Data science is one of the most attractive jobs in the present data-driven world.
Glassdoor ranks data scientist among the topmost jobs of the year 2020. Also in terms of job satisfaction, which is one of the essential parameters of job rankings, it ranks Data scientist jobs among the highest scorers.
Linkedin, according to its report on emerging jobs, has maintained data scientist jobs in the top position for continuous three years running.
Nonetheless it is comparatively new in the job market, the term itself – data science – is no more unheard-of by most of us, though we may not be fully in terms with what it really means.
In simple words, in a world that is continuously bombarded with astronomical data of all kinds, data science makes sure that the huge incoming data is properly and efficiently made to use to give maximum profit to the intended business or industry. Data science structures the source data, models it and streamlines its flow to a proper destination.
As a matter of fact data science may be a nascent term, its origin is old and branches out from an amalgam of probability, statistics, mathematics, programming and data analytics.
Before furthering on this topic, let us briefly understand a couple of terms that go hand in hand with data science.
It refers to identifying the incorrect, corrupt, duplicate or irrelevant data, discarding the unwanted ones and replacing or modifying the bad data with the good ones. Data cleansing is a very important step which perhaps is the opening gambit of Data science. Without a meaningful and correct data, the entire business or operation may stand fatuous.
An example may be the head count and general biographic information of affected people during an epidemic. A wrong data may lead to absolutely misleading statistics and predictions.
The name itself suggests the meaning which is nothing other than data that is big or voluminous. Big data is a humongous or so fast data that it is next to impossible to be handled by traditional methods. Hence emerges the savior i.e. Data science to lead the big data story.
“Artificial Intelligence is – intelligence demonstrated by machines, in contrast to natural intelligence displayed by humans and animals”.Wikipedia
In other words, the aim of AI is to make machines or devices that perceive the environment, study different behaviors against them, form an algorithm and act in accordance with the algorithm formed by the machines.
Machine learning, that is akin to AI, is one of the widely used data science attributes, that we shall see in a while.
Data scientists are not rocket scientists but they sure are one of the most sought after professionals in the job market today. This owes to the fact that the amount of data produced daily is enormous, so the demand for data scientists is very high. But their supply is way below par. This demand-supply gap leads to a high pay scale of the data scientists.
There is an ocean of opportunity in the field of data science which is implemented across various verticals. Be it healthcare, security, finance or robotics. You name a field and data science would be a player there in some form or the other.
Whether you are a student or a professional, you would usually never regret aiming at data science as a career option. You would understand its significance and start appreciating data science more as you read on.
Who can learn Data Science?
The good news is, it is quite viable to learn data science. There are professional degrees as well as online courses to help you kick start your tryst with it.
A piece of better news is, you do not need a very high educational background/degree to get eligible to learn the nuances of data science. An undergraduate degree would suffice. Just a bit of understanding of any programming language, good analytical skills and you are ready to tilt the learning curve! Going with the current trend, taking an online course from the comfort of your home is a great and convenient way to pitch-in to this smokey hot field.
Let us now delve into the top 10 reasons why you should learn Data science –
- Makes You Rich
Well, who wouldn’t want to get rich! And when it comes to data science, it offers remarkable emoluments along with an attractive job profile. Data scientists bring a great value to the table and are highly sought after professionals in the IT field. They are the pivot of the core decision making team in terms of data and hence carry a marked aura.
Like I stated earlier, there is a huge gap between demand and supply of data scientists with their demand on the higher end. This ensues their default high profile treatment both financially and otherwise.
Different Roles in Data Science
A point to note here is that data science comprises multiple roles. Not everyone working in the field of data science is a data scientist. A data scientist is the most experienced of all others in the field. Other roles that are a part of data science are-
- Big data engineer
- Data analyst
- Machine learning engineer
- Data science manager
We shall learn about these roles a little ahead in this article. Each of these roles is important and well paid at their experience level. Needless to say, data scientists are paid the most among these, owing to their experience and expertise in the field. But others mentioned here – do not stand far behind in the league. They too are counted among the ones with the highest salary take away in their horizontal.
The annual average base pay of a data scientist in India as of April 2020 is Rs 1,015K – This is based on Glassdoor report 2020.
There sure are factors determining the salary variation in data science – one being the role factor as discussed. Other factors include location, industry type and financial situation of the company. These in fact are the common salary determining factors for most of the jobs.
With that said, data science is definitely promising and carries humongous job opportunities. All the roles for that matter that fall under its purview are paid way above average and are offered a glaring job profile.
- Offers Great Job Opportunities
As I mentioned before, there are multiple roles and opportunities available to be grabbed in the field of Data science.
Data science has multiple disciplines to work in. All of these render different outcomes, yet these are all interrelated. Let us discuss them here.
Data engineer – Once the data is collected, a data engineer works on the structuring and transforming of raw data into an easily workable format and datasets, maintaining the SOR(system of record), checking on its quality and making it properly accessible so that the data analysts can fetch it without a hurdle.
Data engineers work closely with developers, database architects, data analysts and data scientists and assist them in getting consistent architectural solutions throughout the running of the project. Data engineers are technically sound and have a creative bent of mind to approach a solution. A good understanding and application of algorithms and statistics is a must for this role. Additionally, having a flair for programming and a keen interest in machine methods is one of the other ‘must haves’ that they possess.
These skills and qualities of a data engineer may look a bit overwhelming but are highly rewarding. One can train to be a data engineer and start playing with crude data to give it a chiseled outcome.
Coming to the pay scale of data engineers, they are clearly paid high, considering the variety and quality of work they are involved in.
Data analyst – They give meaning to the numbers. In other words, data can come in different forms like sales figures, logistics, material costs, head counts and so on. A data analyst takes this data, analyzes it through calculations and graphs to produce a result that can be used by the business to make appropriate decisions.
“Information is the oil of the 21st century, and analytics is the combustion engine”.Peter Sondergaard, Gartner research
Data analyst is one of the hottest jobs in the current data driven world. This is because any business in the absence of a proper data analysis mechanism will fail to produce the efficient and profitable output. And a great payscale of the analysts stems from the very vitality of their presence in any business.
Machine learning engineer – According to Study.com, “Machine learning engineers are sophisticated programmers who develop machines and systems that can learn and apply knowledge without specific direction”.
Let us first briefly understand what Machine Learning is. In very simple words, machine learning refers to analyzing input and results to determine the underlying algorithm. I found an interesting sketch from towardsdatascience.com which shows a very subtle interpretation of machine learning by contrasting it with traditional programming. You get a better understanding of machine learning by opting for an online machine learning course.
Traditional programming Vs Machine learning
And this is what machine learning engineers do – create machines or services that do not depend on any rule to determine what action to do. Instead, they study the data trend and form an algorithm to take next action.
Machine learning is a new dimension in the realm of engineering. It sure is a smarter way to think business. In fact, instances of data science and machine learning are visible in many day-to-day activities. Few common examples are that of self-driven cars or news feeds and ads that pop up in our facebook pages. These are all custom made according to the user visits on different categories of pages.
You may not waste time thinking whether to opt for the role of machine learning engineers or not as the job description itself is intriguing and offers a great monetary package as well.
Data and analytics manager – Like any other manager, they have a substantial role to play as they lead the team of data science and make sure that a proper coordination among the different roles is made. They sure need to possess a strong technical understanding along with great communication skills.
Position of a data and analytics manager is not an easy one as they are answerable for their team performance and deliverable. But isn’t it fun to hit a challenging position if you hold the required skills? You can gauge yourself and give this role a thought as your upcoming career.
Needless to say, this is a highly paid role with a great prospect.
Data science in general has many roles to offer. I listed out the few common ones. It can be noted that the nomenclature of the same role varies with places and organizations.
One must focus on honing one’s skills on programming, mathematics, statistics, probability, analytics and databases to get started with data science learning. As we saw, it is a very promising field with our effort worth spending.
- Gives You Decision Making Power
In most of the traditional jobs, what one commonly cribs about is the fact that one doesn’t get to be at the decision making end. And this becomes a major reason for the mushrooming job dissatisfaction everywhere.
With a role in the field of data science, you can keep such negative feelings at bay. This is clearly because data science comprises the core decision making individuals and each of their roles has high weightage and credibility that can never go unnoticed.
- There is Less Competition in the Field
Though not a complete newbie, data science when compared to other traditional IT jobs, is still new and is growing at a fast pace. This growth rate has triggered a huge demand for data scientists and allied roles in the job market. But there is an alarming skill gap between the demand and supply of data science professionals as the number of these skilled people is quite low.
This creates an opportunity to learn and grab a seat in the field of data science. Less competition means a better chance to get hired. And a substantial growth rate in the field is a golden opportunity for the aspiring data science candidates.
- You Learn Diverse Skills
Any role in the area of data science demands appreciable data handling skills. This would mean having a strong hold on analytic skills, mathematics, algorithm, statistics, probability, data structure, planning, visualization, predictive modeling, programming and communication to name a few among others. All these skills can be learned and practiced through a well defined course to target data science jobs.
Learning such a vast skill set not only adds to the overall work profile, but also polishes our thought process. We start data-driven thinking and tend to take smarter decisions in various aspects of life.
- Gives You Freelancing Opportunities
Data science is basically IT based and tending to its jobs does not require either physical movement of individuals or any specific work location. All that is needed is a computer device with a good internet connectivity.
A noteworthy point is that job trend in current scenario is shifting towards lesser dependence on a single employer. With this I am coming towards the concept of freelancing, where people hire freelancers who work for a specific job for a certain duration for some project/s and get paid according to an agreed bidding amount.
Data science that holds a plethora of roles, is not untouched by the concept of freelancing. With sound knowledge and practice in data science, you can offer or opt for freelancing jobs, instead of going by the traditional mode.
Freelancing in this area is climbing the charts and promises a good prospect in the years to come.
- Offers Quick Growth
Data science application is available across the domains like in banking, healthcare, travel, retail and telecommunication. And the demand for data scientist and allied jobs is increasing rapidly due to the ever-growing and speeding data in these verticals.
A good working knowledge and experience in data science ensures a quick career growth. The learning curve in data science is steep and so is the growth curve. In terms of monetary elevation too, the growth rate is noteworthy.
- It is Flexible to Learn
Gone are the days when learning always had to be a classroom story. Online learning is in vogue now and leveraging it for data science learning is quite an option to make your learning flexible.
There are both fixed duration as well as self-paced learning courses available online at various sites. With a proper research you can zero in on one or multiple data science course/s and set the ball rolling.
- Data Scientists are a Desirable Lot
You don’t get them easily whilst you need them badly. This makes the data scientists desirable entities. You can be one too and enjoy the perks of being in demand always.
There are not many who dare to become one. If you know the essence of being a part of the data science world, you need not hesitate to take that first step towards a future that others would envy to have.
- You Add a Feather in Your Cap
Data science is comparatively new in the stream of jobs and getting an experienced and an apt candidate in this field is still not an easy bet for the employers. It therefore is an opportunistic deal to learn data science, get hired and enrich your work profile with appealing roles in it.
Be it any role of data science, it needs an amazing skill set as mentioned before. Also the nature of work is far from being a duck soup or a simple affair. The experience you gain from the job gives an exponential boost to your career and you get to stand out from mediocrities. So pull your socks up and jump into the pool of the future. The feather is waiting for your cap!
Data science is the new engine driving different industries and businesses. Zoom in and you will find yourself using data science already in some form or the other. However, looking close in the job market one can easily make out the dearth of data science professionals out there. The skill gap between the demand and supply in the data science job market is huge. The go-getters view it as a profitable affair as they can always skill up and take that technical plunge to reap the benefits of this new opportunity floating around.
Learning data science with the right tools and techniques can be a game changer for anyone looking for quality work where they can also show their visibility and credibility. The world today needs more data scientists to make an optimal use of the existing technology and more importantly to heal itself through the technical charisma of data science.