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Practical Data Analysis Using Jupyter Notebook
Practical Data Analysis Using Jupyter Notebook

Practical Data Analysis Using Jupyter Notebook: Learn how to speak the language of data by extracting useful and actionable insights using Python

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Profile Icon Marc Wintjen
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eBook Jun 2020 322 pages 1st Edition
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Arrow left icon
Profile Icon Marc Wintjen
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eBook Jun 2020 322 pages 1st Edition
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Practical Data Analysis Using Jupyter Notebook

Fundamentals of Data Analysis

Welcome and thank you for reading my book. I'm excited to share my passion for data and I hope to provide the resources and insights to fast-track your journey into data analysis. My goal is to educate, mentor, and coach you throughout this book on the techniques used to become a top-notch data analyst. During this process, you will get hands-on experience using the latest open source technologies available such as Jupyter Notebook and Python. We will stay within that technology ecosystem throughout this book to avoid confusion. However, you can be confident the concepts and skills learned are transferable across open source and vendor solutions with a focus on all things data.

In this chapter, we will cover the following:

  • 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

To begin, we should define what data is. You will find varying definitions but I would define data as the digital persistence of facts, knowledge, and information consolidated for reference or analysis. The focus of my definition should be the word persistence because digital facts remain even after the computers used to create them are powered down and they are retrievable for future use. Rather than focus on the formal definition, let's discuss the world of data and how it impacts our daily lives. Whether you are reading a review to decide which product to buy or viewing the price of a stock, consuming information has become significantly easier to allow you to make informed data-driven decisions.

Data has been entangled into products and services across every industry from farming to smartphones. For example, America's Grow-a-Row, a New Jersey farm to food bank charity, donated over 1.5 million pounds of fresh produce to feed people in need throughout the region each year, according to their annual report. America's Grow-a-Row has thousands of volunteers and uses data to maximize production yields during the harvest season.

As the demand for being a consumer of data has increased, so has the supply side, which is characterized as the producer of data. Producing data has increased in scale as the technology innovations have evolved. I'll discuss this in more detail shortly, but this large scale consumption and production can be summarized as big data. A National Institute of Standards and Technology report defined big data as consisting of extensive datasets—primarily in the characteristics of volume, velocity, and/or variability—that require a scalable architecture for efficient storage, manipulation, and analysis.

This explosion of big data is characterized by the 3Vs, which are Volume, Velocity, and Variety,and has become a widely accepted concept among data professionals:

  • 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.

Understanding the 3Vs is important for data analysis because you must become good at being both a consumer and producer of data. The simple questions of how your data is stored, when this file was produced, where the database table is located, and in what format I shouldstore the output of my analysis of the data can all be addressed by understanding the 3Vs.

There is some debate—for which I disagree—that the 3Vs should increase to include Value, Visualization, and Veracity. No worries, we will cover these concepts throughout this book.

This leads us to a formal definition of data analysis which is defined as a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusion, and supporting decision-making, as stated in Review of business intelligence through data analysis.

Xia, B. S., & Gong, P. (2015). Review of business intelligence through data analysis. Benchmarking, 21(2), 300-311. doi:10.1108/BIJ-08-2012-0050

What I like about this definition is the focus on solving problems using data without the focus on which technologies are used. To make this possible there have been some significant technological milestones, the introduction of new concepts, and people who have broken down the barriers.

To showcase the evolution of data analysis, I compiled a few tables of key events from the years of 1945 until 2018 that I feel are the most influential. The following table is comprised of innovators such as Dr. E.F. Codd, who created the concept of a database to the launch of the iPhone device that spawned the mobile analytics industry.

The following diagram was collected from multiple sources and centralized in one place as a table of columns and rows and then visualized using this dendrogram chart. I posted the CSV file in the GitHub repository for reference: https://github.com/PacktPublishing/python-data-analysis-beginners-guide. Organizing the information and conforming the data in one place made the data visualization easier to produce and enables further analysis:

That process of collecting, formatting, and storing data in this readable format demonstrates the first step of becoming a producer of data. To make this information easier to consume, I summarize these events by decades in the following table:

Decade

Count of Milestones

1940s

2

1950s

2

1960s

1

1970s

2

1980s

5

1990s

9

2000s

14

2010s

7

From the preceding summary table, you can see that the majority of these milestone events occurred in the 1990s and 2000s. What is insightful about this analysis is that recent innovations have removed the barriers of entry for individuals to work with data. Before the 1990s, the high purchasing costs of hardware and software restricted the field of data analysis to a relatively limited number of careers. Also, the costs associated with access to the underlying data for analysis were great. It typically required higher education and specialized careers in software programming or an actuary.

A visual way to look at this same data would be a trend bar chart, as shown in the following diagram. In this example, the height of the bars represents the same information as in the preceding table and the Count of Milestone events is on the left or the y axis. What is nice about this visual representation of the data is that it is a faster way for the consumer to see the upward pattern of where most events occur without scanning through the results found in the preceding diagram or table:

The evolution of data analysis is important to understand because now you know some of the pioneers who opened doors for opportunities and careers working with data, along with key technology breakthroughs, significantly reducing the time to make decisions regarding data both as consumers and producers.

What makes a good data analyst?

I will now break down the contributing factors that make up a good data analyst. From my experience, a good data analyst must be eager to learn and continue to ask questions throughout the process of working with data. The focus of those questions will vary based on the audience who are consuming the results. To be an expert in the field of data analysis, excellent communication skills are required so you can understand how to translate raw data into insights that can impact change in a positive way. To make it easier to remember, use the following acronyms to help to improve your data analyst skills.

Know Your Data (KYD)

Knowing your data is all about understanding the source technology that was used to create the data along with the business requirements and rules used to store it. Do research ahead of time to understand what the business is all about and how the data is used. For example, if you are working with a sales team, learn what drives their team's success. Do they have daily, monthly, or quarterly sales quotas? Do they do reporting for month-end/quarter-end that goes to senior management and has to be accurate because it has financial impacts on the company? Learning more about the source data by asking questions about how it will be consumed will help focus your analysis when you have to deliver results.


KYD is also about data lineage, which is understanding how the data was originally sourced including the technologies used along with the transformations that occurred before, during, and afterward. Refer back to the 3Vs so you can effectively communicate the responses from common questions about the data such as where this data is sourced from or who is responsible for maintaining the data source.

Voice of the Customer (VOC)

The concept of VOC is nothing new and has been taught at universities for years as a well-known concept applied in sales, marketing, and many other business operations. VOC is the concept of understanding customer needs by learning from or listening to their needs before, during, and after they use a company's product or service. The relevance of this concept remains important today and should be applied to every data project that you participate in. This process is where you should interview the consumers of the data analysis results before even looking at the data. If you are working with business users, listen to what their needs are by writing down the specific points on what business questions are they trying to answer.

Schedule a working session with them where you can engage in a dialog. Make sure you focus on their current pain points such as the time to curate all of the data used to make decisions. Does it take three days to complete the process every month? If you can deliver an automated data product or a dashboard that can reduce that time down to a few mouse clicks, your data analysis skills will make you look like a hero to your business users.

During a tech talk at a local university, I was asked the difference between KYD and VOC. I explained that both are important and focused on communicating and learning more about the subject area or business. The key differences are prepared versus present. KYD is all about doing your homework ahead of time to be prepared before talking to experts. VOC is all about listening to the needs of your business or consumers regarding the data.

Always Be Agile (ABA)

The agile methodology has become commonplace in the industry for application, web, and mobile development Software Development Life Cycle (SDLC). One of the reasons that makes the agile project management process successful is that it creates an interactive communication line between the business and technical teams to iteratively deliver business value through the use of data and usable features.

The agile process involves creating stories with a common theme where a development team completes tasks in 2-3 week sprints. In that process, it is important to understand the what and the why for each story including the business value/the problem you are trying to solve.

The agile approach has ceremonies where the developers and business sponsors come together to capture requirements and then deliver incremental value. That improvement in value could be anything from a new dataset available for access to a new feature added to an app.

See the following diagram for a nice visual representation of these concepts. Notice how these concepts are not linear and should require multiple iterations, which help to improve the communication between all people involved in the data analysis before, during, and after delivery of results:

Finally, I believe the most important trait of a good data analyst is a passion for working with data. If your passion can be fueled by continuously learning about all things data, it becomes a lifelong and fulfilling journey.

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Key benefits

  • 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

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.

Who is this book 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.

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

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Table of Contents

17 Chapters
Section 1: Data Analysis Essentials Chevron down icon Chevron up icon
Fundamentals of Data Analysis Chevron down icon Chevron up icon
Overview of Python and Installing Jupyter Notebook Chevron down icon Chevron up icon
Getting Started with NumPy Chevron down icon Chevron up icon
Creating Your First pandas DataFrame Chevron down icon Chevron up icon
Gathering and Loading Data in Python Chevron down icon Chevron up icon
Section 2: Solutions for Data Discovery Chevron down icon Chevron up icon
Visualizing and Working with Time Series Data Chevron down icon Chevron up icon
Exploring, Cleaning, Refining, and Blending Datasets Chevron down icon Chevron up icon
Understanding Joins, Relationships, and Aggregates Chevron down icon Chevron up icon
Plotting, Visualization, and Storytelling Chevron down icon Chevron up icon
Section 3: Working with Unstructured Big Data Chevron down icon Chevron up icon
Exploring Text Data and Unstructured Data Chevron down icon Chevron up icon
Practical Sentiment Analysis Chevron down icon Chevron up icon
Bringing It All Together Chevron down icon Chevron up icon
Works Cited Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Being a novice, I found this book very well written, informative and above all, helpful. The author knows how to explain concepts in easy to understand language - I didn’t have to google every other word.
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Do NOT listen to the bad reviews, they must have something against the author of the book. This book was VERY well-written, easy to understand, and useful. Highly recommend!!
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Step by step guides are essential!
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Für mich genau der richtige Einstieg in die Data Analysis Welt und Jupyter Notebook. Etwas Python-Kenntnisse sind allerdings sehr von Vorteil.
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