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

The importance of data lineage

Data lineage is the ability to trace back the source of a dataset to how it was created. It is a fun topic for me because it typically requires investigating the history of how systems generate data, identifying how it was processed, and working with the people who produce and consume the data. This process helps to improve your data literacy, which is the ability to read, write, analyze, and argue with data because you can learn how the data impacts the organization. Is the data critical to business functions such as generating sales or was it created for compliance purposes? These types of questions should be answered by learning more about the lineage of the data.

From experience, this process of tracing data lineage involves working sessions directly with the people who are responsible for the data and uncovering any documentation like an ERD demonstrated in the From SQL to pandas DataFrames section or help guides. In many cases, the...