Data Science Domains

August 28, 2021 Vaishali Baji
Reading Time: 4 minutes

Different Areas of Data Science –

In this post, we will talk about the great variety of disciplines that data science has. We are familiar with the top 3 words – computer science, maths, and business domain knowledge. Also, you might have already heard the definition that a Data Scientist is a person who is better in statistics than a software engineer better at statistics, but these are outdated. Modern Data Scientist is expected to be proficient in many areas and has to possess way more skills than just programming and statistics.

Statistics – because Data Science involves working with data and hard facts, the time you have to be able to separate random noise from actual trends also you need to know how to build models that classify populations and provide reliable forecast events.

Visualization – Humans don’t speak in ones and zeros, a Data Scientist has to be able to see data. And what’s even more important is to show it to others this is why Visualization is not only an art form but also a crucial skill you have to have.

Data Mining – along with visualization data mining is the creative part of your interrogating data, looking for trends, looking for anomalies, finding things that have never ever seen before.

Database and Data Processing – this involves everything from cleaning data to storage and manipulation. It’s like knowing how to hold a book and turn pages if you cannot perform these basic tasks then you’ll never get over it.

Machine Learning and Artificial Intelligence – Machine learning and Artificial Intelligence are separate here but, We would put them all together they also cater to one goal teaching computers how to learn far being explicitly program. This is advanced and this is where you go once you’ve gone quite proficient at all the other areas that you’ve covered. And moreover not every data scientist has to go here because this is a very specific set of skills that is applicable to certain business problems and challenges.

If you think this is enough then you are wrong, there are few important things that we will consider here –

Communication and Presentation – Data Scientists have to interact with people on a daily basis, not all insights are in data, some things you can only learn from people who are working with that data. Therefore if a data scientist who cannot talk to a human being for longer than 5 minutes – is not setting himself up for success regardless of how smart he is you have to be an outgoing person to be successful in this profession. Presentation is another form of talking with people only this time it’s the other way around you are trying to put information into their heads. We believe that this is one of the most important skills over data scientist if not the most important one. Because apart from having all the other expertise, we have already talked about a modern data scientist is the gatekeeper between insights and people. You can drive thousands and millions of valuable business insights but if you cannot effectively communicate that to the key decision-makers then it is nothing.

Domain Knowledge – We admire Data Scientists so much because it is an extremely transferable set of skills, one day you are searching for fraudulent transactions in a large banking corporation, the next day you building a regression model for some medical facility. Data Scientist is the only employee who can adapt to a new organization and even industry quickly.

Real-Life Projects – the same practice makes perfect says it all, this call is extremely applicable to data scientists. Unless you work on real-life projects you will not be able to add any value.

Programming – another important part of data science, the better you can talk to machines the more efficient and more successful you will be. It should become a hobby it should be something that’s just a natural part of life.

Creativity – you’d have to be creative, if you cannot generate your ideas you might find yourself stuck as a Data Analyst forever to become a great Data Scientist, you have to cultivate creativity inside yourself, allow your curiosity to guide you and help you find insights that nobody saw it you would ever find.

Happy AnalytiX.

Reading Time: 4 minutes


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