Data Science is the top of the tower in the present world you must have definitely heard about Data Science, from many people, And there is a huge demand for data scientists in various industries, right.

But I bet that no one would have told you about Data Science, which we are going to discuss in this article.

So here we have 10 things you don’t know about Data Science, Now, let’s get started.

What it takes to Become a Data Scientist?

So to become a data scientist, you don’t really have to necessarily possess a degree or a Ph.D. in Data Science.

It is important for you to know the fundamentals of analytics since you need to have the capability to work on analytics tools and understand the basics of data processing, To get started.

You also need to understand how a Data Science project lifecycle works, and how to design your model to fit into the existing business framework.

So these are some of the things that you will need to know to succeed as a data scientist. So do you know guys that every company has a distinct approach to Data Science? Exactly.

It’s completely impossible for you to know everything in Data Science, So what would help you with a knowledge of some of the universally recognized and adopted technologies in the area of Data Science.

Things You Should Know to get started.

It’s important for data scientists to have the knowledge of statistical analysis, which would help in making sense out of the data and drive insights.

So it’s extremely important to know advanced programming, As data scientists will be involved in working on complicated algorithms based on machine learning and data analysis.

 it is also required to have hands-on experience in languages like R and python, And it is necessary for data scientists to have knowledge of big data tools and frameworks like Apache Hadoop, Apache Hive, etc.

It also includes having knowledge of big data visualization tools, just like Tableau, ClickView, etc. 

Data is Never Clean

Let me tell you the fact about Data Science, So data is never clean. Yes, analytics without real data is more a collection of hypotheses and theories.

And so data helps to test and find the right suitable solutions in the context of the end-users. However, in their real world, data is never clean.

Even the organizations which have well-established Data Science centers for decades, their data also is not clean.

a large portion of data scientist time will be spent in just cleaning and processing data for model consumption.

So if you can do this with equanimity, and focus on the big picture, then perhaps you should aim for research and statistics, And rather than a career in Data Science, Yes.

Data is Not Fully Automated

Now, since it is not clean, and requires quite a lot of data processing, there is no Ready set of scripts or buttons to push to develop the analytic model.

So that’s the reason why there is no fundamentally or fully automated Data Science, as each data and problem is different.

So there is no substitute for exploring data, testing models, and validating against business sense and domain experts.

So depending on the problem, and your prior experience, a data scientist needs to get their hands dirty to find solutions.

So here are the only exception could be if you get data in a specific format, and keep doing the same thing over and over.

But don’t you think that this would actually become so boring for you, isn’t it? Well, let’s move ahead.

How data scientists create models?

Do you know guys that no one really cares about how data scientists create models Exactly? As the consumers of Data Science models are decision-makers and executives who don’t bother about how you create models, and what they want is a workable and useful model.

So while it’s tempting for data scientists to explain the technical expertise behind the model, and show the analytic rigor, this is often counterproductive.

This is to see that data scientists’ audience would only care about the outcome and end-use and we’re not really bothered about the decision engine data scientists to have put together.

And then another thing that you won’t be knowing about Data Sciences that just because the analytic model is great, it does not mean that it will see the light of the day. Exactly. That’s true.

As there are many factors which are influencing it and the analytic project gets shared for various reasons all the time, including data change, problem change, no one interest Submit the solution or implementation too expensive, etc.

Machine learning and Data Science

Do you know guys that machine learning and Data Science can walk hand in hand? Yes, as we know that machine learning is the ability of a machine to generalize knowledge from data.

So without data, there’s very little that machines can learn, So in near future, the increase in usage of machine learning in many industries will act as a catalyst to push Data Science to increase relevance.

So machine learning actually is only as good as the data it is given with, and the ability of the algorithms to consume it.

So going forward in the near future, basic levels of machine learning will become a standard requirement for data scientists.

And that is why we say that both technologies actually go hand in hand, Let’s go ahead there.

IoT and Data Science.

Did anyone tell you guys that IoT is the latest technology that contributes to Data Science to a significant level? Yes, that’s true.

Well, IoT refers to the ecosystem of devices connected to each other, Why our internet? Let’s see smart homes smartwatches, headgears, are all part of the IoT ecosystem.

So Data Science is very closely associated with IoT, Because IoT is all about data generation, And Data Science is all about analyzing it.

So on becoming a data scientist, you’ll also be updating your skills enough to be part of this next big tech revolution that is IoT.

Start-up in Data Science

Now, do you know guys that with the expertise in Data Science, you can actually start your own startup sounds quite exciting, isn’t it?

So here are you can simply focus on fields or industries where poorly informed decisions are currently being taken due to the lack of better alternatives.

For example, you can simply start a data monetization business, Perhaps the most obvious way of monetizing the data is simply to sell it to other organizations, which are in need of organized and insightful data.

Well, this was my idea, But if you learn Data Science, you can simply start your own startup with your own idea. How about it? Now let’s go ahead.

Learning VS Practicing Data Science

So more than learning Data Science, what is more, effective is practicing it. That’s why it is said that practice makes perfect.

So if you intend to take up a Data Science course, make sure that your course offers many projects, case studies, and enough real-time data sets to work on.

So more than theories, it’s all about hands-on experience, because your hiring manager will be looking for someone who can not only analyze data but can also advise on selecting the right business problems to the solutions and how one should actually use their big data.

So they need to have a solid understanding of the industry workings and the impact of insights on business decisions.

Conclusion

 So this brings us to the end of the article, If you have any doubts, you can comment in the comment section below. We will get back to you as early as possible. Thanks for reading.