Bridging the Skills Gap: Why You Need to Study Data Science After Graduation

Why You Need to Study Data Science

After graduation, a career in data science ensures a future-proof lifestyle for people with hard skills (data analysis, machine learning, statistical modelling, etc.). It does so by making you stand out in a data-driven job market and giving you opportunities for growth and innovation.

A post-graduation degree is not an essential element when considering pursuing a career in data science. But it helps. The degree makes you more capable of getting your job done and earns you a higher salary. How? You may ask.

With the ongoing unlimited data transfer protocols, it is no surprise that companies will look for as many data scientists as possible. Businesses, oil companies, healthcare, and the government massively rely on data wrangling and rearrangement.

Obtaining a Degree

Data scientists usually have at least a bachelor’s degree. Most people kick start their careers by first getting a bachelor’s degree in data science. Other relevant fields are computer science, statistics, and mathematics. You can land a decent-paying job with these credentials. But if you think you deserve a higher payment for your skills, you may need to think about doing a master’s in this field.

There are two ways to get a postgraduate degree: the traditional way and the digital way. Many people nowadays don’t have the time, energy, or money for a formal education. They are busy handling other life issues like raising children, keeping a job, and looking after a family member—you name it—which is why many turn to online data science master’s degrees.

Problem-Solving Skills

Problem-solving skills are fundamental in data science. A data scientist has to approach problems by understanding existing resources.

Intellectual curiosity is necessary if someone wants to work as a data scientist. Because constantly asking the “whys” is essential to finding out the underlying data. Data scientists often work with organisations, so they must also have a strong business sense. This sense helps them deeply comprehend company needs and translate data accordingly.

A master’s degree can effectively teach students how to get a proper outcome through knowledge (appropriate application of maths and statistics) and technology (analytical platforms, AI). Soft skills like communication, teamwork, adaptability, and data visualisation are also important to being a successful data scientist.

It Looks Good on Your Resume

Since it’s a high-paying field, thousands of candidates apply for positions daily. Naturally, there is a lot of competition. And in this situation, standing out from the crowd requires a master’s degree. The master’s program will be a sign of your in-depth knowledge in the field of data.

An advanced degree in data science will most definitely get the attention of recruiters. Having a master’s degree on your resume will increase your chances of getting an interview and, eventually, landing your desired job. A master’s degree also earns you a higher salary because it is highly valuable. Don’t think of the degree as just another credit because it is more than that. It shows not only your skills but also your commitment.

Endless Job Opportunities

Roles like machine learning engineer and data analyst are among the highest-ranking jobs on LinkedIn and Glassdoor. Here’s how the job market looks.

Machine Learning Specialist: A machine learning specialist has all the tools to solve complex problems. They know more about language processing, fraud detection, and computer vision than your average Joe. They are the designers of algorithms that teach computers to learn from data. A machine learning engineer’s main job is to collect and process data.

Analytics Specialist: An analytics specialist is a data scientist in charge of tasks like data analysis and mining. They visualise data and present it to stakeholders. Analytical specialists usually have a bachelor’s degree in statistics, mathematics, computer science, or related analytical fields. They are excellent at statistical analysis and statistical software like R, SPSS, and SQL querying. They are also experts in data visualisation tools like Tableau.

NLP Specialist: A natural language processing specialist works with human language and text. These experts heavily use programming tools like Python, SQL, and NLP frameworks. NLP specialists take on tasks that require neural networks. Text or sentiment analysis are used to process and understand language data.

Business Intelligence Analyst: B.I. analysts are essential company assets that work with stakeholders, IT departments, data scientists, and other analysts. They make sure data consistency is maintained. The role of business intelligence will offer you tons of career opportunities. There is a growing demand for skilled BI analysts across various industries (finance, healthcare, retail, insurance, technology, e-commerce, etc.). This leads to competitive salaries. They also provide insights on best practices and brainstorm new ideas.

Entrepreneurship

The future of entrepreneurship through data science is promising. Self-made business owners can make huge profits by having a data science background. By seeing data science through an entrepreneurial lens, you can unlock new business ideas, drive innovation, and contribute to the shifting landscape of data-driven entrepreneurship.

Business owners with a background in data science can better understand market trends and pitch better ideas.

Data scientists can pursue various paths, such as developing deep tech innovations or integrating data science as an additional feature.