A decade back, nobody really believed in technology taking over what was once only ‘humanly possible’. Today, the paradigm is shifting and technology is set to transform the core industries. From Healthcare to Banking & Finance, we will see a huge revolution in years to come. How this impacts the workforce is a very important question. Since a lot of work is yet under progress, a majority is skeptical about how it’s going to affect their line of work. But, to be ready is the ‘key’ to sustain in the future. This article tries to answer some of the most intriguing yet confusing queries related to the future of Data Science and how one can prepare themselves by re-designing and upskilling for the times ahead.
According to a Linkedin report from August 2018, in the US alone, there is a shortage of over 100,000 thousand data scientists. That makes this domain one of the most sought after profession. But with this also comes a lot of confusion, especially related to longevity and stability of jobs in data science and how to deal with it.
This brings us to the most important question, what are the minimum requirements to become a good data scientist? The internet will give you a lot of information, but a lot of it is going to demotivate you. Thus, we have tried to categorize & summarize some of the most common myths about data science and career transitioning.
Data Science-Deep Learning-Machine Learning-Artificial Intelligence related myths
As a beginner, these terms might be enough to confuse you and lose track of your transition. Here are some common myths:
Myth No.1: Data Science, ML or AI?
Data science is a vast paradigm and often overlaps amongst DL, ML, and AI. But, one must not confuse themselves and treat it as a separate entity.
Data Science facilitates processes and tools to extract and interpret huge amounts of data faster and efficiently. ML is often the link between AI and data science since it is analyzing and deriving meaningful information from data over a period of time. In a precise way, we can say that ML is a subset of AI that results best when used with data science.
Myth No. 2: Learn the fancy, first!
In continuation to the above point, as a beginner, new terms seem dreamy and you want to get a grip of these technologies quickly. But, that’s where it goes wrong. Like any other core discipline, even data science, ML, AI must be taken seriously and dealt with carefully. If you truly want to dive into the future, you must introspect which way you want to go. There is a lot to read on the internet and help you make a structured plan.
Myth No. 3: Artificial Intelligence will obsolete data scientists
Once you understand the concepts these technologies thrive upon, you will know for sure that even with the most advanced algorithms and models, we will still need skilled professionals to steer them. The focus is on creating more accurate and self-generating algorithms and systems, but even the most sophisticated ones will need human interference and judgment.
Myth No. 4: AI jobs will consume the entire market
Even if we consider AI to sweep off all the current technologies, the fact that a project team is a collaboration of various skilled professionals can’t be ignored.
Roughly, any AI project constitutes these members and to just understand AI will not help. Here are some skilled experts (in no particular hierarchy) that make a successful AI team:
As is evident from the above diagram, to generate an efficient process or product, you need a team and just an AI expert won’t deliver the desired results.
Myth No. 5: General AI (once developed) will be autogenic and no longer require the human expertise
Ok, so most of it is what’s explained in the above point except the fact that a general AI will have both the power and means to self-learn and implement processes. But, firstly, we are way too far from reaching that point yet. Secondly, even if that happens, a general AI would still require some human to counter bias or fraud. We must never forget, the origin of these algorithms is subjected to human bias and thus needs a constant review. Thus, we can never negate the role of human expertise.
Myth No. 6: Data Scientist/ Engineer/Analyst are no different from each other
This is one of the most asked queries. People keep confusing these skills. But, these are totally unique professions and each has its own role on a project/product development. For example,
A data scientist deals with data modeling and products, whereas an engineer works on architecture, data collection, code, and transformation. Role of a data analyst as the name suggests is to analyze data and apply various statistical formulas to generate meaningful insights.
You can read more about the differences here.
Myth No. 7: Scientist and Data can’t be a match
A scientist is someone who gathers data, studies it and generate meaningful information from it. Now this study could be scientific, technical or simply to help your business. Therefore, a data scientist must not be judged by their domains but by the nature of their work.
Myth No. 8: You need to be a Mathematic Wizard
It is one of the pre-requisites but not the only one. When companies lookout for data scientists they definitely want sound analytical and logical reasoning. A data scientist deals in a lot of skills varying from domain knowledge, programming to statistical formulae, thus, an upper hand in mathematics could come handy but will not make you a lesser contender for a job if you don’t have a mathematics degree.
Myth No. 9: You need a doctorate or specialization
Data science has been around for quite some time now but still lacks skilled professionals. One reason is that everybody thinks only a Ph.D. deserves this job. While a doctorate might give you some extra points but you must also know the trade. Just some fancy degree will not help you do your job. What one needs is a thorough grasp of concepts and tool expertise to make it count.
Myth No. 10: You are ahead of everyone if you know how to code
Just like the above two myths, this one generated from a sheer lack of understanding about how data science works. A data scientist role varies on what the project and the organization demands/needs. Not all data scientist vacancies require people with coding skills. The most important skill to be a data scientist however is analytical capabilities.
Myth No. 11: You need a regular data science degree
This is where a lot of experts disagree. Some think that a data science degree will give you a cutting edge knowledge regarding the domain. This again is quite dependent on demand. Some companies prefer professionals with data science degrees while others take a holistic approach and look for versatility.
While a data science degree is a quicker way to get under the subject but a majority of people transitioning to data science still rely on self-learning.
Myth No. 12: You need to have a degree/expertise in either of the following
Initially, data science seemed to be a technical stream and it was considered a CS/Programmer’s job. But now we know that it’s a huge field and with quite a versatile range of skillsets. The only thing that will separate you from the rest is exposure and that’s it. No degree is going to get you the real-time experience which is the most important aspect given the varied nature of projects and needs.
Myth No. 13: Mastering a tool will land you the job
In point 10, we discussed how coding could add to your skillset but it’s not going to help you stand out from the rest. Similarly, tools can help you get deeper but you still need a little bit of analytical and problem-solving skills.
Myth No. 14: Whatever you have learned and worked on will translate into data science
It depends on, where you come from. If you are transitioning from an unrelated domain, no matter how well you know your job, it won’t help. But, sticking to your expertise and learning data science skills related to your domain could get you your dream job and make your future-ready.
Myth No. 15: Data science competitions are going to make you an expert
Competitions are not even close enough to real-life projects and requirements. They might give you an understanding regarding the domain but that’s it.
Myth No. 16: Data science is just about predictive analysis and tools
A general data science life-cycle has the following phases:
Data science is so much more than just some predictions and assessments. It’s a constructive study of techniques and rules. But all this understanding comes with real-time experience. So grab the next opportunity, however small it is and get started.
Myth No. 17: Data Science is difficult to integrate
Python and R are the most common and known examples to bust this myth. What can’t you achieve with these? From the data analysis to complex data models, R/Python helps you link to these complex systems with the help of few APIs and open source connectors. These platforms are versatile enough to integrate with legacy systems, databases as well as new technology systems being developed today.
Myth No. 18: Data science flourishes on huge data thus, is useful for bigger enterprises
Data science flourishes on how well data is managed and used for generating insights for business outcomes, the size of the data doesn’t matter. All organisations don’t create terabytes of data everyday but that doesn’t mean that they can’t leverage Data Science. There are thousands of enterprises across industries that generate limited amount of data but generate valuable insights and get tangible benefits. Of course, you can apply Data Science techniques to large or medium size datasets but there are also ways through which Data Science techniques can be applied to very small datasets (as small as having only a few hundered data points).
Myth No. 19: Data science has no financial gains
Well, it’s not termed as the ‘Sexiest job of the 21st century’ for no reason. IDC predicts a 29% compound annual growth rate of Data
The median base salaries of data scientists have remained relatively
Data scientists holding a
Myth No. 20: It’s just a buzz!!
One reason behind this myth is the frequency at which data science is expanding. With such a versatile skill set and options, it’s really difficult to keep up with it. Thus, it seems like any other short-lived technology which might fade in a couple of years. But the matter of fact is that data is growing exponentially and need for it will only grow in
Data Science is taking all the industries by storm and professionals across the world are building skills to leverage it in their industry and functional area. However, a lot of us get demotivated by these myths and misconceptions. In fact, building foundational skills has become really simple and easy with many online and