Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code.As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices.This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science.__Practical Data Science with Python__ will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors.**What You'll Learn** * Play the role of a data scientist when completing increasingly challenging exercises using Python 3 * Work work with proven data science techniques/technologies * Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data * Apply theory of probability, statistical inference, and algebra to understand the data science practices **Who This Book Is For**Anyone who would like to embark into the realm of data science using Python 3. Gain insight into essential data science skills in a holistic manner using data engineering and associated scalable computational methods. This book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. Along the way, you will be introduced to many popular open-source frameworks, like, SciPy, scikitlearn, Numba, Apache Spark, etc. The book is structured around examples, so you will grasp core concepts via case studies and Python 3 code. As data science projects gets continuously larger and more complex, software engineering knowledge and experience is crucial to produce evolvable solutions. You'll see how to create maintainable software for data science and how to document data engineering practices. This book is a good starting point for people who want to gain practical skills to perform data science. All the code will be available in the form of IPython notebooks and Python 3 programs, which allow you to reproduce all analyses from the book and customize them for your own purpose. You'll also benefit from advanced topics like Machine Learning, Recommender Systems, and Security in Data Science. Practical Data Science with Python will empower you analyze data, formulate proper questions, and produce actionable insights, three core stages in most data science endeavors. What You'll Learn Play the role of a data scientist when completing increasingly challenging exercises using Python 3 Work work with proven data science techniques/technologies Review scalable software engineering practices to ramp up data analysis abilities in the realm of Big Data Apply theory of probability, statistical inference, and algebra to understand the data science practices Who This Book Is For Anyone who would like to embark into the realm of data science using Python 3. Front Matter ....Pages i-xvii Introduction to Data Science (Ervin Varga)....Pages 1-27 Data Engineering (Ervin Varga)....Pages 29-71 Software Engineering (Ervin Varga)....Pages 73-119 Documenting Your Work (Ervin Varga)....Pages 121-158 Data Processing (Ervin Varga)....Pages 159-207 Data Visualization (Ervin Varga)....Pages 209-253 Machine Learning (Ervin Varga)....Pages 255-316 Recommender Systems (Ervin Varga)....Pages 317-339 Data Security (Ervin Varga)....Pages 341-367 Graph Analysis (Ervin Varga)....Pages 369-396 Complexity and Heuristics (Ervin Varga)....Pages 397-425 Deep Learning (Ervin Varga)....Pages 427-450 Back Matter ....Pages 451-462
