Leaving a stable, full-time job to start a completely new career is one of the hardest decisions anyone can make. It is even harder when you have no tech background and no idea whether the new path will work out. Anchal made that decision anyway. She moved from HR into Data Analytics, and today she works as a Data Analyst at Patanjali Parivahan, the logistics arm of Patanjali.
In this student success story, we cover her complete journey. In the interview below, Anchal talks about why she switched, the exact tools she learned, how her Patanjali interview went, and how she uses AI tools to automate parts of her daily work.
If you are a fresher or a career switcher wondering whether a data analytics course can actually lead to a real job, this blog and video are for you.
Key Highlights of the Interview
In this podcast, Anchal shares:
- How she moved from a non-tech/HR background to a data analyst role
- Why she chose a data analytics course over an MBA
- What her real job at Patanjali looks like day to day
- Which tools and skills mattered most in interviews
- Her complete Patanjali interview experience across all 3 rounds
- How she uses AI to automate reports and emails
- Why she still recommends starting with Excel, not Python
- Her advice for freshers starting their data analytics journey
Watch the Full Interview
Prefer watching the video? Catch Anchal's full story below:
Student Highlight
| Metric | Detail |
| Student Name | Anchal |
| Current Role | Data Analyst & Project Coordinator |
| Company | Patanjali Parivahan (Patanjali Pvt. Ltd.) |
| Previous Background | BBA / HR & Talent Acquisition |
| Key Tools Used | Excel, Power BI, Python, SQL, AI (Claude, n8n, Anti-Gravity) |
| Learning Path | WsCube Tech Data Analytics Program |
| Projects Built | ~30 (20 guided + 10 self-driven) |
| Learning Duration | 6 months, full time |
| Interview Rounds Cleared | 3 (HR, Technical, VP) |
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Anchal's Background: From HR to Data Analytics
Anchal started her career after BBA in HR, working for about a year where her daily tasks included taking interviews and repetitive Excel and Google Sheets work.
“I realized I was stuck in the HR loop. I wanted to understand data, as data made me curious. I left my job and spent three months deciding whether to do an MBA or a specialized course.” [03:06]
She realized:
- She was stuck doing the same tasks every day.
- She was more curious about data and how decisions are made using it.
- She wanted a role where she could solve problems using analytics, not just administrative work.
That is when she decided to leave her job and invest 6 months fully into learning data analytics instead of doing an MBA.
Why She Chose Data Analytics (and WsCube Tech)
Before joining WsCube Tech, Anchal:
- Explored multiple courses and platforms, including large MOOC sites.
- Watched a 29 hour free YouTube series from WsCube Tech, which helped her understand concepts in Hindi and English with practical examples.
- Felt that other courses were too theoretical; she needed implementation-focused learning and business context.
Key reasons she chose WsCube Tech:
- Clear, practical explanations in a language she was comfortable with.
- Focus on how to use skills in real business scenarios, not just passing assignments.
- Strong mentorship, community, and placement guidance.
Skills & Tools She Focused On
Anchal built around 30 projects during her learning journey, both guided and self-driven.
Her core stack:
- Excel for data cleaning, pivot tables, dashboards, and conditional formatting
- SQL for querying and working with real datasets
- Power BI for building dashboards for management
- Python for automation, web scraping, and advanced analysis
- AI tools like Claude, n8n, and custom automations for emails and reports
Interestingly, she found Excel the hardest at first, especially dashboards and storytelling, even though she was comfortable with Python and SQL basics from earlier.
Her advice for freshers:
"Start with Excel. It's crucial for data cleaning, and even if you use Power BI or Python, you'll still need Excel. Many MIS analyst roles start with strong Excel skills.” [12:40]
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Internship Experience: From Learning to Real Work
Before Patanjali, Anchal completed an internship at a B2B company working on international rice trade data.
There, she:
- Implemented the same project in three layers: Excel, then Python, then automated web scraping using LLMs and AI.
- Got her first real exposure to web scraping with Python and regex.
- Learned how to translate course skills into real business problems and KPIs.
She emphasizes:
"Internship is crucial. Courses teach you tools; internships teach you how to use them for business problems, KPIs, and management needs." [09:40]
Role at Patanjali: What Does a Data Analyst Actually Do?
Anchal works with Patanjali Parivahan's logistics and transport company, where her role includes:
- Creating daily, weekly, and monthly MIS reports for management.
- Working on commercials, RFQs, and financial year-related reports.
- Building dashboards that help top-level management make decisions.
- Acting as a Project Coordinator for a new Patanjali startup project called Tour Bro, where she handles coordination with vendors and customers.
Her day-to-day mix:
- Data Analyst work: reports, dashboards, and KPIs for transport, cost per km, margin percentage, and more.
- Project coordination: meetings, vendor calls, and cross-department work across maintenance, legal, HR analytics, and others.
She notes that in the beginning she did not even know basic transport industry terms, but through reports and projects she quickly learned domain-specific KPIs and business logic.
Interview Process: How She Got Selected at Patanjali
Anchal gave only one interview before getting selected at Patanjali, after an initial HR analytics interview that did not convert.
Her interview rounds:
- HR round for basic introduction and fit.
- Technical round with manager, focused on:
- Excel: VLOOKUP and HLOOKUP, pivot tables, dashboards, conditional formatting
- Power BI: how she builds dashboards, what she knows
- Discussion of her projects
- A small Excel task to create a dashboard, which she submitted
- Final round with VP for more discussion on the role, expectations and fit.
Key takeaways from her interview experience:
- Basics must be very clear, especially Excel.
- You do not need to answer 10 out of 10 questions perfectly; you need to show clear understanding and how you think.
- Mock interviews and peer practice helped her gain confidence.
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How She Uses AI in Her Daily Work
Anchal actively uses AI to improve efficiency:
- Uses Claude and n8n to automate tasks.
- Built tools to draft and categorize internal versus external emails.
- Automated daily report generation, from download to clean to format to send.
- Reduced repetitive work so she can focus on analysis and coordination.
Her view on AI:
"AI is not a threat; it's an opportunity. It's generating more jobs and more skills. Tasks that used to take 4 to 5 hours now take 30 to 40 minutes. But you still need to understand the code and libraries AI uses, that's where learning happens."
She recently learned n8n workflows to further automate repetitive reporting and data cleaning tasks.
Learning Routine: How Much Time She Invested
During her 6-month learning phase, Anchal:
- Dedicated around 14 hours a day to learning and practice.
- Made her own notes because she learns better with repetition and practice.
- Focused heavily on building projects, not just watching videos.
She admits it was a hard decision to leave her job and stay without income until she got the next role, but the clear goal, "I want to be a data analyst", kept her focused.
Anchal's Advice for Freshers Starting in Data Analytics
If you are just starting, Anchal's key advice:
1. Practice with projects
- Do not just watch tutorials; build dashboards and reports end-to-end.
- Recreate projects from YouTube and LinkedIn to understand tool usage and layout.
2. Start with Excel
- Master data cleaning, pivot tables, and basic dashboards before jumping to advanced tools.
3. Use AI smartly
- Use AI to speed up work, but always read and understand the generated code.
- Treat AI like a senior you ask for help, not a black box.
4. Solve problems yourself first
- Ask mentors and AI, but also try to solve things on your own to build real problem-solving skills.
5. Do an internship if possible
- It bridges the gap between course projects and real business problems.
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How to Become a Data Analyst Like Anchal
Anchal had no tech degree. Six months of practice and around 30 projects got her hired at one of India's biggest brands.
Our Data Analytics Course gives you that same path with structure.
- Excel, SQL, Power BI, Python, and AI tools like Claude and n8n
- A 4-week internship, the part Anchal calls crucial
- Real projects, mentor support, and an active student community
- Placement assistance with resume help and mock interviews
Want the same path? Join the Data Analytics Course at WsCube Tech, built to take beginners from zero to job ready.
Full Interview Q&A: How Anchal Cracked the Patanjali Data Analyst Role
Ayushi: Tell us about your background and how you transitioned into a Data Analyst role at Patanjali.
Anchal: After 12th, I did some small data analytics courses, but during my BBA, I considered myself more of a management student and started applying for HR internships. I did an 8-month HR internship and then worked in an HR role for about a year, mostly doing interviews and repetitive Excel/Google Sheets work.
I realized I was stuck doing the same tasks every day and became more curious about data and how decisions are made using it. So I left my job and was confused between doing an MBA or taking a skill-based course. During that time, I found a 29-hour free YouTube series from WsCube Tech, which explained concepts very clearly in Hindi + English with practical examples. That convinced me to choose data analytics over an MBA. After the course, I understood not just the skills but also how to implement them in business, make dashboards useful for top management, and then I started applying for internships and eventually got this job at Patanjali.
Ayushi: How is your current job different from what you learned in the course?
Anchal: In the course, we learned all the required skill sets for a data analyst, but now with the AI boom, those skills plus AI are helping a lot in making dashboards and reports faster. A task that used to take one week can now be done in 2–3 days. The key is how we implement these skills. I also suggest students understand how AI is helping, because AI is an essential skill now.
Ayushi: Can you explain your current role in simple words?
Anchal: I work in Patanjali Parivar’s logistics/transport company. My daily work is like an MIS analyst: I create daily, weekly, and monthly reports for management, work on commercials and RFQs, and build dashboards that help top-level management make decisions. I also use Python to automate some of my work.
Apart from this, I’m working as a Project Coordinator for a new Patanjali startup project (“Tour Bro”), so I’m not only doing data analyst work but also coordinating with vendors, customers, and different departments.
Ayushi: As a fresher, how much exposure have you got to new types of projects and dashboards after joining?
Anchal: In courses, we work on pre-defined datasets and projects. In real life, things change every day: different reports according to company needs, understanding the business and industry, deciding which KPIs to build, and sometimes just simple Excel is what management needs, not fancy dashboards. So I’m learning how to implement tools according to the actual need of the business.
Ayushi: Can you briefly explain your internship role before Patanjali?
Anchal: I interned at a B2B company working on international rice trade data. There, I worked on the same project in three layers: first in Excel, then in Python, and then automated it using web scraping with LLMs and AI. That was my first real exposure to web scraping with Python and regex. It was difficult, but with good mentorship, I learned how to implement Python in real-life scenarios instead of just doing course tasks.
Ayushi: How important do you think an internship is before landing a job?
Anchal: Internship is crucial. In courses, we focus on completing tasks and making fancy dashboards for our portfolio. In an internship, you understand industry problems, business problems, and how to use tools according to management needs. Not every time you need Python or Power BI; sometimes simple Excel is enough. Internship teaches you how to implement tools based on real requirements.
Ayushi: How difficult was it for you to switch from a non-tech/HR background to a technical role?
Anchal: It was quite hard, but I had some prior exposure to data analytics and computers in school, so I was aware of Python and SQL basics. For me, the hardest part was Excel: VLOOKUP/HLOOKUP, pivot tables, and especially dashboards. I could clean data and make pivots, but I struggled with presenting it properly. Later, with guidance, I learned dashboarding and storytelling. Overall, it was a very good journey.
Ayushi: Why did you choose data analytics and not digital marketing, cybersecurity, or other fields?
Anchal: I also tried a digital marketing course and even did an internship in web development. I chose data analytics because while analyzing data, I felt this was where I fit. I’m a creative person who likes solving problems by understanding data. So I did self-analysis and realized I’m more of an analytics person.
Ayushi: Which tool do you think is most important for a fresher to get into data analytics?
Anchal: Again, I’ll suggest Excel. Excel is important for data cleaning, and even if you use Power BI or Python, you’ll still need Excel. Many MIS analyst roles start with strong Excel skills. If a person knows Excel well, they can get roles like MIS analyst easily.
Ayushi: Which part of Excel did you find most difficult?
Anchal: Dashboarding. I was comfortable with data cleaning and pivot tables, but initially I couldn’t present the data well. If you can’t present, you can’t explain what you built and what story the data is telling. Later, with guidance, I learned dashboarding and storytelling.
Ayushi: Why did you choose WsCube Tech over other platforms/courses?
Anchal: I explored many courses, including big MOOC platforms. I chose WsCube Tech because that 29-hour YouTube series literally made me feel that this is what I want to learn. The teaching style, examples, and practical knowledge were very clear, especially for someone like me who is more comfortable with Hindi + English explanations. In the course as well, concepts were explained in a way that students could understand and see how to implement them in real business scenarios.
Ayushi: How many projects did you build during your learning journey?
Anchal: I recently checked my GitHub; I’ve made around 30 repositories/projects. About 20 were guided course projects, and the rest were self-driven. I focused on building projects because just watching isn’t enough; I needed to understand practical usage.
Ayushi: How many interviews did you give before getting selected at Patanjali?
Anchal: I gave one interview for my first internship and got it. Then I gave one interview for Patanjali and got selected. So, just one interview before Patanjali (for the internship).
Ayushi: Can you explain how your final interview went? What type of questions were asked? Was there more focus on Excel?
Anchal: The interview started with a basic introduction, then moved to Excel: VLOOKUP/HLOOKUP, how to make dashboards, using pivot tables, conditional formatting, etc. Since the data here is transport-related, the industry context matters. Then they asked about Power BI: what I know and how I make dashboards. They also discussed my projects and gave me a small Excel task to create a dashboard, which I submitted. Based on that, I got the job. The basics needed to be very clear.
Ayushi: How many interview rounds were there?
Anchal: Three levels:
- HR round
- Technical round with my manager
- Final round with the Vice President
Ayushi: Was there anything unexpected or different during this interview process?
Anchal: Yes, it’s a funny story. I initially came to Patanjali Parivar for an HR Analyst role because of my HR background, but I didn’t get selected there. By chance, I then gave an interview for a Data Analyst position and got selected. So it was destiny; I was meant to come into data analytics. Now I work on data across all departments, including HR analytics.
Ayushi: How much exposure are you getting to different domains within the company?
Anchal: When I joined, I didn’t even know basic transport industry terms: trucks, drivers, controllers, how to calculate margin percentage, cost per km, etc. There were many new KPIs. The more reports and projects I made, the more I understood what to do with the data. Also, I’m not only working on reports; I’m doing project coordination as well, so I’m getting exposure to maintenance, legal, HR analytics, vendor calls, customer meetings, etc.
Ayushi: Are you happy with where your role is moving?
Anchal: Yes, I’m really happy. It feels related to me because I did a BBA (management work), and I’m getting opportunities to work as a management person as well as a data analyst. I work across different departments, which matches my interests and background.
Ayushi: How much does communication play a role in your daily work vs pure analysis?
Anchal: It’s a combination of both. I have fixed daily tasks: creating reports, working on commercials and RFQs (especially since the financial year has started). At the same time, coordination is also fixed: stand-up meetings, meetings with vendors, and customers. So it’s a mix of data analysis and communication/coordination.
Ayushi: What’s the major difference between your learning journey in the course and your current job journey?
Anchal: Implementation is the biggest difference. In the course, we learn what the skills and tools are and how to use them. In the job, it’s about how we present insights to management and solve real business problems. It’s not necessary to always use a particular tool; it’s about how we present according to management needs.
Ayushi: In your current role, how much do you use AI in your day-to-day work?
Anchal: I’ve made AI tools for automating tasks. I basically use Claude and n8n. For example, if I have to merge tables, I don’t write Python code every time; I directly ask my AI prompt and it does my work. I’m trying to minimize my manual work using AI.
Ayushi: Many people are scared that AI will replace data analysts. What are your thoughts?
Anchal: We don’t have knowledge of everything. If someone is helping us (like seniors), it’s similar to using AI. AI is not taking our jobs; it’s generating more jobs and more skills. Tasks that used to take 4–5 hours now take 30–40 minutes. And it’s helping us learn: when AI writes code, we should still read and understand which libraries are used. Recently, I made a Plotly dashboard and learned about a JavaScript library I didn’t know before. So AI is more of an opportunity than a threat.
Ayushi: What new AI-related things are you learning that are helping you a lot?
Anchal: I recently learned about n8n, where we can create workflows to automate tasks. For example, my daily reports: I know I have to download from the same place, clean the data, process it, and represent it in a specific format. I automated this entire flow using AI, so my time is reduced, and I can focus on other tasks. This helps me manage both my data analyst and project coordinator roles, especially in a startup setup with many meetings.
Ayushi: When you were starting your journey as a student, how much time did you dedicate to learning daily?
Anchal: During the course, after watching the uploaded videos, I made my own notes because I need practice to remember things. I focused heavily on practical projects. I was giving around 14 hours a day because I knew I wanted to do this. I had left my job and wasn’t doing an MBA, so I was fully dedicated to this path.
Ayushi: How was the decision for you to leave your job and fully invest in learning data analytics?
Anchal: It was a very hard decision. I knew I had to do either an MBA/further studies or a data analyst course. I was aware I was leaving the job because I wanted to be a data analyst, but I was still afraid: will I be able to do it? Will I get a job again? When you leave a job, your resume looks blank until you get another opportunity. But when I joined WsCube Tech, that six-month journey was very good: I got a community, I could learn from others’ projects, and that made me more confident. Even when my first dashboard got low marks, I didn’t quit; I kept improving.
Ayushi: What advice would you give to any fresher starting in data analytics?
Anchal: The more projects you make and the more you use your skills on real problems, the better your understanding will be. It’s not about just reading; it’s about implementation. If you don’t know what projects to do, there are tons of projects on YouTube and LinkedIn. Make the same projects exactly as they are made; you’ll get an idea of how tools are used, how dashboards are structured, etc. Practice is really important. Also, try to solve things by yourself first. You can ask mentors or AI, but the more you ask yourself, “Can I solve this on my own?”, the more helpful it will be.
Rapid Fire (From the Interview)
- Ayushi: SQL or Power BI – which would you choose?
- Anchal: Power BI.
- Ayushi: If you had to restart your journey, what would you focus on more: certificates or projects?
- Anchal: Projects.
- Ayushi: One skill every data analyst should focus on?
- Anchal: Communication.
- Ayushi: One interview mistake freshers should avoid?
- Anchal: Using bookish language and definitions instead of clear, practical explanations.
- Ayushi: AI – threat or opportunity for data analysts?
- Anchal: Opportunity.
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