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Beginner to Advanced Analytics With SQL
A large share of enterprise data is held in relational database management systems. This makes SQL highly valuable in extracting insights from existing data tables for data analytics and business intelligence. The "big picture" suggests that in today's digital age, data is more valuable than fuel, and as a computer programming language to harness that value, learning the detail of database development, querying and processing with SQL is essential for professionals, applications, and business.
From Big Data to Data Science With SQL
Increasingly today's explosive volume and variety of data is stored in distributed clusters and cloud storage. To query such huge datasets analysts must work with a new breed of SQL engine: distributed query engines. These open source technologies are very popular and capable of querying enormous datasets making SQL a persistent skill for advanced analytics.
SQL Remains An Essential Skill
Learning how to use Structured Query Language (SQL) to extract and analyze data stored across a variety of databases is still extremely important despite the popularity of applied data science and its range of Python-based libraries to perform complex queries and analytics. This is because SQL remains a top language to communicate with the databases containing the data in today's data-driven era. SQL will persist an essential skill for anyone seeking to work in analytics, information technology, and corporate decision-making.
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101: Data Analytics with SQL

What is data analytics?

Data analysis is a process of inspecting, cleansing, transforming and modelling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Today, analytics insights are automated for improved efficiency and performance in decision-making processes and serve a variety of corporate functions. Professionals competent in advanced analytics can add incredible value to the business and this is a highly in-demand skillset today. 

Skills in data analytics

Data analytics requires many skills such as programming, statistical skill, ability in mathematics, machine learning, data wrangling, communication and data visualization, data intuition and domain knowledge, among others. Emerging and innovative technologies such as distributed systems, cloud computing, processing frameworks, business intelligence, and fast analytics are examples which continue to ensure high demand for skills in advanced data analytics.

SQL

SQL and Communicating With Relational Databases

What is the value in SQL?

Many cool things can be done with programming languages such as R and Python. But without a knowledge of SQL, a data scientist or analyst would have difficulty querying the database that contains such a large repository of raw data generated by the business. At least one computer programming language is mandatory and learning SQL should be a priority for any professional seeking good career opportunities in today’s digital world. 

As a computer programming language SQL is among the easiest to learn and this allows almost anyone to pick up an essential skill, if they are committed. While programming and scripting in SQL can become complex – especially when considerations for processing optimization are concerned – a great deal of heavy lifting is performed with very basic and intuitive commands.

The ability to wrangle raw data using SQL from the company’s data storage is becoming increasingly important. Finding new ways to combine data sets for added value means interacting directly with relational databases. As a precursor to business intelligence, analysts will frequently investigate a system’s databases, tables, variables, architecture and model to develop their next round of analysis, automation, visualization and insights.

This is a widely recognized graphic featuring the join commands that are so common in analytics using relational databases and its tables.

Almost everyone in tech will use SQL

From web development to mobile applications, SQL is a top programming language to develop the databases which invariably store and process the data needed to drive these products. Depending on professional requirements and responsibilities, skills will adapt to the specifics of the role. Nevertheless, the majority of professionals in information technology will work directly with SQL at some point in their career. Whether that be developing databases or analyzing its contents, professionals will agree, there is no outrunning SQL. 

Database Basics for Management Analytics

Relational databases require SQL

There are a variety of relational database products available. Selecting one over the other is typically an enterprise decision or a personal one, depending on the work and environment. Open-source databases such as PostgreSQL or MariaDB are free while others requires purchase or subscription plans such as the Oracle-owned MySQL. Cloud-based databases are also available with Amazon and Google, among others.

As an analyst you can ensure that you are relatively agnostic to which database you use over another if you properly understand the fundamental nature of relational database management systems and its model, and are able to communicate/code in the structured querying language . Then your work is a matter of transforming data into valuable information and knowledge.

What you need to get started

First, you will learn how to query data from a single table using basic data selection techniques such as selecting columns, sorting a result set, and filtering rows. Your next effort is where you learn more advanced query techniques such as joining multiple tables, using set operations, and constructing subqueries. It is wise to also learn how to manage database tables such as creating new tables or modifying an existing table’s structure.

How to learn SQL

There are abundant courses online teaching SQL for students. The best experience involves practice. If you are completely new, consider a course where you are also playing with simple tables in a hosted RDMS environment that way you are not stuck downloading and configuring any software. If you have access to a database system you can take courses that provide datasets while teaching more advanced concepts and techniques.

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