This article was published as a part of the Data Science Blogathon
Introduction
People frequently hear the term “Data Science” but are unsure what it means or how it differs from “Machine Learning” and “Artificial Intelligence.”
They frequently come across certain popular myths about this subject and make incorrect conclusions as a result.
This blog aims to not only differentiate between these terms but also provide you with conceptual clarity on the subject, make you understand how Data Science is often associated with some common misconceptions.
Before delving into myths related to Data Science, it’s necessary to understand what it is.
What is Data Science?
The world has landed into an era where enormous data is generated at an unstoppable pace every day. Considering this, ways to store data and process it has become a major challenge for the technical industries. This is where the concept of Data Science comes into the picture. This enormous amount of data is extracted, analyzed, and divided in such a way that we could draw meaningful inferences from it. This process is called Data Science.
Companies exploit this data to make much better and smarter decisions to extend their business profit. A famous example of this is Netflix. Netflix analyzes the pattern of their viewers, what kind of movie or show are they watching right now, what is their watching pattern, what do they particularly find interesting, etc. Based on this information, Netflix comes up with suggestions and decisions to bring a show that would drive the interest of maximum viewers. The same goes with e-commerce websites where companies analyze the shopping pattern of their customers and bring those products into the market that would attract customers’ attention the most. These examples are enough to make you realize the huge amount of power that the ‘data’ holds. Processing this data is what makes it meaningful.
TIME TO BUST SOME MYTHS!
Now that we’ve gotten back to the point of this blog, let’s get you introduced to some common misconceptions about Data Science.
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Data Science is all about Machine Learning and Building Models.
Data quality is the most crucial aspect of the data science process. Clean data is essential for building better machine learning models since it improves the model’s overall performance and accuracy. Despite being very time-consuming and unpleasant work in the data science process, we could not deny the fact that preparing and cleaning data is a critical step since poor quality of data can lead to inaccurate insights, and could coss a major loss to business organizations. We have far too many data sources available. Understanding which data format to choose, taking into consideration the cost of requirements and gathering data sources, dealing with the faulty data or glitches in its collection, storing the data efficiently in a database, are all miniature versions of the kinds of inquiries we have to ask in the real world. There are many processes involved before creating and deploying the model. So the model building is just a part of Data Science, not the Data Science itself. Maintaining and monitoring the model, retraining it if necessary, are also some of the steps involved in data science. So, having sufficient knowledge about the algorithms and implementation of model-building approaches is not enough to make you a data scientist.
2. Data science is dominated by mathematicians.
Lack of understanding about the implementation of Data Science in businesses often leads to this misconception that a solid mathematical background is important for anyone to pursue this field. Good knowledge of statistics and probability may indeed act as a helping hand in the process of learning data science, however, these are of no use when you don’t know where can you apply these formulas as a data scientist.
For example, you may know how to calculate z-score or the Chi-Square formula, but if you don’t know how can you use these formulas to build a more accurate model, knowing these formulas won’t help you. There are many software available where you can directly compute these values. So focusing on the interpretation of statistical techniques rather than their mechanics is what a data scientist requires. And that is why Data Scientists need not necessarily be mathematical geeks.
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Artificial intelligence will soon substitute data scientists.
In the process of evolution of Data Science, it is possible to say that the activities done by data scientists manually may be replaced by artificial intelligence. But a machine cannot know by itself as to what decision it should make to clean the data, to build an efficient model, to work on the accuracy of models, etc. These decisions depend on the person with the right qualifications. Even if steps are being taken to develop more sophisticated algorithms in the hope to reduce the requirement of Data Scientists, it is very unlikely to happen shortly. Even with the most advanced algorithms, we would still be requiring someone with solid judgment and expertise in the domain to keep the businesses running. Data scientists aren’t going anywhere. Their skills are already in high demand. And it’s only going to increase shortly.
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It’s all the tools you’ll need to become a data scientist
It’s a common assumption that mastering the statistical tools and libraries would be enough to call yourself a data scientist. Experience in working with these tools may provide you a better understanding, but data science is a combination of many skills. Learning about the tools associated with it is just a part of the whole. It can be termed “essential” but not the only specification required in the whole data science spectrum.
Skills such as problem-solving, a good grasp of the concepts, information regarding the correct applications required for a business problem are also important to be mastered along with mastering the tools such as Python or R. Being able to write the code using pre-existing libraries and tools are not enough to label yourself as a “Data Scientist”.
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To succeed in the field of Data Science, you must be a hardcore coder.
Writing codes efficiently, understanding Data Structures and programming, having a solid computer science background are qualities that people think are necessary for one to become a data scientist. Being an excellent programmer is quite different from being an excellent data scientist. We can see that there isn’t much coding involved if we look at what Data Scientists do daily. In fact, the majority of the approaches or algorithms are widely available and only require minor adjustments. A logical mindset is indeed required but being a hardcore coder is not necessary for it. Many great data scientists began their careers in this field without a proper programming background or experience.
End Notes
I believe many myths have still to be debunked, and this blog will not be able to cover them all. However, I believe people must recognize how dangerous it may be to be unaware of these myths. Because of the misinterpretations that abound in this domain, many people lose confidence before even trying to take up a career as a Data Scientist. I hope this blog cleared up some of your misconceptions and motivated you to pursue a career in data science.
About me:
My name is Arya Dwivedi. I am a Computer Science Student aspiring to be a Data Scientist. Thank you so much for taking out a few minutes to read this blog. Feel free to provide constructive feedback.
Thank You
Email: [email protected]
Social Media :
LinkedIn: https://www.linkedin.com/in/arya-dwivedi/
