Book Image

Python: Real-World Data Science

By : Fabrizio Romano, Dusty Phillips, Phuong Vo.T.H, Martin Czygan, Robert Layton, Sebastian Raschka
Book Image

Python: Real-World Data Science

By: Fabrizio Romano, Dusty Phillips, Phuong Vo.T.H, Martin Czygan, Robert Layton, Sebastian Raschka

Overview of this book

The Python: Real-World Data Science course will take you on a journey to become an efficient data science practitioner by thoroughly understanding the key concepts of Python. This learning path is divided into four modules and each module are a mini course in their own right, and as you complete each one, you’ll have gained key skills and be ready for the material in the next module. The course begins with getting your Python fundamentals nailed down. After getting familiar with Python core concepts, it’s time that you dive into the field of data science. In the second module, you'll learn how to perform data analysis using Python in a practical and example-driven way. The third module will teach you how to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis to more complex data types including text, images, and graphs. Machine learning and predictive analytics have become the most important approaches to uncover data gold mines. In the final module, we'll discuss the necessary details regarding machine learning concepts, offering intuitive yet informative explanations on how machine learning algorithms work, how to use them, and most importantly, how to avoid the common pitfalls.
Table of Contents (12 chapters)
Free Chapter
Table of Contents
Python: Real-World Data Science
Meet Your Course Guide
What's so cool about Data Science?
Course Structure
Course Journey
The Course Roadmap and Timeline

Chapter 7. Data Analysis Application Examples

In this chapter, we want to get you acquainted with typical data preparation tasks and analysis techniques, because being fluent in preparing, grouping, and reshaping data is an important building block for successful data analysis.

While preparing data seems like a mundane task – and often it is – it is a step we cannot skip, although we can strive to simplify it by using tools such as pandas.

Why is preparation necessary at all? Because most useful data will come from the real world and will have deficiencies, contain errors or will be fragmentary.

There are more reasons why data preparation is useful: it gets you in close contact with the raw material. Knowing your input helps you to spot potential errors early and build confidence in your results.

Here are a few data preparation scenarios:

  • A client hands you three files, each containing time series data about a single geological phenomenon, but the observed data is recorded...