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Beginning Data Science with Python and Jupyter: Use powerful tools to unlock actionable insights from data: Use powerful industry-standard tools to ... actionable insight from your existing data - Softcover
Synopsis
Getting started with data science doesn't 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
Book Description
Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll 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. We'll 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
- Identify potential areas of investigation and perform exploratory data analysis
- Plan a machine learning classification strategy and train classification models
- Use validation curves and dimensionality reduction to tune and enhance your models
- Scrape tabular data from web pages and transform it into Pandas DataFrames
- Create interactive, web-friendly visualizations to clearly communicate your findings
Who This Book Is For
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. You'll 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
"synopsis" may belong to another edition of this title.
About the Author
Alex Galea has been professionally practicing data analytics since graduating with a Master's degree in Physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks.
"About this title" may belong to another edition of this title.
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Beginning Data Science with Python and Jupyter : Use Powerful Industry-Standard Tools within Jupyter and the Python ecosystem to Unlock New, Actionable Insight from Your Existing Data
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Beginning Data Science with Python and Jupyter
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Digital. Condition: New. Getting started with data science doesn't 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.About This Book. 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 Who This Book Is ForThis 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. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.What You Will Learn. Identify potential areas of investigation and perform exploratory data analysis. Plan a machine learning classification strategy and train classification models. Use validation curves and dimensionality reduction to tune and enhance your models. Scrape tabular data from web pages and transform it into Pandas DataFrames. Create interactive, web-friendly visualizations to clearly communicate your findings In DetailGet to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll 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. We'll 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.Style and approachThis book covers every aspect of the standard data-workflow process within a day, along with theory, practical hands-on coding, and relatable illustrations. Seller Inventory # LU-9781789532029
Quantity: Over 20 available