Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications.
Key Features
Get up and running with the Jupyter ecosystem and some example datasets
Learn about key machine learning concepts such as SVM, KNN classifiers, and Random Forests
Discover how you can use web scraping to gather and parse your own bespoke datasets
Book DescriptionGetting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.
What you will learn
Get up and running with the Jupyter ecosystem
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 forApplied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a 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 such as Pandas, Matplotlib, and Pandas providing you a useful head start.
Key Features
Get up and running with the Jupyter ecosystem and some example datasets
Learn about key machine learning concepts such as SVM, KNN classifiers, and Random Forests
Discover how you can use web scraping to gather and parse your own bespoke datasets
Book DescriptionGetting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.
What you will learn
Get up and running with the Jupyter ecosystem
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 forApplied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a 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 such as Pandas, Matplotlib, and Pandas providing you a useful head start.
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