Introduction
If you love tackling complex algorithms, solving tricky mathematical problems, or are simply attracted to your computer or similar electronic/digital devices by nature of being a geek, then the life of a data scientist is what you should be running after.
One of the most sought-after jobs in the 21st century, becoming a data scientist requires some basic knowledge of core concepts of big data training. These concepts are discussed briefly in the article below.
What is Data?
Fundamentally, data refer to bits and pieces of information that are stored and formatted in a manner that is consistent with the rest of the data for a particular purpose. Data can be found to exist in any format, whether it be on paper, in figures or as bytes in an electronic device, or even as nuggets of information in a person’s brain. However, in the era of the 4th industrial revolution, any mention of data generally denotes the kind that can be transmitted electronically.
Since data has become the lingua franca in the IT industry, it is imperative that we also explore the many dimensions of data through its many phrases old and new:
- Structured data
Data existing in a fixed field such as spreadsheets and datasets are referred to as structured data
- Unstructured data
Information or data that is not arranged in a column-row format
- Raw data
Information yet to be analyzed and formatted
- Metadata
A data set that has been summarized
- Big data
A huge corpus of both structured as well as unstructured data that cannot be processed in the traditional manner of software technologies and database
- Big data analytics
The method of organizing, collecting, and synthesizing data to explore useful information
- Data mining
Data applications are used to figure out patterns that cannot be seen with the naked eye from a group of data in order to predict the behavior of something in the future.
How is SQL Relevant for Data Science?
SQL or Structured Query Language is a type of programming language used in standard databases for data management systems. It was conceived in the 1970s by IBM and has now become a standard process recognized by International Organization for Standardization (ISO) and American National Standards Institute (ANSI).
This programming language is employed for better management of relational databases and performance of operations on the data contained within them.
When we speak of SQL for database management, we speak of language’s capability to engage with more than one database at the same time. SQL is highly popular for data science since it is highly flexible and offers a great accessible learning curve with respect to complex depth. This enables users to create advanced data boards and tools for analyzing data.
To create and engage with databases efficiently, SQL has been utilized in a variety of tools, each with its niche market and focus. This includes big names such as Microsoft Access, PostgreSQL, and MySQL.
Nowadays, big data is a huge concept, and learning how to use SQL for big data analysis can be a great skill that will open up big opportunities for you worldwide.
Benefits of a Big Data Training
To understand how a big data analytics certification course with a SQL specialization is helpful, let us look at the points below:
- Big data training as part of business analytics courses will be a significant skill for top organizations
- Data specialists are in high demand, and a big data analytics certification with SQL specialization will be a wise career choice for you for a guaranteed upward trajectory in terms of progress.
- The role of a data specialist is a high-paying job
- You can work across industries such as data quality, higher education, sales, financial institutions, marketing, business intelligence, and more!
- Your big data training and SQL skills will put you at the forefront of important decision-making groups in your organization.
Conclusion
In the information era, if you want to carve a place for yourself in the digital world big data training with SQL specialization will be highly beneficial for you.
