Although data science is not new, the need for high-quality data has recently skyrocketed. This is an evolution, not a fad or rebranding. Today, real, actionable data, not assumptions and conjecture, is used to guide decisions that affect everything from successful presidential campaigns to a one-man firm with its headquarters at a kitchen table.

Data science is expanding so quickly that a vast ecosystem of practical tools has developed. I'll be rolling it out over the next few days and explaining what I think it all means. I've spent the last month trying to organize this ecosystem into a coherent portrait.

Since data science is so fundamentally interdisciplinary, it can be challenging to classify many of these businesses and tools. At the most fundamental level, however, they can be divided into the three components of a data scientist's workflow. Specifically, gathering, organizing, and evaluating data, I'll discuss them in that sequence, beginning with gathering data or finding data sources.

Data sources

The rest of this ecosystem would be meaningless without the data to power it. In general, there are three types of data sources: databases, applications, and third-party data.

1. Databases

Unstructured databases predate structured databases. The structured database market is worth around $25 billion, and our ecosystem includes big names like Oracle and a few upstarts like MemSQL. Structured databases store a limited number of data columns, are typically run on SQL, and are typically used by business functions where precision and dependability are critical, such as finance and operations. Visit the Data Science Course in Delhi to learn SQL and other database technologies.

1. Applications

Storing critical business data in the cloud has gone from unthinkable to commonplace in the last ten years. This is possibly the most significant change in a company's IT infrastructure.

I've mentioned four major examples in the ecosystem's application space (sales, marketing, product, and customer), but nowadays, every business function has several SaaS applications to choose from.

SalesForce most likely started the trend. They were the first highly successful enterprise data application to create and target their software to end users rather than CIOs. As a result, SalesForce was developing and iterating software for sales teams rather than individual CIOs.

They created something that worked well for their users, demonstrating that enterprise customers would be willing to store critical company data in the cloud.

They keep track of account information, personal identifiers (such as your first and last name), loans taken out by their customers, and so on. A bank must always be aware of the amount of money in your account, down to the penny.

There are also unstructured databases. It's no surprise that data scientists pioneered these because data scientists approach data differently than accountants. Data scientists are more concerned with flexibility than with absolute consistency. As a result, unstructured databases reduce the friction associated with storing and querying large amounts of data in various ways.

1. Third-party data

Unstructured databases and data applications are much older than third-party data. Dun & Bradstreet has been a data seller in business since 1841. However, as the value of data grows in every organization, this space will continue to evolve in the coming years.

The oldest is business data. Although I mentioned Dun & Bradstreet earlier, business data sellers are critical to almost any organization that deals with those businesses. Business data for any B2B company answers the critical question: Who should my sales team be talking to? That data is now used in various applications ranging from online maps to high-frequency trading. 

So these were the main data science ecosystems which you must be aware of. If you are a data science aspirant, have a look at IBM-recognized Machine Learning Course in Delhi, to gain in-depth knowledge of top-notch tools..