Book Image

Beginning Data Science with Python and Jupyter

By : Chris DallaVilla
Book Image

Beginning Data Science with Python and Jupyter

By: Chris DallaVilla

Overview of this book

Getting started with data science doesn’t have to be an uphill battle. This step-by-step video course is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. 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 start with understanding the basics of Jupyter and its standard features. You'll be analyzing an example of a data analytics report. After analyzing a data analytics report, next step is to implement multiple classification algorithms. We’ll then show 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. Finish up by learning to visualize these data interactively. The code bundle for this course is available at https://github.com/TrainingByPackt/Beginning-Data-Science-with-Python-and-Jupyter-eLearning
Table of Contents (3 chapters)
Chapter 1
Jupyter Fundamentals
Content Locked
Section 5
Python Libraries
Having now seen all the basics of Jupyter Notebooks, and even some more advanced features, we'll shift our attention to the Python libraries we'll be using in this course. Libraries, in general, extend the default set of Python functions. Examples of commonly used standard libraries are datetime, time, and os. This video covers: - Python Libraries - External Data Science Libraries - Demo on Importing The External Libraries And Set Up The Plotting Environment