This blog captures the insightful podcast interview with Khushi, one of our successful alumni from the Data and Business Analytics Program. Khushi transitioned from a Bachelor of Commerce background into the world of data analytics and secured a role at Zeta Global as a Data Analyst through our placement support. In this candid conversation, she shares her learning journey, placement strategies, project experiences, and valuable tips for fellow learners aiming to break into analytics.

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Podcaster:
Hello everyone! Today we have Khushi with us. Khushi is one of the alumni of the Data Corporate Training. Hello Khushi, how are you?

Khushi:
I'm doing great!

Podcaster:
Thank you so much for taking the time to join this podcast. Your journey and experience will definitely inspire and guide many learners who are preparing to start their careers in data analytics. So, Khushi, can you begin by giving a quick introduction about yourself—your background and current role?

Khushi:
Yes, sure! I completed my Bachelor of Commerce and started my career at AARA as a Project Coordinator. I was responsible for project management, database management, billing reconciliation, stakeholder and client management—especially handling escalations for off-track projects. I also ensured SLA compliance. Recently, I joined Zeta Global as a Data Analyst.

Podcaster:
That’s great to hear! What kind of tools and technologies are involved in your new role at Zeta Global?

Khushi:
I was interviewed on Excel, Advanced Excel, SQL, and Tableau. Currently, I’m in the training phase. So far, I’ve been trained on Excel and Advanced Excel, and we’ve just begun exploring Tableau.

Podcaster:
Nice! Now, let’s talk about your learning journey. What were the main modules or tools you focused on during the program?

Khushi:
My top priority was to get a job as a Data Analyst. So, I focused on the most relevant modules: Excel, SQL, and Tableau. I found that these three tools were enough for me to crack the job interviews.

Podcaster:
You also joined the live sessions, right? What was your experience with those?

Khushi:
Yes, I did. The live sessions were really helpful. The mentors provided real-life case studies and addressed all our questions—whether they were technical, related to our background, or about switching into data analytics from non-technical fields.

Podcaster:
That’s great. Now a very common concern: You’re from a B.Com background. Many students from non-technical streams like BBA, BA, or B.Com worry about entering the data domain. Did your non-tech background affect your chances?

Khushi:
To be honest, college helps us develop our communication and presentation skills. After that, it’s about the direction we choose. Being from B.Com myself, I can confidently say: Focus on your skills. Specialize in tools relevant to data analytics. That’s what helped me.

Podcaster:
Have you ever faced discrimination during interviews because of your degree?

Khushi:
Maybe in a few companies in the beginning. Some might prefer B.Tech candidates. But not all companies do. Many value skills over degrees. You just have to find the right companies and keep applying.

Podcaster:
Absolutely. So, projects—how important do you think they are during the learning phase?

Khushi:
Projects are super important. They give you a near real-world experience and show how things work in companies. Most importantly, they help you market yourself better to recruiters.

Podcaster:
Right! Learners need to treat their portfolio like a personal brand. If you say you know SQL or Tableau, you should be able to show that through real projects. Okay, now let’s talk about your job application process. What do you think are the most crucial parts—resume, Naukri optimization, or interview prep?

Khushi:
Everything matters, but resume optimization is key. Your resume should be customized for each role. For example, if you're applying to a data role in investment banking, use relevant terminology. Also, make your resume ATS-friendly so recruiters can find you easily on job platforms.

Podcaster:
Great point. And how did you apply for jobs? What worked for you?

Khushi:
I used Naukri, LinkedIn, and company career portals. Applying through the company’s own website often reduces competition. Also, asking for referrals on LinkedIn helps a lot—but do it properly. Don’t just ask, “Do you have openings?” Instead, share the job link and your resume with the person. Make it easy for them to help you.

Podcaster:
Yes, that's a very practical tip. Learners must respect professionals’ time. Okay, wrapping up—what’s your final advice for learners just starting out?

Khushi:
Be consistent. Whether it's learning, applying, or optimizing your profiles—do it regularly. Even during festivals or if you're unwell, try to spend at least 1–2 hours daily on your skills and job prep. That consistency is what helped me.

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Don’t miss the full conversation—watch the podcast now and get inspired by Khushi’s journey!

 

General interview questions answered by Khushi during her selection process

What are your strongest tools or technologies in data analytics?

Sample Answer: I’m most confident in using Excel, SQL, and Tableau. In Excel, I work comfortably with pivot tables, VLOOKUP, INDEX-MATCH, and data cleaning techniques. In SQL, I can write queries involving JOINs, aggregations, subqueries, and window functions. Tableau is my preferred tool for creating data visualizations and dashboards to communicate insights effectively.

How would you approach cleaning messy data in Excel?

Sample Answer: I begin by scanning for inconsistencies like blanks, duplicate values, or formatting issues. I use functions like TRIM, CLEAN, IFERROR, and TEXT to standardize the data. I also use data validation to ensure input correctness and pivot tables to identify outliers or missing information. Sorting, filtering, and removing duplicates are key steps in the process too.

What kind of SQL queries have you worked with?

Sample Answer: I’ve written SQL queries to join tables, filter data using WHERE and HAVING clauses, calculate aggregates like SUM and COUNT, and use subqueries and window functions such as RANK and ROW_NUMBER to solve analytical problems. I’ve applied these techniques in project scenarios like analyzing sales data or customer trends.

Explain a project you worked on during your training.

Sample Answer: In one of my projects, I worked on a retail dataset where I analyzed sales across different regions and time periods. I used SQL to extract metrics like top-selling products and monthly revenue, Excel for initial cleaning and formatting, and Tableau to create an interactive dashboard showing sales performance and trends. It gave me a good understanding of the full data analysis pipeline.

How do you ensure your work meets deadlines and quality standards?

Sample Answer: From my experience as a Project Coordinator, I’ve learned how to manage tasks efficiently. I break down large tasks into smaller milestones, prioritize based on deadlines, and review my work at each step. I also make sure to validate data results by cross-checking with business logic and documenting every step to maintain transparency and consistency.

How would you explain a complex dataset to a non-technical stakeholder?

Sample Answer: I would focus on simplifying the language and using visuals like graphs and dashboards. Rather than explaining technical methods, I’d emphasize the insights and their impact on business. For instance, instead of saying I used SQL joins, I’d say I combined sales and customer data to identify high-value clients by region.

Have you faced any challenges while switching from a non-tech to a tech domain?

Sample Answer: Yes, at first it was a bit overwhelming to grasp technical terms and coding logic. However, I approached it step-by-step with structured learning, practical assignments, and continuous practice. Support from mentors during live sessions also helped me build confidence and improve my skills gradually.