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Can a B.Com. Graduate Become A Data Scientist?

What to do after graduation is a worry for most students.

Gone are the days when people used to grab a job with a mere degree. But that’s not the case anymore. You need to upgrade now and then and stay afloat with the current requirements to keep yourself relevant in the market.

This article helps B.Com. graduates earn a degree and find a high-paying job by focusing on Data Science, a course that offers better job prospects after completing training.

Yes, a BCom graduate can become a data scientist, and that’s what we will learn in this article. Stay tuned to the last line to understand the process and start your career journey.

An Overview of the B.Com. Course

The Bachelor of Commerce is an undergraduate, three-year academic program. It helps students understand the complexities of commerce and business.

With a B.Com, students study business-relevant topics such as accounting, economics, finance, human resources, marketing, and management.

As a student, you can benefit by taking up internships, practical training, and projects in real-world scenarios, which help the candidate understand the topics in detail.

B.Com. offers specializations in finance, accounting, marketing, and international business. After completing B.Com., you can either find a job in the relevant fields or continue educating with postgraduate studies like a Master’s or MBA.

Skills Required to Become a Data Scientist

Commerce graduates fall into the non-technical segment. So, let’s divide the skills into technical and non-technical. Let’s start with the non-technical skills first:

1. Non-technical Skills

a. Excellent communication skills

A data scientist extracts, understands, and analyzes data, presenting findings clearly and concisely to help non-technical audiences understand the outcome effectively. Thus, you will be required to have excellent communication skills in order to become a data scientist.

b. Solid business understanding

A data scientist is essential for enhancing business understanding, identifying challenges, and resolving trade issues to benefit organizations, identify gaps, and explore opportunities. Without having enough business understanding, you will not be able to work on the given projects.

c. Outstanding Data Insights

Data scientists develop this skill through experience and practice, focusing on perceptive information for organizations. As a beginner, you should try to read more about such insights from tutorials or case studies provided by others.

d. Analytical thinking

It involves post-morteming the findings by scrutinizing them, analyzing them, and deducing the results. You will need to work on a number of analytical problems to build such thinking over a period of time.

e. Critical thinking

Critical thinking helps with data evaluation and analysis to determine specific conclusions. Similar to the above skill, you can work on a number of critical thinking problems available on various websites like Geeksforgeeks.

f. Creative thinking

A data scientist must think creatively to spawn new ideas and unusual answers. There is no single process to generate creative thinking abilities but you can read more about how other brands or businesses are using creative thinking to solve business problems.

g. Strong Decision-Making skills

Selecting the best action plan requires strong decision-making skills in data science. This skill takes years to build but you can start testing your decision-making ability in sandboxes or with projects hosted locally.

h. Problem-Solving skills

A data scientist must stay optimal and relentlessly work to solve problems by finding or developing solutions. You will need to work on various problems related to different segments in order to develop this skill over a period of time.

2. Technical skills

a. Expertise with Microsoft Excel

As a B.Com graduate, you might have already used Microsoft Excel for various tasks. However, using it for professional work is a bit different and requires training. At Present Slide, we have published a list of websites where you can start learning Microsoft Excel.

b. Programming

Programming is one of the most important skills required to become a Data Scientist. Python as a programming language stands at the top of the list for data science roles.

If given proper time and focus, you will be able to learn Python in 3 to 4 months. Apart from Python, you will also need to learn a number of other programming languages like SQL, JavaScript, Julia, and more.

💡Also Read: Websites to Learn Microsoft Excel

c. Algorithms and Data Structures

Algorithms and data structures are computer science concepts that reinforce data storage, restoration, and computational problems. To become a data scientist, you will need to have a strong understanding of Algorithms and Data Structures.

d. Managing databases

Data Science is all about Data, and a lot of it. To work on data, you will have to learn to arrange, save, and access data from various databases.

e. Linear Algebra and Multivariate Calculus

The above are advanced mathematical concepts for a data scientist to use in data analysis and machine learning.

f. Web Scraping

Extracting data via automation using various tools is called web scraping. It is an important process for you to understand and learn.

g. Extracting, Transforming, and Loading Data

In this step, the data scientist collects data, refines it, and prepares it for further analysis.

h. Expert at dealing with Unstructured Data

As a Data scientist, you will need to learn how to handle unstructured raw data to present it effectively to marketing and senior teams.

i. Probability and Statistics

These mathematical tools will help you as a data scientist to make the appropriate decisions by arranging the data in the proper sequence.

j. SAS and Other Analytical Tools knowledge

A data scientist has to undergo training in data analytical tools such as SAS, Hadoop, Spark, Hive, Pig, R, etc.

k. Model Deployment

Data scientists require expertise in model deployment for production environments.

l. Cloud Computing

Cloud computing involves using remote computers to store, manage, and handle data and applications online. You will need to have working knowledge of major cloud computing technologies.

m. DevOps

DevOps is an approach to software development that prioritizes collaboration and communication between the development and operations teams.

n. Neural Networks

A data scientist should know numerous neural network architectures and frameworks and be able to develop, train, and optimize neural networks for diverse use cases.

o. Business Intelligence

Business intelligence involves gathering knowledge and directing company choices by utilizing tools and strategies for data analysis.

p. ML with AI and DL with NLP

Deep learning and NLP aim to process and comprehend human language using neural networks, a central goal of machine learning and AI.

💡Protip: You can join a training program on the basic and most important skills first to join as an intern or a Junior role. Later, you can learn other important skills as per the requirements to keep improving and growing.

How Can a B.Com. Graduate Become a Data Scientist – Process

Most people believe in myths, but the digital age has shattered most of them. One myth is that a B.Com. graduate cannot become a data scientist.

The relevant education and learnings a BCom graduate undergoes work in favor of learning data science. The strong foundation in business and economics from the commerce lessons turned out positively for these students’ desire to pursue data science.

An individual understands all the aspects of business analysis, making strategic decisions, problem-solving, critical thinking, and communication while undergoing a B.Com. degree. These skills ultimately pave the path for commerce graduates to become data scientists.

The non-technical background of commerce graduates may be a hindrance to the journey. However, the winning candidate can easily overcome these petty issues with willpower and extra effort. Those who take their goals seriously can achieve anything.

Tips For BCom Graduates to Become a Data Scientist

As mentioned earlier, commerce background graduates must work hard to become data scientists. The technical part that involves programming and learning new tools requires an individual to sit for hours in front of the computer. It requires patience to understand the concepts and repetitive practice to overcome the learning challenges a programming language offers.

Let’s understand how a BCom graduate can become a data scientist.

1. Learn programming languages

R, Python, and SAS are the primary programming languages used in data research. Learning these languages enables you to clean, exploit, and analyze data.

2. Learn to use databases

Playing around with databases is a large part of data research. Learn data extraction techniques for SQL or Hadoop datasets. You can get more at ease with databases by thoroughly practicing the CRUD (Create, Read, Update, and Delete) operations. Thorough practice on websites like Geekforgeeks, Coderbyte, HackerRank, and HackerRank helps to get well-acquainted with database concepts.

3. Learn Tableau

Tableau is one of the instruments. It helps to visualize data remarkably well. Other tools exist, but Tableau is the most widely used. Tableau is one of the tools in high demand in most training facilities. As a result, if you run into any problems while learning, using this tool to seek assistance and direction is simple.

5. Statistics

The study of statistics is infrequent. As a result, one must diligently learn it. Basic statistical understanding is necessary for data science. Search for websites that provide statistical training. Pay close attention to the following ideas to aid you in your career as a data scientist:

  1. Chi-Square Test
  2. Dispersion of data, Bell curve
  3. Hypothesis Testing
  4. Mean, variance, and standard deviation
  5. Measures of central tendency (mean, median, and mode)
  6. Models and algorithms
  7. Percentiles of Data Distribution (First Quartile, Second Quartile, Third Quartile)
  8. The descriptive method and the inferential method

6. Models and Algorithms

Models and algorithms must be well-understood by a data scientist.

A data scientist must understand the models and algorithms for classification, clustering, regression, etc.

K-means, linear regression, logistic regression, and SVM are a few examples.


Data scientist’s work involves relentless efforts and consistent monitoring of data. Aspiring students must plunge into learning these skills to become efficient data scientists. One way to get acquainted with data science concepts is to self-study.

The more you self-study, the more challenges you may face. Finding answers to these challenges will make you an expert data scientist one day.


A B.Com. graduate can become a data scientist. The learnings concerning commercial aspects of business, economics, finance, etc., make a base-level foundation for learning data science.

Other soft skills, such as communication, problem-solving, and critical and creative thinking, are also part of the curriculum for a commerce graduate.

The hardest part may be learning programming languages.

However, with the right goal setting and a forceful willingness to learn the programming techniques, nothing can stop the right candidate. Hopefully, BCom graduates aspiring to become data scientists can benefit from this article.

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