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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

By : Meador
5 (12)
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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

5 (12)
By: Meador

Overview of this book

You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills. In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works. By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
Table of Contents (16 chapters)
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1
Part 1: The Data Science Landscape – Open Source to the Rescue
6
Part 2: Data Is the New Oil, Models Are the New Refineries
11
Part 3: Practical Examples and Applications

Working with date formats

Dates and times are often found in datasets and can present a few unique problems with data, becoming a huge thorn in a data scientist's side. There are many formats across the world, which differ across countries and systems. For example, the United States commonly uses the month/day/year format (mm/dd/yyyy), but in Europe, you are more likely to see day/month/year (dd/mm/yyyy).

Python has a built-in datetime object, but we'll make use of pandas' built-in datetime type as well. This will allow us to easily perform a few different operations on them, including grabbing just the month value, specifying a specific format, and other operations.

Time zones also come into play. There are many different rules across the world on what happens when. This is one reason UTC has become more common. UTC is a set standard that can be used no matter what your specific time zone is.

Specifying a date field in pandas

The easiest way to call out...

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