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Myths associated with data science

We have a task for you, imagine a data scientist, close your eyes and imagine a data scientist, what image comes to your mind when you think about the data scientist they tend to imagine a person who is in a black coat doing all doing artificial intelligence, applying and analyzing all sorts of data and numbers but in reality, (that’s what in front of you was a myth) a profile of data scientist requires the skills of polymath a person whose expertise spans across significant numbers of different areas, a person who embodies an entrepreneur spirit and curiosity of Leonardo Vinci.

What is data science?

Before data science, we were familiar with the term data mining, which can be defined as the overall process of discovering useful knowledge from data? In 2001, William S Cleveland wanted to bring data mining to another level He did by combining computer science with data mining basically he makes statistics more technical so that you can take advantage of computer power and he names the combo "data science," which he hoped would broaden the possibilities of data mining and provide a powerful force for creativity.

A data scientist can predict trends by analyzing the photographs that people share on Instagram or Facebook with hashtags such as #artoftheday they can collect a hundred and thousands of information and then analyze the underlying influence of people sharing those photos because they can interact with who and then patterns emerge in those photos that the most influential photo people share using the hashtags.

This field is marvelously involved and so do the myths associated with it. So, are you ready to unleash all the myths that you have heard and learn about data science?

First, we heard people saying, it’s not for me because it has maths, and you know I’m not that good with it,

Data science does necessitate knowledge of statistics and probability. As a data scientist, however, you will never employ statistical techniques to calculate the outcomes of complex equations. With tools (both paid and free) now available, today's data scientists must concentrate on knowing the interpretation of these methods (when and why to apply them, and how to interpret the results) rather than the application's mechanics (how to calculate). One may go a long way in the data science field with the correct balance of logic and common sense.

Second, we heard people saying, Ph.D. is a necessity for making a career in data science,

Ph.D. is a fantastic complement, but before you get too serious about your job as a data scientist, you should be aware that the data scientist’s role is classified into two categories: applied and research.

Working with current algorithms and understanding how they work is the main focus of Applied Data Science. To put it another way, it's all about putting these techniques to use in your project. This position does not necessitate a Ph.D.

What if, on the other hand, you're more interested in a research position? Then a Ph.D. may be required. Inventing new algorithms from the start, researching them, producing scientific articles, and so on - these all things require a Ph.D. mindset.

Third, A degree is an alternative to learning a tool, why would I learn a tool? Just like a Ph.D. is a great addition to your resume, similarly learning tools will be a great addition and help. There is no doubt that it necessitates analytical and business skills, as well as an awareness of how statistics, machine learning, and AI(Artificial intelligence)may be used to solve business challenges. A Data Scientist should have strong problem-solving abilities and understand when and how to use a tool or algorithm to achieve a specific business goal.

Fourth, we end the article with this. I overheard this young fresher telling his coworkers, data science is all about modeling, There are several layers in data science. The model-building phase is merely a small blip of the larger data science lifecycle, Let’s give you an idea of what a typical data science lifecycle entails,

Understanding the statement of the problem, Developing hypotheses, Obtaining information, Verifying the information, Cleaning up the data, Analyze exploratory, Creating the model, Putting the model to the test/verifying it, Return to the verification of cleaning stage if a mistake is discovered.

If your mind is stuck in your career and you have this curiosity to learn more about this fascinating field, get away from the negativity and avoid these myths by educating yourself as much as possible. Data science has the potential to provide you with the long-term success you desire.

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