Disclaimer: This post may contain affiliate links, meaning we get a small commission if you make a purchase through our links, at no cost to you. For more information, please visit our Disclaimer Page.
Data science is a quickly growing field, and it may be tempting for bachelor of commerce graduates to think they could make a simple transition from analyzing the financials of large companies to analyzing the large amounts of data that make up today’s digital world. So the question is, can a BCOM graduate excel at data science?
Thanks to their mathematical, accounting, and statistical background, a BCOM graduate is an excellent candidate for data science. A BCOM graduate is already skilled in more than half of the skills needed. A few extra courses and data science boot camps will equip them with the other required skills.
Table of Contents
Being a BCOM degree holder doesn’t give you a direct pass to a successful career in data science, but it doesn’t stop you either. That’s why, in this article, you’ll learn how being a commerce graduate gives you an advantage when pursuing data science and how you can transcend into being a data scientist with your commerce degree.
Companies no longer rely on ‘gut-feel to make business decisions, but rather the focus is shifting to getting real solutions using a data-driven approach.
Therefore, anyone with an interest in numbers and their analysis stands out will be a good fit. A BCOM graduate has a deep statistical understanding and quantitative skills and therefore at an advantage when it comes to data analysis.
There are many advantages of being a BCOM graduate pursuing data science.
For example, a company would rather have someone who knows the ins and outs of data mining, collection, and analysis like a BCOM graduate handle their data than someone who doesn’t. More advantages of having a BCOM degree in data science include:
You have solid finance, accounting, and statistical background, which are invaluable to data science – Data scientists manipulate all kinds of data in a business setting to come up with solutions that push the business forward.
As a commerce graduate, these skills make it easy for you to understand business operations and how you can help improve the company’s financial health.
You can identify correlations and inconsistencies that others may not see – Having dealt broadly with numbers, you develop excellent quantitative skills, which help you notice tiny errors in the data that others may miss.
You already know how to explain data with simplicity and write coherent sentences – This is a skill most technically oriented people lack while it’s among the most critical skills required in data science. As a BCOM graduate cum data scientist, you can clearly explain your findings to the non-technical managers and marketers and get solutions implemented.
You’ll find it easy to know machine learning as you’re already familiar with it – Machine learning involves most statistical topics learned in your BCOM course. Some modules, like logistic regression and linear regression, form part of what machine learning entails, and these are familiar topics for a BCOM graduate.
Some programs learned in commerce overlap with those in data science, making it easy to learn data science. For example, Excel is a data analysis program learned in both disciplines, so the knowledge of Excel acquired when learning commerce is advantageous when learning data science.
Having an independent degree in commerce and another in data science will increase your chances of securing a job. Employers are looking for strong analytical skills for excellent data analysis, and such skills are gained in a commerce classroom. Combining these skills with data science technical skills puts you far ahead of your competitors.
You get to make more money – Let’s face it, most people upgrade or change their career paths in a bid to make some extra coins.
As a data scientist, you position yourself to make more than twice what a BCOM graduate would make on the job. Better still, you can get a better-paying job with two degrees.
BCOM equips its graduate with the most needed statistical and analytical skills, which are excellent for data mining and its analysis. Not to mention the soft/human skills that make all the difference. These advantages and more make data science a good career advancement option for a BCOM graduate.
Data science comes with the attractive career benefits anyone would want, including job security, a dynamic work environment, and high pay.
Even though it’s perceived as a technical course and unfit for commerce graduates, the unique blend of skills taught in commerce comes as a massive advantage to the field of data science.
If you want to upgrade your career and pursue data science while holding a BCOM degree, here’s how you can go about it.
Step 1 – Pursue a course online or at a boot camp.
Technology has made brought education nearer through online platforms. Find a reputable institution online and apply for the course. A good one may cost hundreds or thousands of dollars, but it can be worthwhile.
Some platforms like Udacity offer some free courses in this direction, which can help you learn basic concepts in exploring and describing data, computing simple probabilities, using statistical research methods, and the basics of programming software tools like Python.
A boot camp may offer employability options once you’ve completed it, but be sure to check reviews for them. However, this route is one of the more expensive options.
You can also learn off of Youtube, however sometimes the courses offered may not be up to date or structured well.
Step 2 – Complete the course and graduate.
It’s not enough to get the knowledge without the certificate. Someone with a degree and an accompanying certificate stands a better chance of getting hired than one without it.
You can add the finished certificate or courses on LinkedIn to share with potential employers.
Also, the temptation to quit the course can rise, especially if it’s taken online.
Most online learning platforms like Coursera and Udemy offer globally recognized courses and accompany them with acceptable certificates after completion. Therefore, ensure that you complete the course and get your certificate.
Step 3 – Learn different programming languages (beyond the course).
You’ll learn the basics of programming languages during the course, but beyond that, you’ll need to self-educate on other languages useful in data science. Most of these languages, like R and Python are free and available for use.
There are tutorials online which you can use as learning and reference materials until you can use the programs seamlessly.
You can try other free sites like Datacamp to learn programming languages, especially if you are learning from scratch. It’ll also give you a chance to handle real data projects for the experience.
Step 4 – Find projects to do and hone your skills
Now you have already acquired programming skills, analytical skills, and a certificate to show for it, it’s time to practice what you’ve learned.
You can find data sets online that you can use to practice and involve yourself in data-related surveys and competitions.
With time, you’ll master how to transform and manipulate data without anyone guiding you. That means you’re ready for a real job in a natural business setting.
Step 5 – Apply for jobs in the real market as you continue to polish your skills.
There’s no end to learning; find more courses in the same line and do them to keep learning. While at it, polish your resume and send it out to prospective employers.
There’s a high demand for data scientists, and therefore, with your impressive resume, you should get hired in no time as long as you have the experience gained through projects and certifications up to date.
Keep your options open as data science offers a wide variety of jobs that you can choose from.
If the idea of data science fancies you, why not go ahead and do it? It’ll require a lot of hard work, commitment, discipline, and the will to succeed to get it all going.
Most importantly, indulge in constant self-education, especially with the programming languages, to stay updated on the emerging trends in data science.