Data science is a relatively new knowledge domain that requires the successful integration of linear algebra, statistical modeling, visualization, computational linguistics, graph analysis, machine learning, business intelligence, and data storage and retrieval.
The Python programming language, having conquered the scientific community during the last decade, is now an indispensable tool for the data science practitioner and a must-have tool for every aspiring data scientist. Python will offer you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving. Whatever stopped you before from mastering Python for data science applications will be easily overcome by our easy, step-by-step, and example-oriented approach, which will help you apply the most straightforward and effective Python tools to both demonstrative and real-world datasets.
As the third edition of Python Data Science Essentials, this book offers updated and expanded content. Based on the recent Jupyter Notebook and JupyterLab interface (incorporating interchangeable kernels, a truly polyglot data science system), this book incorporates all the main recent improvements in NumPy, pandas, and scikit-learn. Additionally, it offers new content in the form of new GBM algorithms (XGBoost, LightGBM, and CatBoost), deep learning (by presenting Keras solutions based on TensorFlow), beautiful visualizations (mostly due to seaborn), and web deployment (using bottle).
This book starts by showing you how to set up your essential data science toolbox in Python's latest version (3.6), using a single-source approach (implying that the book's code will be easily reusable on Python 2.7 as well). Then, it will guide you across all the data munging and preprocessing phases in a manner that explains all the core data science activities related to loading data, transforming, and fixing it for analysis, and exploring/processing it. Finally, the book will complete its overview by presenting you with the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.