This blog highlights an insightful podcast conversation with one of our recently placed candidates from the Data and Business Analytics Program. Mr. Prashant, who had five years of experience before joining our course, shares his personal journey of upskilling, overcoming challenges, and landing a lucrative job offer. He reflects on how his learning experience with us transformed his career and helped him secure a position with a high-paying package. Here’s a glimpse into his story, as shared with us through this podcast.

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Podcaster: Good afternoon, everyone! We are back with another lovely placement story. We have with us Mr. Prashant today. Mr. Prashant, how are you?

Prashant: I'm doing well. Thank you for having me on this session, and it's been a pleasure to be associated with Jobaaj Learnings.

Podcaster: Okay, Prashant, could you please introduce yourself to our audience?

Prashant: Before joining Jobaaj, I had five years of experience in the data industry. Despite that, I realized there were many concepts I wasn't familiar with. When I joined the course, I was surprised to learn about these areas that exist in the data world. For example, the coordinator, Kashish, not only supported me but also explained scenario-based questions and project work. She helped me understand how to approach these tasks and deliver them effectively, which made a huge difference in my interviews. I had never thought about approaching problems this way, and this knowledge cleared up a lot of confusion for me.

Podcaster: So, you had five years of experience, but you still found new concepts in the course?

Prashant: Yes, exactly.

Podcaster: That’s really interesting! Now, can you tell us about your educational background?

Prashant: I have a technical background with a B.Tech in Bachelor's and an MBA in IT. Even though I had technical education, there were still gaps in my knowledge. After joining Jobaaj, I learned many new concepts that I was unaware of.

Podcaster: That's great to hear! I'm sure Kashish will be happy to hear these kind words from you.

Prashant: Yes, she was extremely helpful throughout the process.

Podcaster: Now, coming back to the job market, many people think that after five years of experience, one is completely settled. Do you think that's true?

Prashant: Honestly, I wasn't struggling to get a job, but I wasn't getting the package I expected. Even though I had five years of experience, the companies I was applying to weren't offering the right salary. My skills were mainly in Power BI, Excel, and SQL, which are basic tools. However, after completing the course, I learned about Python, statistics, machine learning, and Tableau. These skills not only helped me get shortlisted by several companies, but they also grabbed the attention of hiring managers. That's when I realized why I wasn't getting the desired package before.

Podcaster: So, the key takeaway here is that we need to keep upskilling, right? You can't just stay at the same level.

Prashant: Yes, that's right. While working in my previous job, I was just performing tasks but not learning new things. Once I joined Jobaaj, learning became a part of my journey, and it made all the difference.

Podcaster: Absolutely! Now, many people believe that once they pay for a course, they don't need to do much else. They expect a high salary right after completing it. What are your thoughts on this?

Prashant: If you think that way, you're only fooling yourself. It's not just about paying for a course and expecting results. Attitude plays a huge role. If you're not willing to learn, grow, and put in the effort, you'll never make it. Institutes can only give you 80-90% of the knowledge. The remaining 10% comes from your effort. Without that extra push, you won't succeed in the job market.

Podcaster: I completely agree with you, Prashant. Effort and attitude matter a lot. Now, let's talk about the placement process. Was Saniya your placement coordinator?

Prashant: Yes, Saniya, Vishaka, and Vinnie were a huge part of my placement journey. They didn't just process my profile and send me job leads; they also analyzed where I was lacking and worked with me to improve my soft skills, communication, and attitude. Their support gave me the confidence I needed, especially when I faced rejections. They kept pushing me to apply for jobs and provided feedback, helping me believe in myself even when I didn’t.

Podcaster: That's amazing! It's clear that they went above and beyond for you.

Prashant: Yes, absolutely. They really treated me like family and gave me the guidance I needed to keep moving forward.

Podcaster: Now, let’s wrap up with one final question. As someone who landed a job with a 10 LPA package, what advice would you give to freshers or unemployed individuals trying to break into the industry or find better opportunities?

Prashant: My advice is simple: keep learning and upgrading your skills. If you're a fresher or even someone who's been unemployed, analyze your weaknesses and address them. Publish your projects on GitHub, optimize your LinkedIn profile, and stay active on platforms like LinkedIn. Recruiters don’t just look at resumes—they look at the whole package. So, show what you can do, not just what you know.

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

 

General interview questions answered by Prashant during his selection process

Tell us about a data analysis project where you used Power BI. How did you handle large datasets and create effective visualizations? 

Sample Answer: In one of my projects, I used Power BI to analyze sales data from multiple regions. The dataset was large, with over 1 million rows, so I first optimized the data by removing duplicates and filtering unnecessary columns. I used Power Query for data transformation, which allowed me to load only the necessary data into the Power BI model. For visualization, I focused on using bar charts and line graphs to represent sales trends, and I implemented slicers for users to filter the data by region and time period. The dashboard allowed stakeholders to gain insights into regional sales performance and identify opportunities for improvement.

Can you explain a scenario where you used SQL to extract and analyze data? 

Sample Answer: Sure. In my previous role, I was tasked with analyzing customer purchase behavior. I used SQL to extract data from multiple tables in the database, such as customer information, transaction history, and product details. I wrote complex SQL queries using joins to combine these tables, filtered data based on specific conditions (e.g., transactions above a certain amount), and aggregated the results by customer segment. This allowed me to provide insights into purchasing patterns, which were used to target high-value customers with personalized offers.

Explain the concept of data normalization. Why is it important in data analysis?

Sample Answer: Data normalization is the process of adjusting values in a dataset to a common scale without distorting differences in the ranges of values. This is important when you're dealing with different units of measurement or varying scales, such as income data or product prices, and you need to make them comparable. For example, in a machine learning model, normalization helps prevent features with larger scales from dominating the model. It’s also important when combining datasets from different sources, ensuring that data values are comparable and don't introduce bias.

Can you explain the concept of machine learning and how it could be applied in a business scenario?

Sample Answer: Machine learning is a branch of AI that allows systems to automatically learn and improve from experience without being explicitly programmed. In a business scenario, machine learning could be used to predict customer churn by analyzing historical data on customer behavior. By training a model on past customer data, such as purchasing patterns, engagement, and feedback, the model could predict which customers are likely to leave and help businesses take proactive steps to retain them. Another example could be using machine learning for demand forecasting, where a model predicts future product demand based on historical sales data.

How would you explain the difference between supervised and unsupervised learning in machine learning?

Sample Answer: Supervised learning involves training a model on labeled data, where the input data is paired with the correct output. The goal is for the model to learn the mapping between input and output so it can predict outcomes for new, unseen data. For example, in a classification problem, the model might be trained to predict whether an email is spam or not. Unsupervised learning, on the other hand, works with unlabeled data and focuses on finding patterns or relationships within the data. A common example is clustering, where the model groups similar data points together, such as segmenting customers based on purchasing behavior.

In SQL, what is the difference between a LEFT JOIN and an INNER JOIN?

Sample Answer: A LEFT JOIN returns all records from the left table and the matching records from the right table. If there is no match, the result will contain NULL values for the right table’s columns. An INNER JOIN, however, only returns records where there is a match in both tables. Essentially, a LEFT JOIN gives you all the records from the left table, while an INNER JOIN returns only the records that have corresponding matches in both tables.