A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache SparkKey Features\[\*\] Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data\[\*\] Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images\[\*\] A hands-on guide to understanding the nature of data and how to turn i...

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share n...

If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports.Updated for R 2.14 and 2.15, this second edition includes...

This book follows a cookbook style approach that puts orthogonal and non-redundant recipes in your hands. Rather than rehashing the user manual, the explanations expose the underlying logic behind Matplotlib. If you are an engineer or scientist who wants to create great visualizations with Python, rather than yet another specialized language, this is the book for you. While there are several very competent plotting packages, Matplotlib is “just” a Python module. Thus, if you know some Pyth...

Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.

If you know Python and would like to use it for Geospatial Analysis this book is exactly what you ve been looking for. With an organized, user-friendly approach it covers all the bases to give you the necessary skills and know-how.Key FeaturesConstruct applications for GIS development by exploiting PythonFocuses on built-in Python modules and libraries compatible with the Python Packaging Index distribution system – no compiling of C libraries necessa...

Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business c...

Utilize R to uncover hidden patterns in your Big Data Key Features\[\* \] Perform computational analyses on Big Data to generate meaningful results\[\*\] Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases,\[\*\] Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the marketBook DescriptionBig Data an...

· 215 Advanced SAS Interview Questions· 77 HR Interview Questions· Real life scenario based questions· Strategies to respond to interview questions· 2 Aptitude TestsAdvanced SAS Interview Questions You'll Most Likely Be Asked is a perfect companion to stand ahead above the rest in today's competitive job market. Rather than going through comprehensiv...

This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for cer...

Over 60 practical recipes on data exploration and analysisKey Features\[\] Clean dirty data, extract accurate information, and explore the relationships between variables\[\] Forecast the output of an electric plant and the water flow of American rivers using pandas, NumPy, Statsmodels, and scikit-learn\[\] Find and extract the most important features from your dataset using the most efficient Python librariesBook Description...

Master text-taming techniques and build effective text-processing applications with RKey Features\[\*\] Develop all the relevant skills for building text-mining apps with R with this easy-to-follow guide\[\*\] Gain in-depth understanding of the text mining process with lucid implementation in the R language\[\*\] Example-rich guide that lets you gain high-quality information from text dataBook DescriptionText Mining (or text ...