SQL For Data Science

The understanding of data trends, patterns, attributes and its relationship to human behavior and business is a result of data-driven cycle in analytics.

Data Science revolves around data. We associate quarterly and annual growth of businesses based on data received which are facts to help make business decisions.

Most improved business methodologies are data-driven. So, by relevance we can say SQL is all about data. SQL is mainly associated with databases and an everyday tool for database administrators and analysts. Developers use SQL to write applications that require database connections, and systems architects use SQL to design database models.

SQL is essential for any professional working with large amount of data, such as a data analyst, financial analyst and data scientist. It is a common tool in the fields of healthcare, finance, and marketing. Knowledge of SQL helps you retrieve and process data you need.

Source: Data Catchup

What is Data Science

Data science is all about using data to create and implement processes for advancement. Data science not only means coding and visualization of data but includes Impacts made based on data insights, data recommendation, and data models to solve real life problems with data by positively improving business, behavior and environmental outcomes.

These can be achieved through processes that involve data collection, analyzing, mining, optimizing and data applications

What is SQL

SQL is known as Structured Query language. SQL is used to query, insert, update and modify data. If you want to master a database, you need to understand how to query, manipulate and visualize data in same database.

SQL is a powerful tool for Database and Analytics experts.  SQL is used to retrieve data before visualizing data with tools such as: Microsoft Excel, Microsoft Access and Tableau.

Skills needed for Data Science


A career in Analytics has SQL as a basic requirement. SQL is an inevitable skill to learn for data science career path


To be an ardent analyst you should be able to communicate your analysis of data (structured or unstructured) to their consumers either by writing, verbally or using data visualization tools.

Big-Data (PIG, HIVE, Map Reduce)

Big-data deals with large structured and unstructured data sets that need to be analyzed and mined for information. The four V’s in big- data circles: volume, variety, velocity, and veracity.

Analytical Mindset: A problem solver has an attribute of an analytical mind. Data Analysis is about solving data issues, so data science is all about recognizing and resolving data issues.

Python Programming

Python can be used in so many ways as a general purpose high programming language. Python library skills for data science include Pandas, Scikit-Learn, SciPy, Numpy, Matplotlib, Keras, Tensor flow, Stats models, and NLTK (Natural language Tookit for guide through the fundamentals of writing Python programs, working with corpora, categorizing text, analyzing linguistic structure, tokenizing, stemming, tagging, parsing and semantic reasoning).

Microsoft Excel and Access

Microsoft Excel and Access are basic tools for analysts who want to present data in form of charts and tables by importing and exporting data.

R Programming

R programming is a skill for advanced analytics for understanding statistical behavior of data. Mainly used by statisticians for data mining.

R programming is a skill for advanced analytics for understanding statistical behavior of data. Mainly used by statisticians for data mining.

We have many skills out there for data analysis, a key skill to know is first understand the data before applying other skills involved.

Types of SQL Database

MySQL               developed by Oracle

T-SQL                  Microsoft SQL Server) – created by Microsoft

DB2                      created by IBM

SAP HANA        created by Hana

NoSQL                 created by Carlo Strozzi

Maria DB             developed by Maria DB Corporation

PostgreSQL      developed by Postgre Global development group.

MongoDB           developed by MongoDB Inc

Advantages of SQL

The advantages of SQL is inevitable at this moment most companies are gathering, mining and developing on data within their reach.

  • Open Source

SQL as an open source database management language and has a community of developers. Many developers post practical SQL queries on Stack Overflow. Some SQL databases have free versions (MySQL, Maria DB, and Postgres).

  • Universal Language

SQL is a universal language for databases and spreads across disciplines that handle data. A knowledge of SQL can help enhance progress into programming such as Python programming and others.

  • High in Demand

SQL has a high demand in the current job market. Companies are in need of experts with extensive SQL knowledge for extracting or manipulating data from databases. This ensures high earning wages for such individuals.

  • High Speed

SQL queries are used to retrieve large amount of data ranging from millions of rows to numerous number columns in a table from a database.

These millions of records of all sizes are pulled efficiently in seconds or minutes from the database.

  • Create, Update, Retrieve and Set Permissions

SQL is used to create databases, tables, views, stored procedures and create backup of databases. Databases can be updated using SQL queries.SQL helps in retrieving data from a database and set permissions for views and stored procedures.

  • JOIN’s Attribute Tables

SQL is very good for joining relationship tables as one object for clearer understanding to user.

  • Well Defined Standards Exist

SQL databases use long-established standard, which is being adopted by ANSI & ISO. Non-SQL databases do not adhere to any clear standard.

  • Emergence of ORDBMS (Object Relational Database Management System)

Previously SQL databases were synonymous with relational database. With the emergence of Object Oriented DBMS, object storage capabilities are extended to relational databases.

Dis-Advantages of SQL

  • Difficulty in Interfacing

Interfacing a SQL database is more difficult than including a couple of lines of code. In spite of the fact that SQL databases complete with ANSI and ISO measures, a few databases go for restrictive augmentations to standard SQL to guarantee seller secure.

  • High cost – Microsoft SQL Server is made to support millions of records across an enterprise. Because of its robust technology, it also comes with a high price for the enterprise based paid version.
  • More Features Implemented in Proprietary way

Although SQL databases conform to ANSI & ISO standards, some databases go for proprietary extensions to standard SQL to ensure vendor lock-in

Why should I learn SQL for Data Science?

Conclusively, learning SQL for Data science career path is very essential because SQL is a core skill that businesses desire since they all deal with data in large amount. You cannot have an outstanding performance in an advanced analytical position without the knowledge of SQL.

As companies, strive to accomplish more with their information, they will require more people with the skills to analyze and manipulate data.

That is to say SQL revolves around data but not complex business logic.

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