Applied Data Science with Python and Jupyter

Become the master player of data exploration by creating reproducible data processing pipelines visualizations and prediction models for your applications.Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM KNN classifiers and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook 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 learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive web-friendly visualizations to clearly communicate your findingsWho 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. 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.