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Back to Basics: Data Analysis
An introductory look at Data Science from Wiley Publishing. A B&N Exclusive Edition.
249 pages, Hardcover
Published January 1, 2019
2 people are currently reading
34 people want to read
About the author
Lillian Pierson
9 books19 followersLillian Pierson is a CEO & data leader that supports data professionals to evolve into world-class leaders & entrepreneurs. To date, she’s helped educate over 1.3 million data professionals on AI and data science.
Lillian has authored 6 data books with Wiley & Sons Publishers as well as 8 data courses with LinkedIn Learning. She’s supported a wide variety of organizations across the globe, from the United Nations and National Geographic, to Ericsson and Saudi Aramco, and everything in between.
She is a licensed Professional Engineer, in good standing. She’s been a technical consultant since 2007 and a data business mentor since 2018. She occasionally volunteers her expertise in global summits and forums on data privacy and ethics.
Lillian has authored 6 data books with Wiley & Sons Publishers as well as 8 data courses with LinkedIn Learning. She’s supported a wide variety of organizations across the globe, from the United Nations and National Geographic, to Ericsson and Saudi Aramco, and everything in between.
She is a licensed Professional Engineer, in good standing. She’s been a technical consultant since 2007 and a data business mentor since 2018. She occasionally volunteers her expertise in global summits and forums on data privacy and ethics.
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Displaying 1 of 1 review
December 28, 2019
If you are looking for a survey or overview of data analysis that is comprehensive and concise, this is the book for you. It is 100 miles wide and 1 millimeter deep, ambitious in breadth of scope, and exactly what I needed. In my career, I jumped from application development where transactional data was manipulated, to big data engineering. Data analysis was always a main source of use cases, but not my core focus. So I needed a survey to fill in the gaps, and this book did it admirably.
In my Goodreads progress updates for each, you can find a short summary of each of the eleven chapters:
1. Introducing Data Analysis
2. Data Analysis and Your Business
3. Prepare for Data Analysis
4. Explore Big Data Sources and Tools
5. Apply Data-Driven Insights to Business and Industry
6. Machine Learning
7. Math, Probability and Statistical Modeling
8. Subdivide Data with Clustering
9. Modeling with Instances
10. The Principles of Data Visualization Design
11. Useful Techniques that Complement Data Analysis
Although each chapter was valuable and filled a gap in terminology, technology, design, or technique, my favorite chapters were 1, 2, 5, 9, 10 and 11.
Well written, descriptive, insightful, concise and clear, this book provides a great introduction and starting point for every aspect of the breadth of data analysis. For many topics, additional resources or ways to further explore them are provided.
I rated this 4 out of 5 for two reasons: organization, and suggested resources to explore further. I think after chapters 1 and 2, leading with chapters 10, 11 and 5 as a way to motivate data analysis and root it in a catalog of examples and uses cases would have provided a stronger narrative and structure for the remaining, more technical chapters. For example, the various data clustering types and techniques in chapter 8 could have been described in the context of the appropriate examples or use cases in business or visualization. Secondly, ending with a compiled list of suggested resources to learn or explore each topic and technique further, sorted by chapter, would have made the book a better index and jumping off point for the broad and comprehensive overview it provides. That would make it a better future reference making it easier to return to, again and again, to explore a particular topic in more depth and when needed “just in time” for a data analysis problem at hand.
In my Goodreads progress updates for each, you can find a short summary of each of the eleven chapters:
1. Introducing Data Analysis
2. Data Analysis and Your Business
3. Prepare for Data Analysis
4. Explore Big Data Sources and Tools
5. Apply Data-Driven Insights to Business and Industry
6. Machine Learning
7. Math, Probability and Statistical Modeling
8. Subdivide Data with Clustering
9. Modeling with Instances
10. The Principles of Data Visualization Design
11. Useful Techniques that Complement Data Analysis
Although each chapter was valuable and filled a gap in terminology, technology, design, or technique, my favorite chapters were 1, 2, 5, 9, 10 and 11.
Well written, descriptive, insightful, concise and clear, this book provides a great introduction and starting point for every aspect of the breadth of data analysis. For many topics, additional resources or ways to further explore them are provided.
I rated this 4 out of 5 for two reasons: organization, and suggested resources to explore further. I think after chapters 1 and 2, leading with chapters 10, 11 and 5 as a way to motivate data analysis and root it in a catalog of examples and uses cases would have provided a stronger narrative and structure for the remaining, more technical chapters. For example, the various data clustering types and techniques in chapter 8 could have been described in the context of the appropriate examples or use cases in business or visualization. Secondly, ending with a compiled list of suggested resources to learn or explore each topic and technique further, sorted by chapter, would have made the book a better index and jumping off point for the broad and comprehensive overview it provides. That would make it a better future reference making it easier to return to, again and again, to explore a particular topic in more depth and when needed “just in time” for a data analysis problem at hand.
Displaying 1 of 1 review