Find Your Dream Job:

The technology sphere is bursting with demand for data science roles in the market space. The Harvard Business Review ranked data science as the eminent job of the 21st century. In today’s economy, analytics and data science roles are among the most sought-after jobs.

Due to the already high number of engineering graduates in India, this phenomenon is even more prevalent. The problem arises from misaligning ideas of what you want to do with what makes sense to land a good job.

There is no doubt that a large number of engineering graduates aspire to be in this field. Increasingly, folks from other related fields such as statistics and economics are also making their way into the world of analytics and data science. 

Due to the exponential increase in demand, thousands of aspirants from unrelated fields have also taken the risk and put forth the effort to learn the pre-requisites and qualify for a data science job. 

Data Science Challenges:

Data science is still an evolving field, which poses challenges related to understanding the field and all its nuances. Here we will examine some of the major challenges people from non-technical backgrounds face and how they can overcome them. 

The first challenge is defining and understanding data science as a whole. Many people tend to believe that data science is just learning Python or R, learning a few algorithms, or creating data visualizations and dashboards. While none of these are incorrect, they are not all rounded definitions either. 

Data science is a vast field requiring multiple skills to become a “full-stack applied data scientist”, and there are few people who are able to do it perfectly. This is purely due to the evolving nature of this field, and that is fine. Plan how you want to shape your profile and gradually add skills based on the jobs you are seeking.

The second challenge is the approach and process involved in ramping up oneself to land a target role. In some cases, ads promise to turn you into a data scientist within a month. Even those who have been in the industry for over a decade still believe they know around 50-60% of the numerous learning opportunities available. 

The first step should be to get the fundamentals right. You need to be confident that you will be able to comprehend any alpha, beta, or gamma tomorrow.

There may be a few more analytics approaches, a few more variations of ML algorithms, and some more data visualizations by the time you finish reading this article, but if the foundation is strong, the hurdle to learning them as and when needed becomes easier to cross.

Therefore, choose the right learning sources and focus on strengthening the selected elements required for a specific role. 

Understanding the job roles and responsibilities is more important than going by titles and designations. As a result, you will be able to identify the roles that are best suited to you and what to aim for. 

Finding Solutions:

It is important to read case studies and understand various processes in order to become a business problem solver. Analyze the generated data, understand what outcomes could be achieved using data science, and then use it to find the right solution.

When it comes to math, you are not much different from what you learned in high school. Take a more understanding approach to your high school mathematics and statistics. There is more to data science than memorizing algorithms and formulas one can execute blindly.

Instead, focus on understanding the backend operations of statistical models and algorithms. It is important to practice identifying what to use and when to use it based on the scenario of data and insights/outcomes. Instead of executing an ideal world scenario, practice solving complex scenarios when something fails. 

Start by learning one data engineering, data science, and data visualization platform. SQL – Python – Tableau/ Power BI is the recommended combination. Practice as much as possible.

Troubleshoot complex scenarios in which a particular package needs tweaking or a complex data operation/transformation needs to be performed. Do this while remembering the basic syntaxes and packages. 

Along with technical skills, you should also build three essential soft skills. It is important to communicate technical skills or make decisions based on your findings after you have acquired them. 

Design thinking is about constructing a solution that can be consumed by the end-user, so instead of one-off data analysis and model building, you should craft end-to-end business solutions.

Don’t stop at the execution level when it comes to developing your decision-making skills. Instead, interpret the numbers, question the outputs, and solve problems for scenarios based on the outputs of the model.

Pick up learning modules across aspects mentioned in the solutions to the first two challenges to fill the gap between the roles and skills needed. Choose a title that fits your skills, expectations, and growth opportunities to become a full-stack applied data scientist.

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