u/Otherwise_Club2536

I have been using reddit from a few months, and i have recently visited some of the negative reviews present about jobaaj online , see i am student of jobaaj learnings and i have completed their assignments and mock interview, currently i am preparing for interviews, their placement team have aligned interviews for me but i have not cracked any one of them, will they block me from placements, if they block me will they give me my money back , am i been thugged, please guide me if anyone one have taken their course,

myquals: Currently a student at Jobaaj Learnings.

Hi everyone,

I’m currently interviewing for Data Analyst roles and have been preparing around skills like SQL, Excel, Power BI, data cleaning, statistics, and data visualization.

I don’t have a lot of professional experience yet, so I’m trying to understand what the realistic expectations are for someone at my experience level.

One thing I’ve noticed during interviews is that I’m sometimes being asked Machine Learning-related questions as well. I wasn’t expecting ML to be a major part of Data Analyst interviews, so I’m a little confused about how much I should actually prepare.

I understand that having some ML knowledge can be useful for a Data Analyst, especially when working with predictive analytics or alongside Data Science teams. But where should the line be for a Data Analyst?

For example:

• Is understanding supervised vs. unsupervised learning, regression, classification, clustering, overfitting, etc. enough?
• Are Data Analysts expected to know ML algorithms in depth?
• Should I learn Python and scikit-learn and build ML models, or should I prioritize SQL, statistics, Excel and Power BI?
• For people who have recently interviewed for Data Analyst roles, how often have you been asked ML-related questions?

I’d especially appreciate input from people currently working as Data Analysts or who have recently gone through Data Analyst interviews.

I’m trying to understand how much ML is actually expected in the role so I can prioritize my preparation accordingly.

myquals: B.Com, Data Analyst

Question 1:

You have a dataset with 50 million rows, and it doesn't fit into memory. How would you analyze it efficiently?

My answer:

I wouldn't load everything into Pandas. I'd first filter only the required columns/rows, push aggregations to SQL where possible, process the data in chunks, and use tools like PySpark if distributed processing is actually needed.

Question 2:

Your dashboard suddenly shows a 40% drop in active users overnight. What would you check first?

My answer:

I wouldn't immediately assume user activity actually dropped. I'd first validate the data — pipeline status, data refresh, tracking events, source tables, filters, and any recent changes to the product or tracking logic. Once the data is confirmed, I'd investigate the actual cause.

If you were in my place, how would you answer these two questions?

Would love to hear how experienced Data Analysts would approach them.

I recently worked on an end-to-end fraud analytics project using a large transaction dataset.

The challenging part wasn’t just building a dashboard. I worked through the complete process:

* Data cleaning and preprocessing

* SQL analysis of transaction patterns

* Python-based anomaly detection

* Fraud vs. genuine transaction analysis

* Customer risk segmentation

* Identifying unusual transaction behaviour

* Time-based fraud pattern analysis

* Power BI dashboard for monitoring fraud KPIs

One thing I found interesting was how much the results changed after looking at *transaction behaviour rather than just individual transactions*.

For those working in data analytics:

What techniques would you use to improve a fraud detection project like this further?

And how much does it really matter in a Data Analytics interview if you couldn’t answer 1–2 questions?

Right now, waiting for the result feels harder than the interview itself. 🥲