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https://www.wiley.com/en-us/Python+for+Data+Science+For+Dummies,+2nd+Edition-p-9781119547662#downloads-section
John Paul Mueller
Luca Massaron
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Published:
February 27, 2019

Python for Data Science For Dummies

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Overview

Let Python do the heavy lifting for you as you analyze large datasets

Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner’s guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights. This new edition is updated for the latest version of Python and includes current, relevant data examples.

  • Get a firm background in the basics of Python coding for data analysis
  • Learn about data science careers you can pursue with Python coding skills
  • Integrate data analysis with multimedia and graphics
  • Manage and organize data with cloud-based relational databases

Python careers are on the rise. Grab this user-friendly Dummies guide and gain the programming skills you need to become a data pro.

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About The Author

John Paul Mueller is a freelance author and technical editor who has written 124 books on topics ranging like networking, home security, database management, and heads-down programming. Luca Massaron is a data scientist specialized in solving real-world problems with AI, machine learning, and algorithms. He is also a Kaggle Grandmaster and a Google Developer Expert.

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CHEAT SHEET

Python is an incredible programming language that you can use to perform data science tasks with a minimum of effort. The huge number of available libraries means that the low-level code you normally need to write is likely already available from some other source.All you need to focus on is getting the job done.
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It’s kind of amazing to think that IPython provides you with magic, but that’s precisely what you get with the magic functions. A magic function begins with either a % or %% sign. Those with a % sign work within the environment, and those with a %% sign work at the cell level.Note that the magic functions work best with Jupyter Notebook.
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Whenever you create a plot, you need to identify the sources of information using more than just the lines. Creating a plot that uses differing line types and data point symbols makes the plot much easier for other people to use. The following table lists the line plot styles. Color Marker Style Code Line Color Code Marker Style Code Line Style b blue .
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Starting with the idea of reverse-engineering how a brain processes signals, researchers based neural networks on biological analogies and their components, using brain terms such as neurons and axons as names. However, you'll discover that neural networks resemble nothing more than a sophisticated kind of linear regression because they are a summation of coefficients multiplied by numeric inputs.
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Random Forest is a classification and regression algorithm developed by Leo Breiman and Adele Cutler that uses a large number of decision tree models to provide precise predictions by reducing both the bias and variance of the estimates. When you aggregate many models together to produce a single prediction, the result is an ensemble of models.
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Python is an incredible programming language that you can use to perform data science tasks with a minimum of effort. The huge number of available libraries means that the low-level code you normally need to write is likely already available from some other source.All you need to focus on is getting the job done.
Read More
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Scikit-learn is a focal point for data science work with Python, so it pays to know which methods you need most. The following table provides a brief overview of the most important methods used for data analysis. Syntax Usage Description model_selection.cross_val_score Cross-validation phase Estimate the cross-validation score model_selection.
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Every developer on the planet makes mistakes. However, knowing about common mistakes will save you time and effort later. The following list tells you about the most common errors that developers experience when working with Python: Using the incorrect indentation: Many Python features rely on indentation. For example, when you create a new class, everything in that class is indented under the class declaration.
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The Jupyter Notebook Integrated Development Environment (IDE) is a part of the Anaconda suite of tools for Python programming and can do lots of things for you. The following information helps you understand some of the interesting things that Jupyter Notebook (often simply called Notebook) can help you do. Working with styles in Jupyter Notebook Here's one of the ways in which Jupyter Notebook excels over just about any other IDE that you’ll ever use: It helps you to create nice-looking output.
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Google Colaboratory, sometimes called Colaboratory for short, is a Google cloud-based service that replicates Jupyter Notebook in the cloud. You don’t have to install anything on your system to use it. In most respects, you use Colaboratory as you would a desktop installation of Jupyter Notebook. Google Colaboratory is primarily for those readers who use something other than a standard desktop setup to work through the examples.
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As with Jupyter Notebook, the notebook forms the basis of interactions with Google Colaboratory. In fact, Colab is built on notebooks. When you place the mouse on certain parts of the Welcome page, you see opportunities for interacting with the page by adding either code or text entries (which you can use for notes as needed).
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