Must Know Facts About Data Science in 2021

24-Apr-2021

The year 2021 has brought with it a lot of changes and shifts in the way the world thinks and functions. In light of the recent events related to the COVID pandemic, the need for data science has grown immensely. There are many more applications of data science than before.

One can learn more about the applications of data science by taking a Data Science course. In this article, we will discuss the “Data Science Facts in 2021”.

2021 Data Science Facts

1. Data is never clean -

To begin the process of data science a data scientist has to first acquire massive data sets of unformatted or formatted data. These data sets can be obtained from many different sources such as company records, government records, old offices, old warehouses, user feedback, customer reports, agency reports, scientific studies, governmental studies, and scientific discoveries.

From these varied sources of data, we can easily imagine that the data that the data scientists manage to obtain will not always be clean or nicely formatted. It will not always be in an organized and systematic format, and it may sometimes be incomplete or hard to decipher. The Data Science certification discusses more these kinds of data.

A data scientist has to face and tackle many such challenges when they obtain raw data from any of these different sources. In fact, many times they even have to reject and discard many data sets that they obtain because they are near too unusable and practically useless from the point of view of the data scientists.

2. You will spend most of your time cleaning and preparing data -

As we have mentioned above, the data sets that the data scientists obtain are often not organized or nicely formatted. The data in them is often unclean and unusable and practically useless from the point of view of the data scientists. But they can hardly ask for new data sets or ask someone else to make the data organized, nicely formatted, and usable.

So they have to do all the hard work themselves, with their bare hands. They have to dive deep into the data and step through it row by row and column by column.

Then they have to analyze it carefully and see which parts of the data they can modify by using their sophisticated and advanced tools and which parts of the data they can alter by applying sophisticated and advanced computer science algorithms.

Sometimes they may only have to make small or minor changes but sometimes they may have to make large overarching and sweeping changes to the entire data sets in order to make them organized, nicely formatted, and usable. The Data Science certification teaches one how to make such changes.

In this way the data scientists have to clean and prepare the data before the actual process of data science can begin. They have to bring the data to a usable state so that they can start to analyze it and modify it through computer science algorithms and extract meaningful patterns out of it.

After that, they try to identify and determine what the emerging trends in the data are.

3. Data science is rarely automated. One will have to dive deep into the data -

There is a common misconception that the world of data science is fully automated. People think that data science algorithms, data science tools, data science knowledge, and the skills of the data scientists have advanced to the point where most, if not all of the work of the data scientists is carried out automatically, in an automated fashion. This is not the case, however.

The field of data science still has a long way to go and there are a lot of nicks and creases to iron out before data scientists can reach the stage where all of their work is fully automated.

Data scientists right now are not able to make all of their work fully automated and have to do a lot of tasks manually. Some tasks however can be automated and one can get a Data Science certification to learn how to do that.

They have to use their tools of analysis and modification and many sophisticated and advanced computer science algorithms to study the data sets in their possession and to modify them in such a way that they become amenable to being studied and dissected by the intellects of the data scientists.

They have to dive deep into the data and step through it row by row and column by column. Then they have to analyze it carefully and see which parts of the data they can modify by using their sophisticated and advanced tools and which parts of the data they can alter by applying sophisticated and advanced computer science algorithms.

Along the way, they have to pay careful attention to a lot of minor details and nitty-gritty. They have to handle the data delicately and with care.

Otherwise, they might end up damaging the data set in the course of running their data science operations on it. The Data Science certification talks about the risks and dangers of mishandling large data sets.

4. 95% of the tasks do not require deep learning -

The word deep learning has become a buzzword in the field of data science and in the world of technology at large. So many people think that deep learning is an absolute requirement for all kinds of activities related to data science. However, this is not the case.

Deep learning is more of a specialized branch of data science. It is almost esoteric in nature. The knowledge of deep learning is very complex, very advanced, very sophisticated, and very difficult to comprehend and understand. There are entire books written on the subject of deep learning and many online courses dedicated to teaching the subject.

Deep learning is a subset of machine learning. Both branches of knowledge or both fields are directly connected to teaching machines how to learn.

This means that the data scientists try to give machines the ability to make decisions based on the information they gather from their environment without any outside or direct intervention from the data scientists. One can get a Data Science certification to learn how to practice deep learning.

This branch of data science can be said to be quite niche and is not always required or applicable in all activities or projects based on data science.

5. Big data is not all-important -

Similar to the word ‘deep learning,’ the word ‘big data’ has become a buzzword in the field of data science and in the world of technology at large. So many people think that big data is an absolute requirement for all kinds of activities related to data science. However, this is not the case. The Data Science certification debunks this myth.

Big data is more of a collection of tools and techniques which have been created to handle and deal with large volumes of data contained in massive data sets.

By massive data sets, we are referring to exabytes of data. Big data of this sort is found in the databases of technological giants like Google, Youtube, Facebook, Twitter, Instagram, and in the databases of government agencies and miscellaneous governmental entities.

6. People seldom ask how you did it -

The field of data science is a very results-oriented field. Data scientists are very results-driven and focused on obtaining the results or the solutions to some questions or problems.

They are not particularly concerned about how those results will be obtained. This methodology or approach is explained in detail in the Data Science certification.

In short, in the field of data science, the ends justify the means. No one will ask too many detailed questions about the method used to extract some patterns or to identify any emerging trends.

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