The terms “Data,” “Science,” or “Data Science” are not to frighten or terrify readers. “Data Science” doesn’t arouse dread, but it also says little about the field. In a layman’s perspective, everyone is aware of what data is. On the other hand, the term “science” can refer to any set of activities that employ the scientific method.
What is data science?
Data Science, according to Wikipedia, is a field focused on collecting knowledge and insights from data using scientific methods. What it does not tell you is that we are all born data scientists. How? Let’s see what happens.
Whatever you’re doing, you’re watching the world around you. Every waking minute, you get information from your environment and send it to your brain.
It’s just as straightforward (in principle) as the above example suggests. You have data, methods, algorithms, and tools at the end of the day. All you have to do is extract information from it. To do so efficiently, you must adhere to a workflow/pipeline. Let’s explore what everything is contained in a typical Data Science Pipeline.
Data Science pipeline
The data science pipeline describes the complete process, from getting the needed data to making correct computations and forecasts.
Obtaining data is, by definition, the first step in doing Data Science.
Just a heads-up: there are several factors you should keep in mind while gathering your data.
You must identify all of your datasets (which may be from the internet or internal/external databases).
The data should subsequently be converted into a useful format (CSV, XML, JSON, etc.)
Skills required
- Database Management: Depending on your needs and requirements, you can use SQL or NoSQL.
- These databases are being queried.
- Unstructured data retrieval in the form of movies, audios, texts, documents, and so forth.
- Hadoop, Apache Spark, or Apache Flink are examples of distributed storage systems.
Application of data science
As it has become evident, Data Science is a wide phrase, as are its uses. Virtually single app on your smartphone depends on data. Hence,
Let’s have a look at some of the wide sectors where Data Science is being used:
- Internet Lookup
How does Google return such *accurate* search results in milliseconds? Science of data!
- Recommendation Engines
Everything is powered by data, from “people you may know” on Facebook or LinkedIn to “those who purchased this product also liked…” on Amazon to your daily customized playlists on Spotify to even “recommended films” on YouTube.