Category: Data Science Education/Training Review: Data science is a rapidly growing field that requires a strong foundation in mathematics, statistics, and computer programming. For those looking to enter this exciting industry or improve their skills, there are many resources available online. In this review, we will examine several data science education and training products that aim to help learners master the basics of data science using Python as the primary language. 1. Data Science from Scratch: From Data Visualization To Manipulation - This book is designed for beginners who want to learn data science with Python. It covers all the basic principles of data manipulation, visualization, and analysis using Python libraries such as pandas, matplotlib, and seaborn. The author also provides practical examples that help learners understand how to apply these concepts in real-world scenarios. Overall, this book is an excellent starting point for anyone looking to get into data science with Python. 2. Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn - This book provides learners with hands-on experience in building real-world data science projects using Python. It covers topics such as data cleaning, feature engineering, model selection, and evaluation. The author also includes detailed explanations of each step in the project development process, making it easy for beginners to follow along. 3. Data Science from Scratch: First Principles with Python - This book is similar to the first one on our list but focuses more on the theoretical aspects of data science. It covers topics such as linear algebra, statistics, and machine learning algorithms using Python libraries like NumPy, Pandas, and scikit-learn. The author also provides practical examples that help learners understand how these concepts are applied in real-world scenarios. 4. Data Science from Scratch: The #1 Data Science Guide for Everything a Data Scientist Needs to Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, and Decision Trees - This book is designed for beginners who want to learn data science with Python. It covers all the basic principles of data manipulation, visualization, and analysis using Python libraries such as pandas, matplotlib, and seaborn. The author also provides practical examples that help learners understand how to apply these concepts in real-world scenarios. 5. Data Science from Scratch: Ultimate Guide To Master Data Mining And Data-Analytic From Linear Algebra And Statistics To Practical Examples Of Neural Networks and Decision Trees - This book is similar to the first one on our list but focuses more on advanced topics in data science such as neural networks, decision trees, and deep learning. It covers these concepts using Python libraries like TensorFlow, Keras, and Scikit-learn. The author also provides practical examples that help learners understand how these concepts are applied in real-world scenarios. 6. Linear Algebra for Data Science: A Hands-On Approach with Python - This book is designed specifically for data scientists who want to improve their understanding of linear algebra and its applications in data science. It covers topics such as matrix operations, eigenvalue decomposition, and principal component analysis using Python libraries like NumPy and Pandas. The author also provides practical examples that help learners understand how these concepts are applied in real-world scenarios. 7. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking - This book is designed for business professionals who want to improve their understanding of data science and its applications in the workplace. It covers topics such as data cleaning, feature engineering, model selection, and evaluation using Python libraries like Pandas, Scikit-learn, and TensorFlow. The author also provides practical examples that help learners understand how these concepts are applied in real-world scenarios. 8. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data - This book is designed specifically for data scientists who want to improve their understanding of the R programming language and its applications in data science. It covers topics such as data import, cleaning, visualization, and analysis using R libraries like dplyr, ggplot2, and caret. The author also provides practical examples that help learners understand how these concepts are applied in real-world scenarios. 9. Data Science from Scratch with Python: Concepts and Practices with NumPy, Pandas, Matplotlib, Scikit-Learn and Keras - This book is similar to the first one on our list but focuses more on advanced topics in data science such as deep learning and neural networks using Python libraries like TensorFlow and Keras. The author also provides practical examples that help learners understand how these concepts are applied in real-world scenarios. 10. Learning In Python (Computer Networking Easy) - This book is designed for beginners who want to improve their understanding of computer networking using Python. It covers topics such as TCP/IP, DNS, HTTP, and FTP protocols using Python libraries like socket, urllib, and requests. The author also provides practical examples that help learners understand how these concepts are applied in real-world scenarios. Overall, the data science education and training products listed above provide a comprehensive overview of the basics to advanced topics in data science using Python as the primary language. These books are well-written, easy to follow along, and provide practical examples that help learners understand how these concepts are applied in real-world scenarios.