Book Image

Practical Data Analysis Using Jupyter Notebook

By : Marc Wintjen
Book Image

Practical Data Analysis Using Jupyter Notebook

By: Marc Wintjen

Overview of this book

Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.
Table of Contents (18 chapters)
1
Section 1: Data Analysis Essentials
7
Section 2: Solutions for Data Discovery
12
Section 3: Working with Unstructured Big Data
16
Works Cited

Summary

Let's look back at what we learned in this chapter and the skills obtained before we move forward. First, we covered a brief history of data analysis and the technological evolution of data by paying homage to the people and milestone events that made working with data possible using modern tools and techniques. We walked through an example of how to summarize these events using a data visual trend chart that showed how recent technology innovations have transformed the data industry.

We focused on why data has become important to make decisions from both a consumer and producer perspective by discussing the concepts for identifying and classifying data using structured, semi-structured, and unstructured examples and the 3Vsof big data: Volume, Velocity, and Variety.

We answered the question of what makes a good data analyst using the techniques of KYD, VOC, and ABA.

Then, we went deeper into understandingdata types by walking through the differences between numbers (integer and float) versus strings (text, time, dates, and coordinates). This includedbreaking down data classifications (continuous, categorical, and discrete) and understanding data attribute types.

We wrapped up this chapter by introducing the concept of data literacyand its importance to the consumers and producers of data by improving communication between them.

In our next chapter,we will get more hands-on by installing and setting up an environment for data analysis and so begin the journey of applying the concepts learned about data.