Название: Data Science in 30 Days: The Complete Beginner's GuideАвтор: Aniket Jain
Издательство: Independently published
Серия: Zero to Hero in 30 Days: The Accelerated Learning Series
Год: 2025
Страниц: 179
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB
Data Science in 30 Days: The Complete Beginner’s Guide is the ultimate hands-on roadmap for anyone looking to master Data Science for dummies and transition into the world of data-driven decision-making. Designed to take you from an absolute beginner to a confident data practitioner in just 30 days, this book provides a structured, easy-to-follow approach to learning Data Science for beginners efficiently.
With step-by-step lessons, real-world examples, and hands-on exercises, this book is perfect for those looking for a Python Data Science book that balances theory with practical applications. Whether you are a student, professional, or hobbyist, you’ll find everything you need to get started with Data Science projects with Python and apply it in various domains such as business analytics, finance, Artificial Intelligence, and Big Data processing.
NumPy (Numerical Python) is one of the most essential and widely used libraries for data manipulation in Python. It provides robust support for numerical computing, making it an indispensable tool for data scientists, researchers, and engineers. With NumPy, users can perform complex mathematical operations efficiently on massive datasets, ensuring optimal performance and reduced execution time. Unlike traditional Python lists, NumPy arrays offer significantly faster processing speeds, better memory management, and an extensive suite of built-in functions that cater to diverse computational needs, making it the go-to choice for handling numerical data in modern Data Science applications.
Pandas is a fundamental Python library used for data analysis and manipulation. It provides efficient, flexible, and user-friendly data structures, primarily the DataFrame and Series, which enable the handling of structured data in an intuitive manner. A DataFrame is essentially a two-dimensional labeled data structure similar to a table in a relational database or an Excel spreadsheet. It allows data to be stored in rows and columns, making it easier to perform operations such as filtering, sorting, grouping, and aggregation. This structure enables complex data handling with minimal code, significantly improving productivity and readability for data scientists and analysts.
What You’ll Learn in 30 Days:
- The fundamentals of Data Science, including data collection, cleaning, and preprocessing
- How to use Python, NumPy, Pandas, and Matplotlib for Python for data science tasks
- Exploratory data analysis (EDA) techniques to uncover insights from data
- Introduction to statistics, probability, and hypothesis testing for data science for business applications
- Supervised and unsupervised machine learning models, including regression, classification, and clustering
- Working with databases, SQL queries, and big data tools like Apache Spark
- Advanced topics such as data science for finance, deep learning, NLP, time series analysis, and model deployment
Why This Book Stands Out:
- A structured day-by-day learning plan to keep you on track
- Practical exercises and coding challenges to reinforce each concept
- Covers both beginner and intermediate topics, making it ideal for continuous learning
- A bonus section with cheat sheets, common errors, and interview questions
If you’re looking for the best Python Data Science book that breaks down complex concepts into digestible lessons, this book is for you. Whether you’re interested in Data Science projects with Python, preparing for a data science bootcamp, or simply want to explore Data Science for business in an easy-to-understand manner, this book provides a complete, practical guide.
By the end of 30 days, you’ll have the confidence and skills to analyze data, build Machine Learning models, and derive meaningful insights. If you're looking for the best Data Science for dummies guide to start your journey, this is it.
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