Tutor profile: Shourya M.
Subject: SQL Programming
What do you know about SQL performance?
"SQL-Tuning is black magic like alchemy: it consists of obscure rules, understood only by a handful of insiders.” That is a myth. SQL databases use well-known algorithms to deliver predictable performance. It is, however, easy to write SQL queries that cannot use the most efficient algorithm and thus deliver unexpected performance. SQL is one of the most elemental language which is easy to learn, and building block for data analytics and operations. Many time like: Wrapping the table column in a function renders the index useless for simple date fetch query, by using date function for optimization. Also, scenarios the statement which can be executed as an indexed Top-N query. It performs just like a B-Tree traversal only so it's very efficient. The trick is that the index supports the where as well as the order by clause. The database uses the index to find the last entry that matches the where clause and takes it as result. There is no need to actually perform a sort for the order by.
Why use SAS?
SAS in front runner in analytics domain for all enterprise. SAS has been in industry for quite some time and nearly/ roughly 70% enterprise uses it. If you want to work with big industry player in Banking, Insurance, Analytics, Consulting, then learning SAS gives you a head start. The solid and ever reliable support by SAS team, gives enterprise assurance for continuous use of it. Simply put out SAS is a commercial software, which would add value to your skillset.
Subject: R Programming
Why learn and use R programming, when we have other tools/ languages like SAS, SQL etc?
R vs SAS (or any other tool) for argument sake, has been a hot debate in analytics industry much like Apple vs Samsung, Linux vs Windows vs iOS. R is the Open source counterpart of SAS, which has traditionally been used in academics and research. Because of its open source nature, latest techniques get released quickly. R gives us much more powerful option when it comes to actually building a predictive model and not just build it, but further optimizing the algorithm. There is a lot of documentation available over the internet and it is a very cost-effective option. R has indefinite (well, actually you can count it but it is huge) library packages for every known operation, manipulation, machine learning algo, metric solution, visualization, and playing with data. There has been major shift towards use of R recently, with major players now adapting R as their prime tool that can build over their database platform. Cost benefit and easy to use, gives it more levy.
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