Data Science is still in its early stages. The more statistics you look at, the more you see how jobs are only increasing in number. This is the natural progression for the domain in a world where data is pretty much everywhere. As the data increases, there is a requirement to leverage insights from it. This is where data science comes in.
Data science’s increasing scope has caused some mismatch between the demand and availability of the most in-demand Data Science skills, from Python and R expertise to software suites like Tableau, to knowledge areas in AI, mathematics and social sciences. This makes the domain a bit difficult to enter.
Data Science on the Rise
Dubbed as the sexiest job of the 21st century for years, the question of whether Data Science is still in demand or was it a passing fad often comes up. But data is everywhere. Emails are sent rampantly, even more so during the work-from-home age. Smart speakers and other Internet of Things (IoT) devices dominate how people live in an urban setting. Youtube is not just a video sharing platform but almost a fully free university now. Each of the Big Tech four enjoy an abundance of users and data being created and processed. You need someone to make sense of all of that: enter data scientists.
Regardless of job openings or hiring trends, there will be a demand for Data Scientists going forward. The age of information requires and creates data in an endless spiral. The Data Science domain is here to stay.
The Data Science Demand: Demystified
It is hard to describe what exactly is demanded from a data scientist. The work surely differs depending on the nature of the enterprise. A large-scale company leverages their data teams very differently from how a startup does. In this article, we will go over some popular jobs in the data science domain and understand the job descriptions as well to demystify the field.
A common misconception is that a Data Science professional is meant to do everything on their own. That is a fallacy in some ways because as with everything in the industry, Data Science has its own nitty-gritty specializations.
The Flipside of The Mismatch
Like most fields, Data Science is now an amalgamation of different roles. In other words, the demand for data professionals has increased because the need for different kinds of expertise has increased. As more specific data roles turn up regularly, the wider and more lucrative a career in Data Science becomes. The Data Science demand is here to stay.
Fields of Fields and Respective Job Roles
In the course of this article, we will discuss the different roles available in Data Science as well as the most in-demand Data Science skills to excel at these roles if you want to learn Data Science with Python or R.
Let’s start with roles which deal with the "data" part of Data Science.
Data Engineering is the part that often gets overlooked but perhaps is what holds everything together. In a nutshell, data engineering is about the plumbing of it all. It is about answering what flows, from where, how far, and how fast. The work is mainly about supplying data and managing delivery of data at scale. The latter is the tough part of it all where you'll need to work with fixing connections between different service or providers, making sure the data flows properly between applications and so on. This part of the work rarely has any working with data. You're usually only interested in the correct flow of it so others may work with it.
Skills To Have
Most people in this role are skilled at programming but also understand SQL. They invariably need some skill in different cloud providers like Amazon Web Services, Microsoft Azure or Google Cloud. Also, open-source alternatives like Airflow, Kafka and distributed technologies like Hadoop are the crux of this side of things.
This is the part of the spectrum which is the most common description of Data Science. If you've ever heard that "Data Scientists crunch numbers", it was probably referring to this side of the whole deal. Data Analysis is the task of finding patterns in data using different tools and adding in domain knowledge to make sense of those insights. It is not always about the numbers though as data comes in various forms such as textual, multimedia, and so on. Depending on the company or job, an analyst may work with a plethora of public and private datasets while the product counts on them to find some actionable insights or patterns. A pattern is often useless if it does not corroborate with some believable story in terms of the domain or industry, however. Therefore, with data analysis, there is also a need for data storytelling.
Skills To Have
Data Analysis is really not about what tool you use or how you conduct analysis. The skill lies in intuition and understanding of the data and trends. This can be done on Microsoft Excel, using SQL, using languages like R or Python, or even on platforms like Tableau.
Storytelling aims to take the patterns and understands the story behind them. This often involves talking to different stakeholders and teams and understanding why a pattern is apparent. Lots of visualization happens as more stakeholders join. The key here is that often the stakeholders are not from a purely technical background and to make everything easier for them, the story should be solid, and the plots should be clear. Data Storytelling and Visualization is often clubbed within Analysis but can be an expertise of its own.
Skills To Have
Since this is only an extension of analysis, dashboarding and storytelling can again be done using either Microsoft Excel, making decks and presentations, using languages like R or Python, or again, platforms like Tableau.
Machine Learning / Artificial Intelligence
Machine Learning which is commonly attributed as Artificial Intelligence (which is not exactly true but out of the scope of this article) is the idea of using the underlying patterns in a data and letting an algorithm understand them to solve some real-world problems. In other words, learn from the data, execute on new data. Machine Learning has two different subsections.
In a practical setting, you are looking at Applied Machine Learning Engineers. Unlike popular belief, machine learning engineers don't really need to understand all algorithms and methods in-depth. An overview of understanding is enough. The true quality of a machine learning engineer lies in their execution of bringing the real world and the theoretical algorithms together.
This side of Data Science deals with understanding algorithms, feeding data into them, using them, customizing them and then serving the results. The requirement here is a broad knowledge of different algorithms and use-cases, their common pain points and drawbacks and of course, fantastic programming and wrangling skills.
Skills To Have
An ML Engineer often needs a great command over languages like Python or R as well as some understanding of software engineering, cloud environments. Knowledge about building APIs and serving results comes in handy as well since a lot of what ML Engineers make is integrated directly into the larger products and software. It helps to be both data- and tech-savvy.
Data Science Research
This is where the other half of the Machine Learning argument lies. Data Science Research is about building the algorithms which the Applied Machine Learning Engineer uses. Research is about finding novel ways to look at data. This is a statistics and mathematics heavy role and demands unmatched understanding and enthusiasm for what works and what came before. Research is all about taking what came before and improving over it. Research is more commonly about solving what needs to be solved.
Skills To Have
Unlike Applied Machine Learning, Data Science Research requires more theoretical expertise in mathematics and statistics, as well as programming languages like Python and R.
Data Science Leadership / Decision Science
While implementation is a huge part in Data Science, the most important part is asking the right questions. For that, you need a Data Science leader or manager. This role is essentially for people who are not only involved in Data Science but have a knack for product, design thinking and related areas to know how to define a problem. Data Science Leadership revolves around asking questions about the questions being asked from the data. A good leader focuses equally on the problem definition and implementation. They often also act as a bridge between the other teams and the data team.
Skills To Have
To be a good fit for this side of things, an overall understanding of algorithms and some understanding of the statistics and mathematics behind them is required. Along with that, familiarity with game theory, social science, design thinking and other related fields is a great edge to have. Else, you would have to rely on a specific domain expert or social scientist to help make sense of the what is what.
Wrapping Up: Is Data Science in Demand?
Absolutely! Data Science is still a fantastic career to opt for. It is in demand regardless of where you start from, what your initial skill set is or what you Excel at. There’s some role for everyone. The question then comes to what is required for a career in data science.
That is a tougher thing to answer. Based on the organization, the domain, the unique needs of the product, a data scientist may be a combination of any of the above. They may also be skilled in one or two of the above, while being able to wear any of the other hats when required. As responsibilities in the data landscape increase, the understanding of what a Data Scientist does also changed considerably.
In a startup strapped for cash, a data team might consist of two people handling everything from data engineering to problem definition to analysis to visualization to machine learning. In a large-scale MNC, each of these roles may be defined and given to one individual each. In fact, the roles may change depending on the phases of a project as well. You need more Data Engineers in the beginning of a project but more Analysts towards the end of it. In the same way, when solving a problem, you need more Machine Learning Engineers, but once the problem is solved, only a couple of them are required for maintenance and update.
To conclude, Data Scientist is not a term that has a strict definition, which makes the demand for data scientists ever-present but obscures any clarity on what is exactly required. If looking for someone to add to your team, understanding the requirements and expectations before you hire someone can help you hire the best individual you need. If looking for a job yourself, you must be clear on which side you enjoy, what tools you're comfortable with or willing to learn, and which part in the Data Science process intrigues you.
All in all, the Data Science demand should only increase going forward as new roles, new specializations and new requirements come to light.
Frequently Asked Questions(FAQs)
Is data science still in demand 2022?
Yes! The avenues to get a career in Data Science have only increased which has made sure of the Data Science demand. From Data Analysis, to Machine Learning Engineers, to Data Decision Makers, the choices are endless.
Which degree is best for data scientist?
Data Science is a multi-faceted domain which means you don’t exactly require a specific degree. In fact, many people in the industry are self-taught or took their learning from online courses and bootcamps. KnowledgeHut offers some of the best Data Science courses in India, with general courses as well certifications available in specific skills.
Which stream is best for data scientist?
Since “Data Scientist” is an umbrella term for multiple roles, the stream is not as relevant. In fact, your role can have a heavy influence from statistics, mathematics, and even social science, depending on which part of the Data Science spectrum suits you best. Regardless of what stream you come from, there is a possible career in Data Science for you. All you have to do is start learning!