A beginner’s Guide to BigQuery Sandbox and exploring public datasets.

A beginner's guide to BigQuery Sandbox

          As you might realize by now, writing SQL queries is one of the essential skills any inspiring data analyst needs to master. After all, larger datasets are typically stored in relational databases and Structured Query Language is the language that helps us communicate with such databases. Sure, NoSQL is gaining prominence amid the growing popularity of nontraditional databases, but we need to learn to crawl before we start walking. Merely 10 years ago, you would need to download and install a RDBMS software package (be it MySQL, PostgreSQL, or SQLite), load a sample database and do a hundred pushups before you could write your very first SQL query. Luckily technology sprung ahead and we now have a plethora of web-based SQL editor options from SQL Lite Online to SQL Fiddle that eliminate the software setup step, but might still require us to load sample data. What if you wanted to access real-world big data sets from the comfort of your browser without having to download any software, no hassle, no trial, no credit card required? Well, you’re in luck, what follows is the beginner’s guide to Google BigQuery’s Sandbox. An active Google account is your cost of admission. BONUS: Machine Learning models are powered by nothing else but SQL are also included.
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Highlights of Gary Cokins Business Analytics seminar

Business Analytics

         Last month I attened Business Analytics seminar by “an internationally recognized expert, speaker, and author in performance improvement systems and cost management” : Gary Cokins.

         My slideshare presentation in the bottom of this post summarizes Gary’s informative and thought-provoiking presentation. In fact, some statements in this post were provoked by this seminar. Gary started by quoting Jeanne X. Harris :“40% of important decisions are not based on facts but rather on intuition, experience, and anecdotal evidence.” Arguably, we could improve the world by basing our decisions on facts, and making better decisions in the process. In addition, better decisions result in better actions, saving us time, effort, money, and other resources. The “actions” part is really important in terms of differentiating Business Analytics from Business Intelligence.

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