Practical Data Science Cookbook Second Edition

Over 85 recipes to help you complete real-world data science projects in R and Python About This Book * Tackle every step in the data science pipeline and use it to acquire clean analyze and visualize your data * Get beyond the theory and implement real-world projects in data science using R and Python * Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on real-world project examples this is the book for you. Whether you are brand new to data science or you are a seasoned expert you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn * Learn and understand the installation procedure and environment required for R and Python on various platforms * Prepare data for analysis by implement various data science concepts such as acquisition cleaning and munging through R and Python * Build a predictive model and an exploratory model * Analyze the results of your model and create reports on the acquired data * Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics this book covers how to set up your numerical programming environment introduces you to the data science pipeline and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis-R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline ranging from readying the dataset to analytics and visualization About the Author Prabhanjan Tattar has 9 years of experience as a statistical analyst. His main thurst has been to explain statistical and machine learning techniques through elegant programming which will clear the nuances of the underlying mathematics. Survival analysis and statistical inference are his main areas of research/interest and he has published several research papers in peer-reviewed journals and also has authored two books on R: R Statistical Application Development by Example Packt Publishing and A Course in Statistics with R Wiley. He also maintains the R packages gpk RSADBE and ACSWR.Tony Ojeda is an accomplished data scientist and entrepreneur with expertise in business process optimization and over a decade of experience creating and implementing innovative data products and solutions. He has a master's degree in finance from Florida International University and an MBA with a focus on strategy and entrepreneurship from DePaul University. He is the founder of District Data Labs is a cofounder of Data Community DC and is actively involved in promoting data science education through both organizations.Sean Patrick Murphy spent 15 years as a senior scientist at The Johns Hopkins University Applied Physics Laboratory where he focused on machine learning modeling and simulation signal processing and high performance computing in the Cloud. Now he acts as an advisor and data consultant for companies in San Fran