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Published:
January 9, 2020

Data Science Programming All-in-One For Dummies

Overview

Your logical, linear guide to the fundamentals of data science programming

Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models.

Data Science Programming All-In-One For Dummies is a compilation of the key data science,

machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time.

  • Get grounded: the ideal start for new data professionals
  • What lies ahead: learn about specific areas that data is transforming
  • Be meaningful: find out how to tell your data story
  • See clearly: pick up the art of visualization

Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!

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About The Author

John Mueller has produced 114 books and more than 600 articles on topics ranging from functional programming techniques to working with Amazon Web Services (AWS). Luca Massaron, a Google Developer Expert (GDE),??interprets big data and transforms it into smart data through simple and effective data mining and machine learning techniques.

Sample Chapters

data science programming all-in-one for dummies

CHEAT SHEET

Data science affects many different technologies in a profound manner. Our society runs on data today, so you can’t do many things that aren’t affected by it in some way. Even the timing of stoplights depends on data collected by the highway department. Your food shopping experience depends on data collected from Point of Sale (POS) terminals, surveys, farming data, and sources you can’t even begin to imagine.

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Articles from
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Before you begin using Bayes’ Theorem to perform practical tasks, knowing a little about its history is helpful. The reason this knowledge is so useful is because Bayes’ Theorem doesn’t seem to be able to do everything it purports to do when you first see it, which is why many statisticians rejected it outright.
Data science affects many different technologies in a profound manner. Our society runs on data today, so you can’t do many things that aren’t affected by it in some way. Even the timing of stoplights depends on data collected by the highway department. Your food shopping experience depends on data collected from Point of Sale (POS) terminals, surveys, farming data, and sources you can’t even begin to imagine.
A recommender system can suggest items or actions of interest to a user, after having learned the user’s preferences over time. The technology, which is based on data and machine learning techniques (both supervised and unsupervised), has appeared on the Internet for about two decades.Today you can find recommender systems almost everywhere, and they’re likely to play an even larger role in the future under the guise of personal assistants, such as Siri (developed by Apple), Amazon Alexa, Google Home, or some other artificial-intelligence–based digital assistant.
Data science programming begins with the language you choose. The most common languages for data science programming are Python and R. Every data form in Python and R begins with a scalar — a single item of a particular type. Precisely how you define a scalar depends on how you want to view objects within your code and the definitions of scalars for your language.
Because data is so valuable and users are sometimes adverse to giving it up, vendors constantly find new ways to collect data. One such method comes down to spying. Microsoft, for example, was recently accused (yet again) of spying on Windows 10 users even when the user doesn’t want their data collected.Lest you think that Microsoft is solely interested in your computing concerns, think again.
Pattern matching is extremely useful in data science. But, how exactly does pattern matching work? Patterns consist of a set of qualities, properties, or tendencies that form a characteristic or consistent arrangement — a repetitive model. Humans are good at seeing strong patterns everywhere and in everything.
Pattern matching in computers is as old as the computers themselves. In looking at various sources, you can find different starting points for pattern matching, such as editors. However, the fact is that you can’t really do much with a computer system without having some sort of pattern matching occur.For example, the mere act of stopping certain kinds of loops requires that a computer match a pattern between the existing state of a variable and the desired state.
Both linear and logistic regression see a lot of use in data science but are commonly used for different kinds of problems. You need to know and understand both types of regression to perform a full range of data science tasks.Of the two, logistic regression is harder to understand in many respects because it necessarily uses a more complex equation model.
A deep learning framework is an abstraction that provides generic functionality, which your application code modifies to serve its own purposes. Unlike a library that runs within your application, when you’re using a framework, your application runs within it.You can’t modify basic deep learning framework functionality, which means that you have a stable environment in which to work, but most frameworks offer some level of extensibility.
Data science seems like a terribly precise field, but the outcomes are only as reliable as your data. The word reliable seems so easy to define when it comes to data sources, yet so hard to implement. A data source is reliable when the results it produces are both expected and consistent. A reliable data source produces mundane data that contains no surprises; no one is shocked in the least by the outcome.
The data you use for data science programming initiatives comes from a number of sources. The most common data source is from information entered by humans at some point. Even when a system collects shopping-site data automatically, humans initially enter the information. ©Shutterstock/ra2 studioA human clicks various items, adds them to a shopping cart, specifies characteristics (such as size) and quantity, and then checks out.
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