We are going to work through the book 'Data Smart: Using Data Science to Transform Information into Insight' on a weekly basis, chapter by chapter.
We will discuss and work through the spread sheet based exercises in the book, and converting those exercises to other programming language formats. The first week will cover chapter 1, a review of spread sheet techniques, and our own additional material on converting spreadsheet data structures and formulas to other languages. The free LibreOffice spreadsheet can be used with the book. The author is known as an analyst who reliably and pragmatically provides critical value to his customers. Here is an example of his rare but spot on thinking:
This is an ideal book to learn, review or practice the foundations of Data Science. It uses only spread sheets and is therefore accessible to a wide variety of participants. This book fills the niche between business oriented survey books, and implementation or algorithm focused computer books. It covers the essence of each technique, a working business example of what to apply it to and how to apply it, with very little implementation or algorithm details. This is currently a significant gap area, with people tending to know a lot about either a knowledge domain like business or medicine, or an implementation or algorithm domain like Hadoop or statistics, but often less on how to bridge these two areas.
There are also some very good reasons to practice with spread sheets in addition to simplicity. The majority of corporate data of all types is stored outside of centralized databases in a vast and diverse array of Excel, Access, and text files. The day to day data analysis tool of choice for the vast majority of users is spread sheets. That makes spread sheets a ground zero connection point between users and 'Data Scientists', for both human and computer communication. Spread sheets can also be considered a 'data flow' programming language, and have functional programming like aspects, making them amenable to conversion to languages like Scala, Scheme and Haskell.
If you are already familiar with the topics, or are studying Data Science, Machine learning or Big Data with a different book or language instead of spreadsheets, these meetings will still be a valuable opportunity for practice with and discussion of pragmatic Data Science problems.