SQL, Python, and R: A Guide In Under 4 Minutes | Sisense Beginner's Guide

[Music] sql in the most pure sense here is structured query language and it's ultimately used to interact with databases allows you to gather insights very quickly aggregate and join data and importantly easily visualize it so the key functionality here is that sql makes data analysis less manual easier to qa more robust it's used to communicate with relational databases really what we're doing here is putting all this data into a database or data warehouse and the database allows us to conduct ad hoc analysis put this data into bi to liberate the data and let our business users explore so we have three basic primary sql commands here that we're going to be using in almost every single query select the from and the where it is high level it is general purpose that means a couple things it means that it is very far abstracted away from having to worry about things like managing the memory or the processing power on your computer especially compared to other programming languages out there it's very easy to learn it's easy to even to look at and read and it's easy to develop on and then build out for whatever you're trying to accomplish you can take many of the things that you might be doing in sql or excel and apply those in ways that are really more effective with the programming language because it's operating line by line and gives you a lot of flexibility about how those calculations and data manipulations might be executed if you think about basic data manipulation and cleaning that is where python really starts to become a lot easier to work with the data focus on these three core areas data manipulation data visualization and then some basics about a couple libraries for machine learning and predictive analytics art is a well-known functional programming language in the data space however it often doesn't get as much attention as its sister python within this space r is very good at what it does r is open source making a preferable alternative to other math functional programming languages that require licenses like matlab the three most common use cases for are data transformations statistical analyses and data visualization data transformations are where we would add or modify values in a data table for further analysis statistical analyses specifically examine the statistical properties and significance of the data we have and data visualization allows us to present our data in an intuitive and digestible manner [Music] sql the blunt instrument is really really good at the heavy lifting then you get into python and r smaller more refined tools this is where you refine your data it's not for heavy lifting this is for nuance this is for stat analysis for feature engineering cool visualizations as well start with sql you do the heavy lifting you start modeling data using python do things like pivots which are really really hard in sql but really easy in python or a regression analysis just like one line of python and then create a visualization and you share that

Channel: Sisense

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Id: rtixRiDxbwc

Length: 3min 48sec (228 seconds)

Published: Tue Aug 18 2020