Data is all around us, and it's kind of crazy to imagine what it must have been like to store it all manually in filing cabinets before the digital age came in to make it all easier for us. Luckily, now we have databases (YAY!), but wait, how do we pull the information we need or want from these databases with as little fuss as possible? Drumroll, please… Enter, SQL! But before we dive into SQL and why it's handy...
Data analytics is growing more and more in popularity as more businesses move to gathering and storing all their data online, so it's a pretty big deal, especially in the world of business, or governance etc. As you are most probably aware, data is being collected all the time, yet in its raw form, this data will leave you scratching your head because it won't make sense. This is where data analytics comes in: it allows companies to pull out, edit and add specific data they are searching for. This helps such companies or organisations to draw insights and make the most informed decisions for their next strategic move.
Data analysts are much sought after because they are able to organise and categorise this data to make it interpretable and therefore usable, and they speak SQL. Btw in case you were wondering, data analysis and data science are two different fields. Data science is more multidisciplinary as it combines statistics, scientific methods, artificial intelligence (AI) and more to extract value from data. Plus they use a range of tools like smartphones, sensors, websites and more to interpret data.
Generally speaking, data analysis can be used infinitely depending on what information is being looked for, yet more specifically, it's used to make better, faster and business decisions to reduce overall business costs and develop new and innovative products and services. For example, it could predict future sales or purchasing behaviours, security purposes as it helps to protect against fraud, analyse the effectiveness of marketing campaigns or increase supply chain efficiency.
SQL stands for Structured Query Language and is pronounced as Sequel. This is the language used in data analysis to communicate with data!
Three key things to know about SQL
RDBMS stands for Relational Database Management System. This drastically helps to organise data so that it can easily be extracted and analysed. Let us break it down even further so you really know what it means. It's a system that manages data organised in tables and the relations between them. Let's break it down again. When we talk about systems we mean RDBMS can store many different kinds of data for many different kinds of applications in one place. For example, if we are talking about a sale, there is a table or dataset for the sale information, one for customer information and the other for the sales item or inventory; therefore, there is a relation between these data sets.
Key benefits of RDBMS:
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Because it’s an effective and reliable way to store information as the basis for online transaction processing systems, and these are systems that keep businesses running. RDBMS are applied in corporate administration and accounting, banking and insurance systems, government data, point of service (POS) and E-commerce systems, and the list goes on. And of course, all the data gathered in these systems are used in the analytics environment to generate insights, but you first need to access such data. And how do we do that?...Through SQL, which speaks to RDBMS!
A SQL query allows you to query (investigate) a specific piece of information.
Tables are the database objects that hold the data in the relational databases. SQL can be applied to programs like Python or even a simple excel spreadsheet. In SQL lingo, a Column is a field, and a Row is a record, and finally, there is an entity that is the smallest unit that contains a meaningful set of data. An entity is also known as the dataset object. With SQL, the best way to learn is by giving it a go! Yet, here are some quick terms to get you familiar with the lingo.
If you are looking to query something, the term "select" always comes first and is eventually followed by "from". The "where" term allows you to filter out rows that you do now want to have in your search results. Here is a quick example of what it could look like if we were to be using a student database:
Select: “*’” or the specific field eg. (student_name_dateofbirth)
From: (table name) e.g Student
Where: date of birth = (select max ( date of birth) from student)
By the way, aside from the equal "=" sign you can use does not equal to "<>" or is greater than "<"; the list goes on, and you change this based on what you are looking for. When you want to merge two or more tables or datasets, you can use the "join" statement that puts two different tables together side by side based on a shared value; the term joins usually appears after from but before the where statement. For example; From table_1 join table_2
Finally, we will mention one more! The “union” term matches columns from top to bottom. This union statement usually sits between two select statements. The union can only occur on columns with the exact same column names and columns that have the same data type.
To continue learning more about the basics of Data Analytics and SQL, watch our webinar below:
All in all, getting into data analytics can be super interesting and satisfying once you get the hang of it, it's like going on treasure hunts! And if you’re really interested in becoming a serious data analyst pro, want to build your career or seriously widen your job prospects look no further than our insightful Data Analytics Bootcamp!
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