Beginner Data Science Level

If you're new to data science, consider these introductory texts:

The book Data Science from Scratch offers a foundational overview tailored for those unfamiliar with the field. No prior knowledge of Python is necessary, making it an excellent starting point.

Introduction to Machine Learning with Python is another great resource for novices eager to delve into machine learning concepts without needing existing Python expertise.

For those aspiring to become data scientists, my book Data Science Job: How to Become a Data Scientist serves as a comprehensive guide. Drawing from my experiences as a project manager and data science analyst, this book covers:

• Steps to secure your first data science position,
• Essential skills to acquire,
• Key points to highlight in job interviews,
• And more.

Intermediate Data Science Level

Once you have read a few books and completed some hands-on projects, the following titles can help deepen your understanding:

Python for Data Analysis emphasizes utilizing NumPy and pandas, making it a valuable resource for those looking to refine their data handling skills.

The Python Data Science Handbook serves as an essential guide to commonly used Python libraries, including Pandas, Scikit-Learn, and NumPy. This detailed workbook provides practical methods for data manipulation, complete with illustrations and clear explanations.

In this book, you'll learn to work with:

• Jupyter and iPython for data processing,
• NumPy for efficient array creation,
• Pandas for data analysis with DataFrames,
• Matplotlib for versatile plotting,
• Scikit-learn for advanced machine learning algorithms.

Python Machine Learning straddles the line between intermediate and advanced topics, catering to both novices and experts. It begins with foundational concepts and progresses to sophisticated techniques.

The second edition of Hands-on Machine Learning with Scikit-Learn and TensorFlow is an invaluable resource covering foundational topics and extending into neural networks and deep learning.

For those interested in finance, Python for Finance is a crucial read that illustrates how data science techniques can be applied to analyze capital markets.

Expert Data Science Level

Experienced data scientists should consider focusing on research papers rather than books, as they provide a more realistic perspective and insights into deep learning applications.

Deep Learning with Python, authored by a key figure behind the Keras library, offers practical techniques that can be readily applied to real-world scenarios, making it an essential read.

Deep Learning is a definitive reference for algorithms in this domain, providing limited code while offering substantial insights into solving machine learning challenges.

For those with a passion for mathematics, Machine Learning: a Probabilistic Perspective provides an in-depth look at the mathematical foundations of machine learning. I recommend taking your time with this book—perhaps over a cup of coffee.

How to Become a Data Scientist in 2024?

I hope these recommendations assist you in becoming a more proficient data scientist! If you're looking for your first entry-level position in data science and are unsure where to start, consider enrolling in my Data Science Job course. Inside, I offer direct support to help you transition to a junior data scientist role.

If you found this article helpful, be sure to explore my other resources on becoming a data scientist:

• Complete guide to becoming a Data Scientist
• A practical guide to becoming a Data Scientist
• Recommendations for Data Science books and courses

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