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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

Walking through the data science workflow

While there will be deviations in the path that you take in any particular problem, you can be sure that you'll be following the same rough outline for most of them. In the following diagram, you can see the flow we will use in this chapter, and it's the same that you will use for most problems that you come across:

Figure 9.1 – Data science workflow

Figure 9.1 consists of the following steps in the data science flow:

  1. Understanding the problem space.
  2. Data exploration/preprocessing/manipulation. We combine these into one, but there are distinct parts of each that we will dive into.
  3. Feature selection/extraction.
  4. Predictive modeling.
  5. Project outcomes and conclusion.

These steps will become very familiar to you in this and the following chapters. Let's now look at the first step in this journey.

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