Breaking into Product Analytics from a non-technical background often feels intimidating. Many students from commerce or management streams believe product analytics is reserved for engineers, coders, or people who are exceptionally good at math. But real-world career journeys often tell a very different story.
This blog captures the complete journey of a professional who transitioned from a BCom and MBA background into a Product Analyst role, without a traditional tech foundation, heavy coding, or advanced mathematics. It highlights how curiosity, structured learning, and hands-on experimentation with data can unlock high-growth analytics roles—even in competitive tech-driven companies.
Starting Point: A Non-Technical Background
Coming from a BCom background, followed by an MBA, the initial career path was not data-centric. Early roles focused on operations, where decision-making was fast-paced and execution-driven rather than analytical.
Like many commerce graduates, formal education did not include practical training in tools such as Excel, SQL, or analytics platforms. Data was present, but it was not yet the core focus of the role.
The first meaningful interaction with data came during an operations role at Cult.fit, where basic Excel was used for tasks like rostering trainers and managing schedules. Even though Excel was unfamiliar, it sparked curiosity—especially around formulas and how data could simplify real operational problems.
This curiosity became the foundation for everything that followed.
Exposure to Data in Real Business Roles
The next phase involved moving into a product marketing role at an e-commerce B2B company. This role introduced structured data usage beyond Excel, including SQL.
Initially, SQL felt overwhelming. Coming from a non-tech background, concepts like tables, queries, and joins seemed complex and intimidating. However, instead of avoiding data, there was a conscious effort to observe, learn, and ask questions, especially by watching how analysts worked.
Even without formal training, continuous exposure helped demystify data. Over time, it became clear that data exists in every business function—operations, marketing, product, finance—and learning to work with it creates a massive career advantage.
The Turning Point: Learning SQL From Scratch
The real transformation happened at a 0-to-1 EdTech startup, where the team was small and data was flowing in rapidly—but there was no dedicated analyst.
Engineers could access the data, but lacked time to analyze it deeply. This created an opportunity.
With help from a colleague, the first SQL query was written. Instead of memorizing it blindly, there was a conscious decision to understand every single line of the query, no matter how long it took.
For over a week, learning happened through:
- YouTube tutorials
- Online articles
- Hands-on experimentation
- Understanding what tables are and how data flows
Over the next 3–4 months, SQL skills improved dramatically. Queries turned into dashboards, dashboards turned into insights, and insights started influencing business decisions.
Founders noticed the impact. This validation reinforced confidence and confirmed that analytics was not just learnable—it was enjoyable.
From Analyst Work to Product Analyst Role
After brief transitions through acquisitions and fintech roles, the journey culminated in joining FinBox, a B2B fintech company, as a Product Analyst.
Here, analytics became more structured:
- Writing complex SQL queries
- Using BI tools like Redash
- Tracking product metrics
- Supporting decision-making with data
Eventually, this journey came full circle, returning to Cult.fit, but this time as a Product Analyst, not in operations.
This return symbolized growth—not just in title, but in skills, confidence, and impact.
Do You Need to Be Good at Math?
One of the biggest fears for aspiring analysts is mathematics.
The reality?
You do not need to memorize formulas or be exceptionally good at math.
What truly matters is:
- Understanding concepts, not calculations
- Knowing what metrics mean
- Interpreting results correctly
Concepts like correlation, regression, averages, and trends are important—but tools like Excel, SQL, and BI platforms already handle the math.
Analytics is more about thinking logically and interpreting outcomes than solving equations manually.
Is Coding Mandatory for Product Analysts?
Coding is often misunderstood.
Product analytics does not require hardcore programming. SQL, the most-used tool, reads almost like English. Even experienced professionals do not memorize every syntax.
Key truths about coding:
- Logic matters more than syntax
- Google and AI tools are always available
- Understanding what you want from data is more important than how fast you code it
Most analysts start with Excel, move to SQL, and only later explore Python if required.
Using AI Without Losing Your Edge
AI tools like ChatGPT have changed how analytics work—but they haven’t replaced analysts.
AI works best as a co-pilot, not a replacement.
Product analysts still need to:
- Ask the right questions
- Validate outputs
- Interpret data in business context
- Tell stories using insights
Those who understand fundamentals can use AI to become faster and more effective. Those who rely blindly on AI risk losing relevance.
The rule is simple:
Build skills first, then use AI to amplify them.
Tools Commonly Used by Product Analysts
In real-world product analytics roles, the most-used tools include:
- SQL – Primary tool for querying and extracting data
- Excel / Google Sheets – Quick analysis, reporting, and logic building
- BI Tools – Metabase, Redash, Power BI, Tableau
- Python (Optional) – Advanced analysis or automation
- JavaScript / App Scripts (Occasional) – Connecting data sources
Mastering just SQL and Excel already puts you ahead of most candidates.
What Hiring Managers Look For in Product Analysts
Beyond tools, hiring decisions focus heavily on mindset and problem-solving ability.
Key traits evaluated:
- Strong SQL fundamentals
- Curiosity and analytical thinking
- Ability to define the problem before jumping to solutions
- Clear communication
- Openness to feedback and learning
Being “perfect” technically matters less than being trainable and thoughtful.
Advice for Non-Tech and Commerce Students
For those starting out or switching careers:
- Start with basics, not advanced topics
- Learn in a structured way
- Practice in your current role
- Use real company data wherever possible
- Focus on understanding, not memorization
Even creating simple Excel dashboards or reports can be the first step toward an analytics role.
Confidence grows with application—not certificates.
Conclusion
The journey from BCom to Product Analyst proves that analytics is not limited to engineers or coders. With curiosity, consistency, and structured learning, anyone can transition into high-impact, high-paying analytics roles.
Product analytics rewards those who:
- Ask better questions
- Understand business problems
- Learn continuously
- Apply data thoughtfully
For anyone willing to start from the basics and stay curious, analytics is not just accessible—it’s transformative.
Frequently Asked Questions (FAQs)
1. Can someone from a BCom or non-technical background become a Product Analyst?
Yes. A non-technical background like BCom or MBA does not limit your chances of becoming a Product Analyst. Many successful analysts start from commerce, operations, or marketing roles and transition by learning data tools, understanding business problems, and applying analytics in real-world scenarios.
2. Do I need to know advanced coding to become a Product Analyst?
No. Product analytics does not require hardcore programming. Most Product Analysts primarily use SQL and Excel. SQL is easy to learn and reads almost like English. Advanced coding languages like Python are optional and role-dependent.
3. Is strong mathematics required for a career in product analytics?
You do not need to be exceptionally good at math. What matters is understanding analytical concepts such as averages, trends, correlation, and interpretation of results. Tools like Excel, SQL, and BI platforms handle calculations automatically.
4. How can I start learning analytics with no prior experience?
Start with the basics:
- Learn Excel fundamentals
- Understand how data is stored in tables
- Move gradually to SQL
- Practice using real data from your current role
Structured learning combined with hands-on practice is the most effective approach.
5. How long does it take to transition into a Product Analyst role?
The transition timeline varies, but with consistent effort, many professionals see significant progress within 3 to 6 months. Real growth comes from applying what you learn, not just completing courses.
6. What tools are most important for Product Analysts?
The most commonly used tools are:
- SQL for querying data
- Excel / Google Sheets for analysis and reporting
- BI tools like Metabase, Power BI, Tableau, or Redash
Learning SQL and Excel alone can make you job-ready for many entry-level roles.
7. Can I become a Product Analyst by transitioning within my current company?
Yes. Internal transitions are often easier. Look for opportunities to use data in your existing role—such as creating reports, dashboards, or insights. Proving value internally reduces risk for employers and increases your chances of role transition.
8. Will AI tools like ChatGPT replace Product Analysts?
No. AI acts as a co-pilot, not a replacement. While AI can generate queries or summaries, Product Analysts are still needed to ask the right questions, validate insights, interpret results, and make business decisions.
9. What do hiring managers look for in Product Analyst candidates?
Hiring managers value:
- Strong SQL fundamentals
- Curiosity and problem-solving mindset
- Clear communication skills
- Ability to define problems before solving them
Being trainable and thoughtful often matters more than knowing every advanced concept.
10. What is the biggest mistake beginners make while learning analytics?
The biggest mistake is jumping into advanced topics too quickly without mastering fundamentals. Skipping basics creates confusion and slows progress. Building a strong foundation and practicing consistently leads to long-term success.
