Principles of Data Science
Master the core concepts of modern analytics with this comprehensive principles of data science guide. Whether you’re starting your data journey or seeking to strengthen your analytical foundation, this book offers practical insights into the techniques, tools, and theories that power real-world data science solutions.
What You’ll Learn
- Understanding the data science workflow from data collection to decision-making
- Exploratory data analysis and data visualization techniques
- Supervised and unsupervised machine learning methods
- Statistical inference and probability theory in data contexts
- Model evaluation, performance metrics, and validation techniques
- Working with data using Python, NumPy, pandas, and scikit-learn
- Ethics and responsibility in data science
Requirements
- Basic understanding of Python programming
- Familiarity with high school-level math and statistics
- Curiosity to explore and analyze data
Book Description
This principles of data science book is a foundational guide for aspiring data scientists, analysts, and developers. It demystifies the data science process—from asking the right questions and cleaning data to building predictive models and communicating results. With a hands-on approach, the book integrates theory with coding exercises using Python’s most popular data libraries.
Through real-world examples and intuitive explanations, you’ll gain a deeper understanding of how to explore data, apply machine learning algorithms, and draw actionable insights. You’ll also discover how to evaluate model performance and avoid common data pitfalls. Ethical concerns and the importance of responsible AI are also covered to help you think critically about your analyses.
By the end, you’ll have a strong command of data science fundamentals and the confidence to apply them in real projects or further studies.
About the Author
Written by an experienced data scientist and educator, this book bridges academic theory with real-world application. The author brings years of experience working with startups, enterprises, and research labs in applying data science to solve practical problems.
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