In this podcast, we have the pleasure of hearing from Nisha Saini, a fresh graduate who recently completed her MBA in Data Science and secured a position as a Data Analyst at Numerator in Pune. She is also a proud member of the Jobbaaj Learning community. Nisha walks us through her journey, the skills that helped her stand out in the competitive job market, and how she successfully navigated the interview process. This podcast is part of our series featuring out-placed candidates from the Data and Business Analytics Specialization program who found success with our support. If you're an aspiring data analyst, this conversation could offer valuable insights into how to build your skills and land that dream job!

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Podcaster:
So today we have Nisha Saini with us, who is not only a member of the Jobaaj Learning Community but also starting her career as a Data Analyst at Numerator in Pune. We’ll discuss her skill set, educational background, the questions she faced in her interview, and more. Nisha, welcome, and thank you for joining us. Can you please introduce yourself to our community?

Nisha:
Thank you for having me. My name is Nisha, I’m 22 years old, and I live in New Delhi. I recently completed my MBA in Data Science from Amity University online. Additionally, I completed a Data Analyst course from Jobaaj Learning, where I learned key skills like PowerBI, advanced Excel, Tableau, Python, AI, and libraries like NumPy, Pandas, and Matplotlib. Thanks to these skills, I secured my job at Numerator. The role is a Data Analyst (French), based in Bododra, but it’s a work-from-home opportunity, which I’m very happy about.

Podcaster:
That’s fantastic, Nisha! Before your MBA, what was your graduation in?

Nisha:
I graduated with a BA in English.

Podcaster:
Great! So, after completing your BA, you decided to pursue an MBA and ended up in Data Analytics, which is exactly what you were aspiring for?

Nisha:
Yes, that’s right. In my 12th grade, I had PCB (Physics, Chemistry, Biology) with web development, and I had already learned languages like Java and Python. So, with that base knowledge, I decided to pursue an MBA.

Podcaster:
That's interesting! Now, could you tell us about any projects you worked on and showcased in your portfolio to land this job?

Nisha:
Certainly! I worked on several projects, including one using PowerBI. I analyzed Netflix data, comparing movies and TV shows by visualizing it with Pandas, NumPy, and Matplotlib. This project stood out on my resume and helped me during the interview.

Podcaster:
That sounds impressive! Could you walk us through your interview process? How many rounds were there, and what were the main topics or questions asked in each round?

Nisha:
The interview process consisted of three rounds. The first round focused mainly on my knowledge of French, with vocabulary questions. Then, they gave me a project to solve using Excel’s advanced functions, like SUMIF, IF, and other formulas. The final round was more about discussing my skills in Excel, PowerBI, and other tools. They didn’t ask too many questions beyond those topics, mainly focusing on my proficiency in French and Excel.

Podcaster:
Interesting! Do you have any idea about the tools and technologies you’ll be using in your job role?

Nisha:
Yes, for now, I’ll be focusing mostly on advanced Excel. We might use PowerBI after a couple of months, but initially, Excel will be the main tool.

Podcaster:
Great! If you had to provide a roadmap for students aspiring to become data analysts, especially those who are just starting out, what skills should they focus on? And how many projects should they work on?

Nisha:
My advice would be to start with mastering advanced Excel. Once you are comfortable with that, transitioning to PowerBI will be easier since it's just a more advanced version of Excel. Focus heavily on Python, as it requires a deeper understanding and consistent practice. For libraries like NumPy and Pandas, they’re essential for data visualization and analysis, so they should be a priority too. Lastly, Tableau is another tool that’s very similar to PowerBI, so if you're interested in visualization, both are worth learning.

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

 

General interview questions answered by Nisha during her selection process

Can you describe a situation where you used advanced Excel functions to solve a problem?

Sample Answer: In a project, I had to analyze sales data from different regions. Using advanced Excel functions like VLOOKUP, IF, and SUMIF, I was able to merge multiple datasets, calculate regional sales growth, and summarize the results in pivot tables for clear reporting.

How would you explain a complex data analysis process to a non-technical stakeholder?

Sample Answer: I would focus on simplifying the analysis by using visuals like charts and graphs. I’d explain the data in a way that highlights key takeaways, such as trends or comparisons, and avoid using too much technical jargon. I’d ensure the stakeholder understands the 'why' and 'what' behind the numbers rather than the exact methodology.

How do you ensure data accuracy and consistency when working with large datasets?

Sample Answer: I ensure data accuracy by validating datasets for missing or incorrect values. I use data cleaning techniques like handling outliers, removing duplicates, and filling missing data. I also cross-check my results with other data sources or through manual verification when possible.

What is the importance of data visualization in analytics? Can you share an example of how you used it?

Sample Answer: Data visualization is critical for making complex data more understandable and actionable. For example, in my Netflix data project, I created visualizations in PowerBI to compare the popularity of movies and TV shows across regions, which helped highlight trends and patterns that would have been difficult to identify in raw data.

Can you explain the difference between Pandas and NumPy in Python and when to use each?

Sample Answer: NumPy is primarily used for numerical data and performs mathematical operations very efficiently. Pandas, on the other hand, is used for structured data in the form of DataFrames. It’s great for handling mixed data types and has more built-in functions for data manipulation, like merging datasets and grouping data.

How do you handle missing or incomplete data in your analysis?

Sample Answer: I handle missing data by first understanding the cause of the missing values. I may fill missing values with the mean, median, or mode depending on the dataset or remove rows/columns with a large number of missing values. Sometimes, if the missing data is random, I can use machine learning techniques to predict and fill in the gaps.