises to reinforce learning.
• Updated content in the latest edition to reflect current data science practices.
• Focuses on understanding the 'why' behind data science techniques.

2. A Hands-On Introduction to Data Science

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Why we chose this book

Next on my list is this option from Chirag Shah, which is an essential read if you want to gain practical data science and data analytics skills.

Shah’s approach to teaching is very hands-on, which I very much appreciate, and it also focuses on real-world applications and data science projects, making it a pragmatic guide for beginners and intermediate learners alike.

Expect to start with the basics of data manipulation and cleaning, crucial skills for any data scientist, before learning how to handle and prepare data for analysis, a fundamental step in the data science process.

As the book progresses, you delve into more advanced topics like statistical analysis and machine learning. Plus, as promised, you will gain hands-on experience with key techniques like regression analysis, classification, and clustering. These skills are vital for understanding patterns and making predictions from data.

The book also covers essential tools and programming languages used in data science, with a significant focus on Python. This is great, as it means you will learn how to use Python libraries like Pandas for data manipulation, Matplotlib for data visualization, and Scikit-learn for machine learning.

Data visualization, another critical skill, is also thoroughly explored. After all, so much of data science is storytelling, and what better way than with plots? So, get ready to learn how to create insightful, visually appealing representations of data.

It's also nice that in the later chapters, Shah introduces more complex concepts, such as natural language processing (NLP) and deep learning, providing a comprehensive view of the data science landscape.

Features

• Practical skills in data manipulation and cleaning.
• Hands-on experience with statistical analysis and machine learning.
• Proficiency in Python and its libraries for data science tasks.
• In-depth learning of data visualization techniques.
• Introduction to advanced topics like NLP and deep learning.
• Real-world examples and exercises to solidify understanding.

3. Data Science For Dummies

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Why we chose this book

Part of the famous 'For Dummies' series, this option from Lillian Pierson is an excellent starting point for anyone beginning their journey into data science.

For me, what stands out the most about this data science book is how it makes complex concepts accessible to beginners, offering a straightforward, jargon-free introduction to the field of data science.

I also appreciate that from the outset, Pierson focuses on imparting practical skills.

Expect to begin with an overview of what data science is and why it's important before diving into data collection and mining basics, which is ideal for learning how to gather and analyze large sets of data effectively.

Much of the book also explains statistical methods and predictive analytics. This is great, as it means you can learn essential techniques such as regression analysis, classification, and hypothesis testing, which are foundational to making sense of data patterns and trends.

Pierson also introduces the basics of programming for data science, with an emphasis on Python and R, two of the most popular languages in the field. Plus, it's nice to see that there are lots of practical examples and exercises on how to use these programming languages for data analysis.

Data visualization is another key skill covered in the book, so you'll be ready to present data clearly and compellingly with graphs, charts, and other tools.

Another stand-out feature of this book is its coverage of the proprietary STAR Framework, a process that’s been proven to lead profitable data science projects.

To round things off, the later chapters also explore more advanced topics like machine learning and big data technologies, offering a glimpse into the future of data science.

Features

• An easy-to-understand introduction to data science concepts.
• Practical guidance on data collection and data mining.
• Essential techniques in statistical methods and predictive analytics.
• Basics of Python and R programming for data analysis.
• Skills in creating effective data visualizations.
• Lillian Pierson's proprietary STAR Framework for leading profitable data science projects
• Insight into advanced topics like machine learning and big data.

4. Essential Math for Data Science: Take Control of Your Data

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Why we chose this book

One thing's for sure: if you want to pursue data science, you need math! This is why I had to include Essential Math for Data Science by Thomas Nield, as it's an amazing resource for anyone looking to deepen their understanding of the mathematical foundations crucial to data science.

I particularly like how this data science book offers clear and concise explanations of complex mathematical concepts that are tailored for data scientists.

Expect to start out by learning about the basic mathematical principles necessary for data science, including algebra and calculus. The idea here is to refresh your foundational knowledge and ensure you have a solid base to build on with more advanced skills.

For me, one of this book’s key strengths is its focus on statistics and probability, as these are both essential for understanding data analysis and machine learning.

This means you'll be learning about descriptive statistics, probability distributions, and statistical inference, enabling you to interpret data and draw meaningful conclusions effectively.

Linear algebra, another key component of data science, is also covered in detail. This means you'll go in-depth with concepts like vectors, matrices, and linear transformations, which are fundamental in understanding algorithms used in machine learning and data processing.

The book also delves into optimization techniques by covering how to find the most efficient solutions to various data science problems. This includes discussions on gradient descent and other algorithms that are pivotal in machine learning.

Overall, Nield does an excellent job of linking mathematical concepts to real-world data science applications with practical examples and exercises.

I like this as it makes it easier for you to see how these mathematical principles are applied in actual data science tasks; plus, it can really help you feel ready for any upcoming data science interviews you might have planned.

Features

• Comprehensive coverage of algebra, calculus, and their applications in data science.
• In-depth focus on statistics and probability for data analysis.
• Clear explanations of linear algebra concepts crucial for machine learning.
• Practical insights into optimization techniques used in data science.
• Real-world examples linking mathematical theory to data science applications.

5. Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning

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Why we chose this book

Becoming a Data Head is another great option for aspiring data scientists, as it covers not only the hard skills you need to work in data but also the lesser-discussed soft skills you need to succeed. 

I particularly like its approachable style and practical insights, which are ideal for beginners or anyone looking to enhance their data literacy.

From the outset, Gutman demystifies the core concepts of data science and analytics with an accessible introduction to the key terms and principles, such as data types, data structures, and the basics of data collection and storage. This foundational knowledge is crucial for anyone looking to become proficient in data analysis.

I also think this book excels in explaining data analysis techniques in a way that's easy to grasp.

So, not only will you learn about various methods for data exploration, including statistical analysis and data visualization, but you'll benefit from relatable examples and real-life scenarios to help you understand how to apply these techniques to uncover insights from data.

Perhaps the most unique aspect of this data science book is its focus on the human element in data science, including the importance of critical thinking, problem-solving, and communication skills in the field.

These are the types of skills you absolutely need to have, and I like that he provides practical advice on how to interpret data results and communicate findings effectively.

To cap things off, this book also offers insights into popular data analysis tools, including an overview of Excel, SQL, and more specialized data science software.

Features

• Clear explanations of fundamental data science and analytics concepts.
• Guidance on data analysis techniques and their practical application.
• Emphasis on critical thinking and problem-solving skills in data analysis.
• Insightful tips on effective communication of data findings.
• Overview of popular data analysis tools and software.

6. Introduction to Data Science: Data Analysis and Prediction Algorithms with R

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Why we chose this book

Written by a professor of data science and a fellow of the American Statistical Association, Introduction to Data Science by Rafael Irizarry is a great choice for anyone looking for a comprehensive and accessible read, which makes it an excellent choice for both students and professionals who are new to the field.

You'll start out with the fundamental concepts of data science, including the basics of data collection and data types, which are crucial for understanding how to handle and analyze data effectively.

A major portion of this data science book is also dedicated to data visualization and exploratory data analysis (EDA), as you'll learn the importance of visualizing data to uncover patterns, trends, and outliers.

I also like that it provides practical examples using popular data visualization tools, helping you to develop essential skills in presenting data insights.

Statistical inference is another key area that's covered in depth, with concepts like probability, hypothesis testing, and confidence intervals being tackled in a clear and concise way.

You'll even get an introduction to the basics of machine learning, including supervised and unsupervised learning techniques, with concepts like regression, classification, and clustering being discussed.

Finally, you'll also learn about the practical applications of data science with R and Python with a range of hands-on examples and exercises, allowing you to apply what you've learned in real-world data analysis scenarios.

Features

• Comprehensive overview of fundamental data science concepts.
• Detailed guidance on data visualization and exploratory data analysis.
• Clear explanations of statistical inference and its applications.
• Introduction to machine learning techniques and their use in data science.
• Practical programming examples using R and Python.

Best Intermediate Data Science Books

7. Data Science on the Google Cloud Platform

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Why we chose this book

Kicking off our list of intermediate-level data science books is this terrific read from Valliappa Lakshmanan, Director of Analytics and AI Solutions at Google Cloud.

If you're interested in data science in the cloud, especially with the Google Cloud Platform (GCP), this is an essential guide thanks to its practical approach and focus on using Google Cloud's sophisticated tools and services for data science projects.

Expect to start with an introduction to GCP, making it accessible to anyone that's new to cloud computing. You'll also get a detailed overview of the platform's architecture and services, which is invaluable for understanding how to effectively utilize GCP for data science.

For me, this book shines in its coverage of how to set up and manage data processing pipelines on GCP. This is really great, as you'll learn to leverage services like BigQuery for large-scale data analysis, Cloud Dataflow for data processing, and Cloud Machine Learning Engine for building and deploying machine learning models.

This hands-on knowledge is crucial for data scientists who want to work with big data in a cloud environment.

Another plus point for me is the focus on practical scenarios and real-world applications, with case studies and examples that demonstrate how to apply GCP tools in various data science tasks, from data ingestion and cleaning to advanced analytics and machine learning.

It's also nice that this book delves into important topics like building scalable and reliable data pipelines, exploring data using SQL and machine learning, and visualizing data insights. These are all fundamental for data scientists who need to work with complex datasets and derive actionable insights.

Features

• Comprehensive introduction to the Google Cloud Platform for data science.
• Practical guidance on setting up data processing pipelines on GCP.
• In-depth tutorials on using BigQuery, Cloud Dataflow, and Cloud Machine Learning Engine.
• Case studies demonstrating real-world applications of GCP tools in data science.
• Techniques for scalable data analysis, machine learning, and data visualization.

8. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

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Why we chose this book

Remember when I mentioned that math is essential for data science? Well, if you want to be granular, you also need to think stats, as these are possibly some of the most important math skills you need, which is why I had to include Practical Statistics for Data Scientists by Peter Bruce.

If you want to deepen your understanding of statistics within the context of data science, this is such a great read, as it helps to present complex statistical concepts in a practical, easy-to-understand manner, making it ideal for data scientists at all levels.

Expect to start off by getting a solid foundation in descriptive statistics, which is essential for understanding how to summarize and describe data sets effectively. This includes coverage of central tendency measures, variability, and data distribution.

You'll then move on to focus on inferential statistics by learning how to make predictions and generalizations about data. This includes topics like hypothesis testing, confidence intervals, and p-values, all fantastic tools to make informed decisions based on data.

I also appreciate that this book dives into regression analysis, one of the most critical techniques in data science. You'll even learn simple and multiple regression methods, gaining skills in modeling relationships between variables and making predictions.

Another key area covered in this book is exploratory data analysis (EDA). Bruce even emphasizes the importance of EDA in discovering patterns, spotting anomalies, and testing hypotheses in a dataset, providing practical examples to illustrate these concepts.

To round things off, you'll also get an introduction to key machine learning concepts and techniques, such as classification, clustering, and decision trees, demonstrating their application in statistical analysis.

Features

• Solid grounding in descriptive and inferential statistics.
• Detailed explanations of hypothesis testing and regression analysis.
• Practical skills in exploratory data analysis (EDA).
• Introduction to key machine learning concepts for statistical applications.
• Real-world examples demonstrating the application of statistical techniques in data science.

9. Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

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Why we chose this book

Here, we have another great choice for data science in the cloud, but this time, we're talking about Data Science on AWS by Chris Fregly.

If you want to utilize the power and flexibility of Amazon Web Services, this is a great starting point thanks to its practical approach to implementing data science solutions on one of the most popular cloud platforms.

You'll start out with a comprehensive overview of AWS services and architecture, which is really crucial for understanding how to effectively use AWS for data science projects.

With the basics done, you'll then learn how to set up and manage robust data processing pipelines on AWS with AWS services like Amazon S3 for data storage, Amazon EMR for big data processing, and AWS Lambda for serverless computing.

These are all essential skills for handling large-scale data efficiently in the cloud.

It's also nice to see that machine learning on AWS is another major focus, as Fregly guides readers through using Amazon SageMaker, a service that allows data scientists to build, train, and deploy machine learning models at scale.

You also get the added benefit of practical examples and insights into using SageMaker and other AWS machine-learning tools.

Plus, if you're already used to working with popular data science tools and programming languages, such as Python, R, and Jupyter notebooks, you'll really like the sections on AWS integration with them.

To round things off, I also appreciate that real-world case studies and examples are provided throughout the book. This practical application is really helpful for understanding the capabilities and advantages of using AWS in various scenarios.

Features

• Detailed introduction to Amazon Web Services for data science.
• Practical guidance on setting up data processing and storage solutions on AWS.
• In-depth tutorials on using Amazon SageMaker for machine learning.
• Integration of AWS with popular data science tools and languages.
• Real-world case studies demonstrating AWS applications in data science.

Best Advanced Data Scientist Books

10. Cleaning Data for Effective Data Science 

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Why we chose this book

Perhaps one of the most important duties of any data scientist is data cleaning, so it made perfect sense to me to include Cleaning Data for Effective Data Science by David Mertz.

If you want to master one of the most crucial aspects of data science, I think this book really stands out for its detailed and practical approach to this often-overlooked yet critical process of preparing data for analysis.

The book begins by highlighting the importance of clean data in data science, and you'll learn how even the most sophisticated data analysis techniques can lead to misleading results if the underlying data is not properly cleaned and prepared. These are the types of skills that can help you earn a data science certification.

I really appreciate that this data science book provides detailed explanations of various types of data impurities, such as missing values, inconsistent formatting, and outliers. Mertz also covers how to identify these issues along with effective strategies for dealing with them.

Another major plus point of this book is its focus on practical tools and techniques for data cleaning. This means you will learn to use popular programming languages like Python and R, along with their libraries and tools, for data-cleaning tasks.

This even includes detailed explanations on how to use Pandas in Python and dplyr in R for data manipulation and cleaning.

It's also nice to see advanced topics like data transformation and feature engineering, which are essential for preparing data for machine learning models. You also get the benefit of practical examples that show how to transform raw data into formats suitable for analysis.

Features

• In-depth understanding of the importance of data cleaning in data science.
• Techniques to identify and rectify common data impurities.
• Practical guidance on using Python and R for data cleaning.
• Advanced topics in data transformation and feature engineering.
• Iterative approaches to refining data cleaning processes.
• Specific focus on time series data, de-trending, and interpolation

11. Practical Data Science with Python

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Why we chose this book

When it comes to the practical aspects of data science, Python is one of the most popular languages for working professionals, whether they're using popular tools like TensorFlow or Keras.

So, it made a lot of sense to me to include Practical Data Science with Python by Nathan George.

George begins by introducing Python and its significance for data science, making it accessible to readers with varying levels of Python proficiency while also offering a solid foundation for beginners or advanced insights for more experienced programmers.

For me, one of this book’s key strengths is its comprehensive coverage of Python libraries for data science, such as Pandas for data manipulation, NumPy for numerical computations, and Matplotlib and Seaborn for data visualization.

I also like that the author provides practical examples and exercises to help you understand how to leverage these libraries effectively in data analysis.

Expect to delve into critical data science processes like data cleaning, data exploration, and data visualization, with an emphasis on the importance of these processes in deriving meaningful insights from data and how to execute them efficiently using Python.

You'll also learn about various machine learning algorithms and techniques, including supervised and unsupervised learning, and how to implement them using Python’s Scikit-learn library. This is ideal if you want to learn how to develop predictive models and analyze complex datasets.

To round things off, you'll also cover advanced topics like natural language processing (NLP) and deep learning, providing a well-rounded perspective on the applications of Python in data science.

Features

• Introduction to Python and its role in data science.
• In-depth exploration of Python libraries like Pandas, NumPy, and Matplotlib.
• Practical guidance on data cleaning, exploration, and visualization.
• Comprehensive overview of machine learning algorithms and their implementation in Python.
• Insights into advanced data science topics like NLP and deep learning.

12. The Handbook of Data Science and AI

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Why we chose this book

Finishing off my list of data science books is The Handbook of Data Science and AI by Stefan Papp.

If you're looking for an authoritative resource and a deep dive into the interconnected worlds of data science and artificial intelligence, this is a great choice.

Papp begins by laying out the foundational principles of data science, ensuring you have a good understanding of the basics of data analysis, statistics, and data management. This sets the stage for more advanced discussions and ensures you have a solid grounding.

Another unique aspect of this book is its comprehensive coverage of artificial intelligence, particularly its relationship with data science. It's nice to see the author explore the historical context of AI, its evolution, and its current state, providing a thorough background that's often missing in more narrowly focused texts.

Machine learning, a critical component of both data science and AI, is then covered extensively. Expect to dive into various machine learning algorithms, from basic to advanced, and discuss their practical applications. This also includes a focus on deep learning, neural networks, and their increasing importance in AI research and applications.

It's also interesting to see Papp address the ethical and societal implications of data science and AI, an increasingly important aspect as these technologies become more common. He even prompts readers to consider the responsibilities of data scientists and AI practitioners in shaping a future where technology is beneficial and ethical.

Finally, I also like that this book is rich with real-world examples, case studies, and practical applications, bridging the gap between theoretical knowledge and real-world implementation.

Features

• Solid foundation in the principles of data science.
• Comprehensive exploration of artificial intelligence and its evolution.
• In-depth coverage of machine learning and deep learning techniques.
• Discussion of the ethical and societal implications of data science and AI.
• Real-world examples and case studies illustrating practical applications.

Data Science Career Opportunities and Growth

Data science offers a wealth of career opportunities. From data scientist to machine learning engineer, the field is ripe with possibilities. Plus, it’s nice to know that the Bureau of Labor Statistics is projecting 36% growth for data science jobs by 2031. 

If you’re new to the field of data and data science, here are some of the most common roles:

1. Data Scientists not only perform data analysis, but they also design and implement models that use data to predict and optimize outcomes.
2. Machine Learning Engineers apply predictive models and leverage natural language processing while working with vast datasets.
3. Data Engineers prepare the "big data" infrastructure to be analyzed by data scientists.

Wrapping Up

And there you have it, the 12 best data science books to read in 2024, with a range of data science books for beginners and experienced data scientists alike.

As we continue to live in a world defined by data, data science continues to be in high demand by organizations that want to capitalize on the hidden value within their ever-evolving datasets.

By taking the time to review our recommended data science books, you should be able to find a range of data science books that align with your goals and learning style.

Whichever book you choose, we wish you luck as you continue your journey into the world of data science. 

Happy reading!

Are you new to data science and not sure where to start? Check out:

Dataquest’s Career Path for Data Science with Python

Frequently Asked Questions

1. What Is Data Science?

Data Science is an interdisciplinary field combining programming, statistical analysis, and domain expertise to extract insights from data. It uses machine learning and AI models to predict outcomes, enhance decision-making, and discover patterns in data. 

2. Which Are the Best Data Science Books?

The best data science books will vary depending on your experience level and specific interests, and we’d recommend any of the books on our list. That said, if you have little to no background, Data Science from Scratch is a friendly introduction, and if you’re more experienced, we’d recommend Practical Data Science with Python for a great hands-on guide.

3. How Can I Learn Data Science?

To learn data science, start by understanding statistics, mathematics, and programming languages such as Python or R. To get the most out of your time learning data science, consider combining online courses with one of the best data science books. We’d also recommend participating in Kaggle competitions to apply what you've learned.

4. Can 12th Graders Do Data Science?

Yes, 12th graders can begin learning data science, particularly if they're studying calculus, statistics, and programming. Learning Python, a versatile programming language used in data science, is a good start. There are resources like online tutorials and educational platforms tailored for this age group.

5. Can I Learn Data Science in One Year?

Yes, it's possible to learn the basics of Data Science in a year, but proficiency requires consistent practice. This includes learning programming languages, statistics, and machine learning algorithms and applying these skills in real-world projects. Self-study, using resources like our recommended data science books, and following a structured learning path can aid in achieving this.

6. What Book Should I Read for Data Science?

The best book to learn data science depends on your current level and specific area of interest. If you're seeking one comprehensive book for Data Science, consider Data Science from Scratch, as it offers an in-depth overview of the tools, ideas, and principles behind data science. It also includes a crash course in Python, making it a valuable asset for those starting their data science journey.

7. Is Data Science Stressful?

Data science, like any profession, can be stressful at times due to factors like tight project deadlines, data complexities, or high expectations. The role involves continuous learning, which can also feel overwhelming. However, it is often mitigated by the intellectual stimulation and satisfaction derived from solving complex problems and making impactful decisions. 

8. What Is a Data Scientist’s Salary?

The salary of a Data Scientist can vary significantly based on geographical location, years of experience, industry, and the specific role within data science. In 2024, the median base salary for a data scientist in the U.S. is over $100,000 per year. 

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