Practical Data Analysis Using Jupyter Notebook
Learn how to speak the language of data by extracting useful and actionable insights using Python
Marc Wintjen
- 322 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Practical Data Analysis Using Jupyter Notebook
Learn how to speak the language of data by extracting useful and actionable insights using Python
Marc Wintjen
About This Book
Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook
Key Features
- Find out how to use Python code to extract insights from data using real-world examples
- Work with structured data and free text sources to answer questions and add value using data
- Perform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing data
Book Description
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.
What you will learn
- Understand the importance of data literacy and how to communicate effectively using data
- Find out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysis
- Wrangle data and create DataFrames using pandas
- Produce charts and data visualizations using time-series datasets
- Discover relationships and how to join data together using SQL
- Use NLP techniques to work with unstructured data to create sentiment analysis models
- Discover patterns in real-world datasets that provide accurate insights
Who this book is for
This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.
Frequently asked questions
Information
- Chapter 1, Fundamentals of Data Analysis
- Chapter 2, Overview of Python and Installing Jupyter Notebook
- Chapter 3, Getting Started with NumPy
- Chapter 4, Creating Your First pandas DataFrame
- Chapter 5, Gathering and Loading Data in Python
- The evolution of data analysis and why it is important
- What makes a good data analyst?
- Understanding data types and why they are important
- Data classifications and data attributes explained
- Understanding data literacy
The evolution of data analysis and why it is important
extensive datasets
âprimarily in the characteristics of volume, velocity, and/or variabilityâ
that require a scalable architecture for efficient storage, manipulation, and analysis.
- Volume is based on the quantity of data that is stored in any format such as image files, movies, and database transactions, which are measured in gigabytes, terabytes, or even zettabytes. To give context, you can store hundreds of thousands of songs or pictures on one terabyte of storage space. Even more amazing than the figures is how much it costs you. Google Drive, for example, offers up to 5 TB (terabytes) of storage for free according to their support site.
- Velocity is the speed at which data is generated. This process covers how data is both produced and consumed. For example, batch processing is how data feeds are sent between systems where blocks of records or bundles of files are sent and received. Modern velocity approaches are real time, streams of data where the data flow is in a constant state of movement.
- Variety is all of the different formats that data can be stored in, including text, image, database tables, and files. This variety has created both challenges and opportunities for analysis because of the different technologies and techniques required to work with the data.