Alex Galea - Beginning Data Science with Python and Jupyter: Use powerful tools to unlock actionable insights from 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- 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
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 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.
Elie Kawerk likes to solve problems using the analytical skills he has accumulated over the years. He uses the data science process, including statistical methods and machine learning, to extract insights from data and get value out of it.
His formal training is in computational physics. He used to simulate atomic and molecular physics phenomena with the help of supercomputers using the good old FORTRAN language; this involved a lot of linear algebra and quantum physics equations.
You can find out more about Elie on his LinkedIn profile (https://www.linkedin.com/in/elie-kawerk-data-scientist/).
If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.
Data science is becoming increasingly popular as industries continue to value its importance. Recent advancements in open source sofware have made this discipline accessible to a wide range of people. In this book, we show how Jupyter Notebooks can be used with Python for various data science applications. Aside from being an ideal "virtual playground" for data exploration, Jupyter Notebooks are equally suitable for creating reproducible data processing pipelines, visualizations, and prediction models. By using Python with Jupyter Notebooks, many challenges presented by data science become simple to conceptualize and implement. This is achieved by leveraging Python libraries, which offer abstractions to the more complicated underlying algorithms. The result is that data science becomes very approachable for beginners. Furthermore, the Python ecosystem is very strong and is growing with each passing year. As such, students who wish to continue learning about the topics covered in this book will fnd excellent resources to do so.
By the end of this book, you will be equipped to analyse data using Python and use Jupyter notebooks effectively.
, Jupyter Fundamentals, covers the fundamentals of data analysis in Jupyter. We will start with usage instructions and features of Jupyter such as magic functions and tab completion. We will then transition to data science specific material. We will run an exploratory analysis in a live Jupyter Notebook. We will use visual assists such as scatter plots, histograms, and violin plots to deepen our understanding of the data. We will also perform simple predictive modeling.
, Data Cleaning and Advanced Machine Learning , shows how predictive models can be trained in Jupyter Notebooks. We will talk about how to plan a machine learning strategy. This lesson also explains the machine learning terminology such as supervised learning, unsupervised learning, classification, and regression. We will discuss methods for preprocessing data using scikit-learn and pandas.
, Web Scraping and Interactive Visualizations , explains how to scrap web page tables and then use interactive visualizations to study the data. We will start by looking at how HTTP requests work, focusing on GET requests and their response status codes. Then, we will go into the Jupyter Notebook and make HTTP requests with Python using the Requests library. We will see how Jupyter can be used to render HTML in the notebook, along with actual web pages that can be interacted with. After making requests, we will see how Beautiful Soup can be used to parse text from the HTML, and used this library to scrape tabular data.
This book will require the following minimum hardware requirements:
- Processor: Intel i5 (or equivalent)
- Memory: 8GB RAM
- Hard disk: 10 GB
- An internet connection
Throughout this book, we will be using Python and Jupyter Notebook to run our code. Additionally, Anaconda environment is needed to run Python and Jupyter notebook. Please ensure you have the following installed on your machine:
- Python 3.5+
- Anaconda 4.3+
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