R Programming In Data Science Dates And Times

One of the fundamental difficulties of data science is working with dates and times. This course shows data engineers, D... (more)

One of the fundamental difficulties of data science is working with dates and times. This course shows data engineers, DevOps practitioners, and data-science programmers the most common (and many not so common!) problems and how to use R-based tools to implement solutions. Learn how dates and times are stored and retrieved in base R. Find out how to format, compare, add and subtract, and extract dates and times using built-in R functions. Then discover how to incorporate specialized R packages, such as lubridate, busdater, zoo, timelineR, anytime, datetime, and more, to perform some of the heavy lifting. Instructor Mark Niemann-Ross walks you through each package, so you can appreciate the advantages and best uses of each one. (less)

R is powerful, but not intuitive. There is a strong and diverse R ecosystem, and data scientists are expected to mix and... (more)

R is powerful, but not intuitive. There is a strong and diverse R ecosystem, and data scientists are expected to mix and match from the different versions and packages. Before you can even begin programming, you have to choose, install, and set up R to work for you. In this course, Mark Niemann-Ross provides a direct and efficient introduction to the many flavors of the R programming language, including base R, tidyverse R, R Open from Microsoft, and Bioconductor R. He also provides a peek at programming with R interactively and via the command line, and introduces some helpful packages for working with SQL, 3D graphics, data, and clusters in R. At the end of this short course, you will have installed a version of R along with a few core libraries and an optimized IDE. (less)

In a perfect world, every dataset would be stored as XML text with context for every piece of information. Numbers would... (more)

In a perfect world, every dataset would be stored as XML text with context for every piece of information. Numbers would never be stored as strings. Decimal values would never be stored as scientific notation. Strings would never be longer than 500 characters. But obviously, we don't live in a perfect world of data. And big data only makes this issue, well, bigger. This is the problem of variety; data arriving in multiple formats. Data scientists spend an inordinate amount of time with this problem, using brain power that would be better spent on valuable analysis tasks. In this course, Mark Niemann-Ross introduces the problem of data variety and demonstrates how to use the unique capabilities of R to solve them. Learn how to import a wide variety of data, from Excel to ODS files. (less)

High-velocity data-such as the information that springs from Twitter and IoT devices-comes barreling in at a speed beyon... (more)

High-velocity data-such as the information that springs from Twitter and IoT devices-comes barreling in at a speed beyond normal comprehension, demanding high-performance from both hardware and software. While it might not initially appear up to the challenge, the R programming language can be revved up to operate with high-velocity data. Written close to the metal by sitting directly on top of the C programming language, R provides a rich set of data structures and concepts. This course drills down into efficient R programming, providing practical strategies that can help you work your mojo on high-velocity data. Instructor Mark Niemann-Ross begins by sharing a framework for understanding the different types of high-velocity data. He then covers how to use R to acquire high-velocity data, as well as how to leverage profiling tools and optimize R code for use with high-velocity data. He wraps up by exploring how to use R to present data, including how to use Shiny-an R package that allows you to build web apps straight from R-for interactive dashboards. (less)

Data fills all available space, and now that storage is cheap, the amount of data has exploded. However, all that inform... (more)

Data fills all available space, and now that storage is cheap, the amount of data has exploded. However, all that information is useless without analysis and context. The R programming language is designed to make it easier to analyze and visualize massive amounts of data. For example, R provides the ability to multiply one block of variables by another-an assumption that provides inherent advantages over other languages. This course shows why R is ideal for high volumes of data, introduces more efficient ways to use the language, and explains how to avoid the problems and capitalize on the opportunities of big data. Learn how to determine if you have enough memory and processing power, produce visualizations of big data, optimize your R code, and use advanced techniques such as parallel processing to speed up your computations. Plus, discover how to integrate R with big-data solutions such as SQL databases and Apache Spark. (less)