Building a Laptop Laboratory for Data Science - Computer Science Assignment Help

Assignment Task

Professional data scientists are not purely statisticians. Yes, applied statistical skills are important, but they must also possess practical software engineering skills and be able to build reliable and testable models that run rapidly, repeatably. They must understand data types in order to implement analytical algorithms, so that their employers and clients will gain a competitive advantage from the robust models they produce. Our aim here is not to treat one financial instrument at a time, but rather in mass. In essence, clusters and structures of instruments are needed so that comparisons can be made. Investing is a matter of decision-making, and the more stock candidates, the better the chances of success.

Regarding the subtitle, “Building a Laptop Laboratory for Data Science,” this book guides the reader on how to build a software simulation laboratory on which significantsized working modules can answer analytics inquiries. Laptops are fast becoming pervasive. When using the R language, any operating system will do. As evidence of how inexpensive powerful computing has become, the laptop on which all of the book’s simulations were run can be found for less than $200. After installing Crouton, a variant of Ubuntu Linux found for free online, and RStudio, an analyst can soon be downloading datasets and analyzing away with millions of rows from freely available financial datasets.

Our own laptop computer is called AL, short for Analytics Laboratory. (In some families, cars are given names: Betsy, Handsome, Chester, Venom, and Myles are typical. If cars can be named, why not name a device that is at one’s side most days?) AL’s hardware and operating system was purchased from a Groupon coupon for the nominal price of $139. Your version of AL could run on an Apple or Windows PC: any computer that can run RStudio and hold a large set of flat files and a small portion of a database will do.

For the flat files, one of the coded modules downloads and caches six million rows of prices for subsequent analysis. While many books have included code, in this book, when we include the code, the pieces build upon each other, providing an increasing level of sophistication as readers follow along with the option to try running the code on their own computers. The reader can use R on any type of computer operating system for which R is available, which covers most of them. When running the Analytics Library on a higher-performance Apple or Windows laptop with a large internal hard drive, one can literally load the “whole market” and perform queries at will.

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