Nandini Athwani, a learner from the WsCube Tech Data Analytics Course, transitioned from a pharmacy background to a career in data analytics, landing her first role as an Operations Analyst at Stockverse, a US-based stock trading company, just months after completing the program. In this in-depth interview, she shares her career switch, learning routine, projects, interview experience, the tools that helped her get hired, how she uses AI in her day-to-day work, and her advice for freshers starting a career in Data Analytics. Key Highlights of the Interview In this interview, Nandini shared her journey related to: Career switch: Biology to Pharmacy to Data Analytics to Operations Analyst First interview, first offer: This was Nandini’s first-ever interview, and she got selected. Role: Operations Analyst supporting a 20-person sales team at a US stock trading firm. Core tools: Advanced Excel, SQL, Power BI, Python, and AI. Daily work: Mostly data cleaning, organizing large sales/enrollment/payment datasets, and building Power BI dashboards. Interview focus: Excel case tasks, basic Power BI questions, and strong communication. AI at work: Uses AI to generate query logic, understand APIs, and speed up dashboard work. Advice: Be consistent, focus on projects, and don’t fear starting from zero. Watch the Full Interview What does Nandini’s journey from Pharmacy to Data Analytics really look like? Watch her full interview to find out.
Student Highlight Attribute Details Name Nandini Athwani Course Data Analysis Course Background Biology student (switched to Pharmacy) Current Role Operations Analyst Company Stockverse (US-based stock trading company) Key Tools Advanced Excel, SQL, Power BI, Python, AI Work Focus Data cleaning, reporting, dashboards for a 20-person sales team Interview Outcome First interview (selected as Operations Analyst) Nandini’s Background: Why a Pharma Student Chose Analytics Nandini started as a biology student and later moved into pharmacy. While exploring career options, she realized that staying only in pharma might limit her growth in an increasingly AI-driven job market. She noticed that fields like clinical research already handle large datasets, and she wanted a role where she could work directly with data and visualization. That’s when data analytics clicked as the right path. Recommended Professional Certificates
Data Analytics Course with Gen AI 4.9 ★★★★★ (1032) 3785 Learners 18 Weeks View BrochureLearn More Data Science Course with Internship & Placement Support 4.5 ★★★★★ (1254) 3785 Learners 20 Weeks View BrochureLearn More Nandini’s Reasons for Choosing Data Analytics and WsCube Tech Nandini chose data analytics because: It’s a high-growth, high-paying field with strong future demand. It connects with her domain knowledge (pharma/clinical research deals with data). She genuinely enjoys visualizing data and turning raw numbers into insights. She joined WsCube Tech’s Data Analysis Course to get structured, beginner-to-advanced training in: Advanced Excel SQL Power BI Python and AI Coming from a non-tech background, she appreciated the step-by-step path from basics to advanced topics. Skills and Tools She Worked On During her 5‑month program, Nandini focused on: Advanced Excel: Data cleaning, complex formulas, charts, basic forecasting. SQL: Querying, filtering, and transforming data efficiently. Power BI: Building interactive dashboards, choosing the right charts, adding filters/dropdowns. Python: From basics to advanced, though she found this the most challenging. AI: Using prompts to generate logic, understand APIs, and speed up analysis tasks. She put extra focus on SQL, as it made her data cleaning and extraction much faster. No Tech Background? Start Exactly Where Nandini Started Nandini came from Biology and Pharmacy. She had never written a formula in Excel or a query in SQL before joining. The Data Analytics Course at WsCube Tech teaches every tool from the very basics, which is exactly why the switch worked for her. What the 18-week program includes: 106+ hours of live classes across 5 milestones, starting from Excel basics The full toolkit: Advanced Excel, MySQL, Power BI, Tableau, Python and Pandas AI built into every module, including prompt engineering, AI assisted SQL, and automation with n8n and Make.com 10 projects and case studies on real company data from Amazon, Swiggy, Myntra, Tata Power and Apollo Mentors from Microsoft, Amazon, Google, KPMG and Rapido A 4-week internship as a Data Analytics Intern at WsCube Tech Bonus modules in statistics, machine learning, Microsoft Fabric and Google Analytics 4 No coding background needed. You start from zero and build up, the same way Nandini did. Experience: From Learning to Real Work Nandini completed around 6 to 8 course projects, mostly in Excel and Power BI, and submitted them on time. These projects gave her: Real-world context for messy datasets Confidence to explain her work in interviews A portfolio she could reference when interviewers asked about projects She says projects were especially important for her because she was shifting from a medical/pharma background into IT, and they helped her prove she could handle real data tasks. Responsibilities of Nandini as an Operations Analyst At Stockverse, a US-based stock trading company, Nandini supports a 20-person sales team. Her key responsibilities include: Data cleaning & organization: Handling large, messy sales, enrollment, and payment datasets. Reporting: Preparing weekly and monthly performance reports using advanced Excel and Power BI. Dashboards: Building Power BI dashboards to visualize sales performance, conversions, and top performers. Tracking & insights: Monitoring conversions, identifying top performers, and highlighting trends for leadership. Most of her day goes into data cleaning and structuring; dashboarding and meetings happen on a weekly/monthly cadence. Upcoming Masterclass Interview Process This was Nandini’s first-ever interview, and she got selected as an Operations Analyst. Application: She applied widely on LinkedIn and Indeed, built a strong profile, and received a walk-in interview call from Stockverse. Mindset: She was nervous at first, but mentors helped her prepare and stay calm. She emphasizes that confidence and clear communication matter a lot, even if you don’t know every answer. 1. Technical Round (Excel) She faced case-style Excel tasks such as: Splitting salesperson data and extracting key metrics Using advanced formulas (e.g., nested IFs, lookups, copy/paste patterns) Applying conditional formatting and building charts Discussing simple forecasts: “If sales continue at this speed, what happens in the next year?” 2. Power BI Round Interviewers asked basics like: “How do you create a dashboard?” Which charts to use for specific metrics How to add dropdowns/filters to make a dashboard interactive 3. Communication Nandini estimates that communication skills account for 50–55% of interview success. Being able to explain her thinking clearly, even while solving tasks, made a big difference. How Nandini Leveraged AI in Operations Analytics As a fresher, Nandini is still learning AI from more experienced colleagues, but she already uses it to: Generate queries and logic for dashboards via prompts Understand API-related tasks at a basic level Speed up repetitive analysis and reporting work She sees AI as a productivity booster and plans to deepen her AI skills over time. Her Learning Path Nandini’s learning journey looked like this: Course duration: Around 5 months of structured training. Daily study: About 1–1.5 hours of self-study apart from offline classes (morning/evening). Focus areas: Heavy focus on SQL for efficiency Consistent practice in Excel and Power BI via projects Pushing through difficulties in Python instead of giving up She stresses that consistency matters more than long, irregular study sessions. Even when topics felt hard, she kept showing up and finishing project deadlines. WsCube Tech Student Success Story: Placed at Stockverse (Full Interview Q & A) Ayushi: Tell us about yourself and your background. Nandini: I’m Nandini Athwani. I completed my Data Analytics course from WsCube Tech. I come from a science background (Biology), then switched to a Pharmacy background, which is totally medicinal. But I felt that only being a pharmacist is not enough in today’s AI-driven environment. After a lot of research online about what’s best for me to get higher-paying jobs, I came across Data Analytics courses. Someone suggested WsCube Tech, and I realized it’s very important to do something beyond just academic degrees. In today’s world, only degrees are not enough. Ayushi: Why did you choose Data Analytics over other fields like web development, digital marketing, etc.? Nandini: When I searched, I felt a Data Analytics course could connect with my Pharmacy background. In clinical research, data analysis is already a big part, and hospitals handle a lot of data. So I thought this is something correlated with my degree plus AI. I don’t have interest in digital marketing or web development. I love to visualize data, so I chose Data Analytics. Ayushi: When you were learning Data Analytics, which skill felt the most difficult? Nandini: Python, of course. That was the most difficult for me. Ayushi: Which skill did you focus on the most during your learning? Nandini: I focused the most on SQL. SQL is very fast and efficient, so I put a lot of focus there. I did work a lot in Python too, but it felt very high-level for me. Ayushi: What tools and topics were covered in your course? Nandini: It was a total five-month course, starting from basic to advanced: Advanced Excel, then SQL, Power BI, and of course Python and AI, all from very basic to advanced. Ayushi: Coming from a non-technical background, what helped you the most during the course, lectures or projects? Nandini: Lectures were of course very important. I used to watch offline classes and then rewatch online if I had doubts. But specially, the projects were very helpful. Projects give you real-world experience. Because of projects, I understood what actually happens in real companies. Shifting from a medical/biology background to an IT environment was very difficult initially, but since WsCube Tech taught from basics, I felt relaxed that I was understanding something. Staying consistent and working on projects was a must. Ayushi: How many projects did you complete during the course? Nandini: I did all the WsCube Tech projects. As far as I remember, around 6 to
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