A Friendly Guide To Data Science
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A Friendly Guide to Data Science
Unlock the world of data science—no coding required. Curious about data science but not sure where to start? This book is a beginner-friendly guide to what data science is and how people use it. It walks you through the essential topics—what data analysis involves, which skills are useful, and how terms like “data analytics” and “machine learning” connect—without getting too technical too fast. Data science isn’t just about crunching numbers, pulling data from a database, or running fancy algorithms. It’s about asking the right questions, understanding the process from start to finish, and knowing what’s possible (and what’s not). This book teaches you all of that, while also introducing important topics like ethics, privacy, and security—because working with data means thinking about people, too. Whether you're a student exploring new skills, a professional navigating data-driven decisions, or someone considering a career change, this book is your friendly gateway into the world of data science, one of today’s most exciting fields. No coding or programming experience? No problem. You'll build a solid foundation and gain the confidence to engage with data science concepts— just as AI and data become increasingly central to everyday life. What You Will Learn Grasp foundational statistics and how it matters in data analysis and data science Understand the data science project life cycle and how to manage a data science project Examine the ethics of working with data and its use in data analysis and data science Understand the foundations of data security and privacy Collect, store, prepare, visualize, and present data Identify the many types of machine learning and know how to gauge performance Prepare for and find a career in data science Who This Book is for A wide range of readers who are curious about data science and eager to build a strong foundation. Perfect for undergraduates in the early semesters of their data science degrees, as it assumes no prior programming or industry experience. Professionals will find particular value in the real-world insights shared through practitioner interviews. Business leaders can use it to better understand what data science can do for them and how their teams are applying it. And for career changers, this book offers a welcoming entry point into the field—helping them explore the landscape before committing to more intensive learning paths like degrees or boot camps.
Mastering Machine Learning: A Friendly Guide to Understanding How AI Learns
If you've ever wondered how Netflix always knows what you want to watch… If you've felt overwhelmed by the buzz around artificial intelligence but wished someone would just explain it simply… If you're a student, professional, or curious mind looking to use AI without needing a tech degree… This book is for you. Demystifying the Smart Tech Behind Chatbots, Face Recognition, and Predictive Magic—For Curious Minds of All Ages Mastering Machine Learning: A Friendly Guide to Understanding How AI Learns is your god-sent crash course into the invisible power behind the tech we use every day. It’s not just a book—it’s your personal guide to unlocking smart solutions for everyday problems. Packed with: ✅ Tips & Tricks anyone can use, with step-by-step guides for building your own smart tools ✅ Real-life stories of how machine learning has transformed homes, classrooms, and businesses ✅ Eye-popping illustrations & relatable analogies that make complex ideas surprisingly easy ✅ DIY projects & cheat sheets for hands-on learning—even if you’re tech-shy ✅ Ethical insights to help you use AI responsibly and wisely ✅ Bonus content on how sci-fi inspired today’s smart tech Whether you're a curious teen, a creative entrepreneur, or a life-long learner, this book is your backstage pass into the world of learning machines—and how they can help you learn, grow, and thrive. GET YOUR COPY TODAY! 🚀
Practitioner’s Guide to Data Science
This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes. Key Features: • It covers both technical and soft skills. • It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment. • It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!