You're reading from Learn Python by Building Data Science Applications

Product typeBook

Published inAug 2019

Reading LevelIntermediate

PublisherPackt

ISBN-139781789535365

Edition1st Edition

Languages

Python

Tools

Pygame

Concepts

Application Development

Authors (2):

Philipp Kats

David Katz

View More author details

Section 2: Hands-On with Data

This section introduces us to the world of scientific (or data-oriented) Python, including its own IDE—Jupyter—and a suite of packages that assist in working with data. We will perform exploratory data analysis, draw some charts, and train a number of models. All of this will be implemented through our three projects, utilizing the data we collected in the first section.

This section comprises the following chapters:

Chapter 10, Python for Data Applications
Chapter 11, Data Cleaning and Manipulation
Chapter 12, Data Exploration and Visualization
Chapter 13, Training a Machine Learning Model
Chapter 14, Improving Your Model – Pipelines and Experiments

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Authors (2)

Philipp Kats

Philipp Kats is a researcher at the Urban Complexity Lab, NYU CUSP, a research fellow at Kazan Federal University, and a data scientist at StreetEasy, with many years of experience in software development. His interests include data analysis, urban studies, data journalism, and visualization. Having a bachelor's degree in architectural design and a having followed the rocky path (at first) of being a self-taught developer, Philipp knows the pain points of learning programming and is eager to share his experience.
Read more about Philipp Kats

David Katz

David Katz is a researcher and holds a Ph.D. in mathematics. As a mathematician at heart, he sees code as a tool to express his questions. David believes that code literacy is essential as it applies to most disciplines and professions. David is passionate about sharing his knowledge and has 6 years of experience teaching college and high school students.
Read more about David Katz

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