Learn How To Get Your Dream Job In The Field of Data Science & Make Your Way Into It
The demand for data science roles on the market is booming in the technology sphere. The HBR rated data science as the sexiest job of the 21st century, which is no surprise.
Due to market offers of higher pay than ever before, as well as huge volumes of entry-level jobs in analytics and data science, these are today’s most desirable jobs.
As a result of the already high number of engineers in India, this phenomenon is even more prevalent.
When you align your ideas with what you want to achieve instead of what is practical to land a good job, this happens. Engineers naturally gravitate towards this field, as a great number of them graduate with a bachelor’s degree in this discipline.
A greater percentage of folks from other related fields such as statistics and economics have also entered the world of analytics and data science in recent years.
Thousands of people from unrelated fields are now willing to make the career transition and put in the effort to learn the pre-requisites to land a data science job as the demand for data scientists has increased exponentially in the last few years.
Immediately following that question is, “Can anyone become a data scientist?” and then, “How do I land a job in data science?”
In a nutshell, yes. Nevertheless, there are a number of factors to consider before one makes a decision.
Issues at Hand
Data science is extremely complex to define and understand in its entirety, which poses the first challenge.
People with improper guidance often mistake data science for learning Python or R, learning a few algorithms, or building data visualisations and dashboards. While none of these definitions are incorrect, they are not all rounded as well.
The field of data science requires a number of aspects to enable one to become what is known as a “Full stack applied data scientist”, and there are not many who can do so 100% of the time.
That is purely due to the nature of this field, which is fine. Make a plan for how you want to shape your profile and gradually add skills to the mix based on your target careers.
A second challenge is the approach and process required to land a target role. Thousands of advertisements promise to transform you into a data scientist in 6 months or, in some cases, even a month.
Those who have spent more than a decade in the industry are still convinced that they only know about 50–60% of what is available for them to learn.
The basic approach should be to get the foundations right. If it is strong enough, you will be confident in your ability to learn, understand, and pick up any alpha beta gamma material tomorrow.
Choose the right learning sources and focus on strengthening the selected elements for the target role
The third challenge is to understand the job description itself.
It’s hard to categorize and standardize this field because it’s so vast and changing so fast. Despite the fame and popularity of the title data scientist, other roles are equally important.
For instance, business analysts, ML engineers, data engineers, BI architects, and data analysts should not be ignored. Who can be considered a data scientist depends on the company, team or job role.
Knowing the job role and responsibilities is more important than titles and designations. You can then determine which roles suit you best and what should be your goal.
What are the best strategies to make the transition to a career in data science and land a dream job?
Analytics and data science are just tools for solving business problems better; they are the means by which problems can be resolved. As such, it is critical to never lose sight of that when solving a problem.
For any aspiring data scientist to work on a business problem, three technical skills must be developed: business skills, math skills, and technological skills.
In order to become an expert business problem solver, you need to read case studies and understand various processes. Consider ways to understand the generated data, to analyse the data and to determine the outcomes you could achieve from this business problem by using data science.
For the math portion, it isn’t much different from what you learned in high school. Taking a more understanding approach to your high school mathematics and statistics will help you.
Data science is not about memorizing algorithms and formulas that one can blindly apply — it’s about understanding the backend workings of statistical models and algorithms. Finally, practice identifying what to use and when, given the situation of data and insights/outcomes needed.
If something fails, you should practice solving complex scenarios rather than just executing an ideal world scenario.
Invest some time in learning one data engineering, one data science, and one data visualisation platform to start upskilling. The combination of SQL — Python — Tableau / Power BI is recommended.
Practice as often as possible. Troubleshoot complex scenarios when it is necessary to tweak a particular package or perform complex data operations or transformations. Recall the basic syntax and packages as you do this.
There are three major soft skills you need to develop, in addition to your technical skills. In order to succeed with technical skills, you must be able to communicate them and make decisions based on them.
When learning design thinking, you should remember that you’re designing for end-users who will consume the solution, not just for individual analyses of data or one-off tasks.
Don’t stop at execution when you’re developing decision-making abilities. Rather, interpret the numbers, question the model outputs, and solve problems for scenarios based on the model outputs
As a soft skill, communication is crucial, and while it isn’t difficult to learn, it is important to upskill yourself to become better at it. Practicing is the first step. Practice giving presentations, explaining complex algorithms to a business audience, creating readouts for the technical audience, and giving mock interviews.
Choose the roles and skills to target, and then pick up modules across the aspects mentioned in the first two challenges to fill the gap. ”