Alex Galea [Galea - Beginning Data Science With Python and Jupyter: Use Powerful Industry-Standard Tools Within Jupyter and the Python Ecosystem to Unlock New, Actionable Insights From Your Data
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Getting started with data science doesnt have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.
Key Features- Get up and running with the Jupyter ecosystem and some example datasets
- Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests
- Discover how you can use web scraping to gather and parse your own bespoke datasets
Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. Youll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. Well finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context.
What you will learn- Get up and running with the Jupyter ecosystem and some example datasets
- Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests
- Plan a machine learning classification strategy and train classification, models
- Use validation curves and dimensionality reduction to tune and enhance your models
- Discover how you can use web scraping to gather and parse your own bespoke datasets
- Scrape tabular data from web pages and transform them into Pandas DataFrames
- Create interactive, web-friendly visualizations to clearly communicate your findings
This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. Youll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.
Table of Contents- Jupyter Fundamentals
- Data Cleaning and Advanced Machine Learning
- Web Scraping and Interactive Visualizations
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Alex Galea [Galea: author's other books
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Jupyter Notebooks are one of the most important tools for data scientists using Python. This is because they're an ideal environment for developing reproducible data analysis pipelines. Data can be loaded, transformed, and modeled all inside a single Notebook, where it's quick and easy to test out code and explore ideas along the way. Furthermore, all of this can be documented "inline" using formatted text, so you can make notes for yourself or even produce a structured report.
Other comparable platforms - for example, RStudio or Spyder - present the user with multiple windows, which promote arduous tasks such as copy and pasting code around and rerunning code that has already been executed. These tools also tend to involve Read Eval Prompt Loops ( REPLs ) where code is run in a terminal session that has saved memory. This type of development environment is bad for reproducibility and not ideal for development either. Jupyter Notebooks solve all these issues by giving the user a single window where code snippets are executed and outputs are displayed inline. This lets users develop code efficiently and allows them to look back at previous work for reference, or even to make alterations.
We'll start the lesson by explaining exactly what Jupyter Notebooks are and continue to discuss why they are so popular among data scientists. Then, we'll open a Notebook together and go through some exercises to learn how the platform is used. Finally, we'll dive into our first analysis and perform an exploratory analysis in Basic Functionality and Features .
In this lesson, you will:
- Learn what a Jupyter Notebook is and why it's useful for data analysis
- Use Jupyter Notebook features
- Study Python data science libraries
- Perform simple exploratory data analysis
All code from this book are available as lesson-specific IPython notebooks in the code bundle. All color plots from this book are also available in the code bundle.
In this section, we first demonstrate the usefulness of Jupyter Notebooks with examples and through discussion. Then, in order to cover the fundamentals of Jupyter Notebooks for beginners, we'll see the basic usage of them in terms of launching and interacting with the platform. For those who have used Jupyter Notebooks before, this will be mostly a review; however, you will certainly see new things in this topic as well.
Jupyter Notebooks are locally run web applications which contain live code, equations, figures, interactive apps, and Markdown text. The standard language is Python, and that's what we'll be using for this book; however, note that a variety of alternatives are supported. This includes the other dominant data science language, R:
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