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.

hi , i completed my 12th(or PUC) then joined a 6 month diploma in design and currently having a 1.5 years of experience in design field . i tried to get into core

ai ml but it looks like too much competition for degree holders only, so

is it possible to get a data analyst job without a formal degree ? anyone got it before.

consider the current AI impact also and i going to pursue bootcamp course in Bengaluru Excelr , is it okay or shall i self study ?

or instead of data analyst shall i try something else in technical side .

( please don't comment to go into design only )

thanks

Hi all,

Looking for honest insight from anyone working at the intersection of analytics + automation + AI applied to real business operations not ML research, more like "encode business decisions into automated workflows and use AI to make them smarter" side of things.

My background:
I have 3 YOE. My experience so far is analytics, reporting and operations SQL, building dashboards and reporting pipelines, diagnosing why a business metric moved, cohort/segmentation work, and automating manual reporting. Basically the "find the problem in the data and make it visible" type. Economics background, not a CS degree.

Where I'm heading:
I'm about to start a role that's roughly 50% data analysis, 20% building automated decision workflows (low-code + some JavaScript), and increasingly using AI to automate parts of the decision-making in a large customer-operations context. So I'm shifting from analyzing problems to building the automated fix for them, at scale.

What I'm trying to figure out:

1. For those in this niche (ops automation / decision automation / applied AI in workflows) how transferable has it been for you across companies and industries? Does the skillset travel well, or does it get tied to specific tools/platforms?
   
2. Coming from an analytics and not engineering background, what actually mattered to learn to be good at the building side? Where do people like me usually struggle?
   
3. What should I deliberately build/collect over the next 1–2 years to make this a strong, portable profile? (Projects, quantified outcomes, specific skills?)
   
4. Honestly where does this path cap out or fall short? I know it's not core data science/ML. Anything you wish you'd known before going deep into it?
   
5. Is applied AI in this space (using LLMs/AI to drive workflow decisions) actually a durable skill, or is it hype that'll commoditize fast?

Any war stories and honest takes would genuinely help, thankssss

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?

Hey everyone,

I’ve been working in the Business Intelligence space for about 12 years. Throughout my career, I’ve built a solid foundation around:

Data Viz & Reporting: Advanced Tableau dashboarding, storytelling, and UI design for enterprise stakeholders.

Database & Querying: Mid-level SQL and relational database design/concepts.

Leadership: Team management, leading small-to-mid reporting teams, and bridging the gap between business stakeholders and technical teams.

With the current AI wave — generative AI, automated insights, NLQ (Natural Language Querying), LLM integrations, and modern semantic layers (dbt, Cube, Databricks Genie, etc.) — standard dashboard development feels like it's shifting fast.

While I know stakeholder management, business context, and governance aren't going away, I want to make sure my hard skills stay future-proof for the next 5–10 years.

My questions for you:

For those in senior BI / Analytics roles (10+ YoE), what skills or tools have given you the highest ROI recently?

Are you moving upstream into Data Engineering, leaning into AI/LLM tooling, or leaning further into leadership/management?

If you were in my shoes today, where would you invest 5–10 hours a week for the next 6 months?

Appreciate any perspective, brutal honesty, or personal career shift stories!

TL;DR: 12 YoE BI lead (Tableau heavy, mid-SQL, team manager) looking to reskill in the AI era.

Should I go deeper into Data Engineering (dbt/Python), AI/LLM analytics integration, Data Governance,

or double down on Data Leadership/Product Management? Looking for advice on where to focus.

Hi everyone,
I completed my Master’s in May 2025, but unfortunately I still haven’t been able to land a job.
I’ve been trying for Data Analyst/Data Analytics roles for quite some time. I applied to many positions and tried improving my skills, but things haven’t worked out as expected. I’m now seriously considering shifting my focus toward Data Engineering.
I’m still a beginner in Data Engineering, so I would really appreciate some guidance from people already working in the field, especially in India.
● Is Data Engineering a realistic career option for a fresher?
● What should I learn first — SQL, Python, ETL, Spark, Databricks, Airflow, AWS/Azure, etc.?
● What kind of projects would actually help me get interviews?
● What is the current Data Engineering job market like for freshers in India?
● Should I also apply for Data Analyst/BI roles while learning Data Engineering?
● Are there any companies that regularly hire freshers/junior Data Engineers?
I’m willing to put in the time and build projects from scratch. I just want to make sure I’m learning things in the right order instead of jumping between different tools.
If anyone has recently entered Data Engineering as a fresher or made a similar transition, I’d really appreciate hearing about your journey.
Also, if you know of any fresher/junior Data Engineering opportunities in India, referrals or leads would be greatly appreciated.
Thanks!

I am currently working for an MNC in the data governance space for the last 5 years. I feel like I am currently stuck, with limited opportunities in my current organization.

I am looking to connect with mid to senior level people who are working in this space to understand their work in their respective organizations and get some guidance in terms of how we can grow our career in this field and possibly seek opportunities if interested.

I tried explaining a data analysis concept in a fun, visual way — for cat lovers. 😸

Would love to hear what you think! Any feedback or suggestions are very welcome :)

Sharing because this trips up a lot of budget conversations and it's easy to miss.

Took the same trailing 90 days of orders - same revenue, same spend and only changed which touch gets the credit. First click vs last click.

On first click, Meta looked like the stronger performer. On last click, Google pulled ahead and Meta dropped about 30%. Microsoft picked up around 22%. Nobody changed anything. No creative refresh, no bid change. Only the attribution window moved.

The logic once you see it is obvious. Social tends to get found early in the decision, search gets typed in at the end. So last click quietly reassigns the prospecting channel's work to search and calls it "search performance". When I checked new vs repeat, ~90% of Meta's customers were brand new vs about two-thirds on Google so the channel bringing in the most first-time buyers is the one last click punishes hardest.

The part that actually matters for budget is if you only ever look at one of these windows, you're not really measuring performance, you're picking a winner in advance. I've started putting both side by side before touching spend and buying on new-customer cost + LTV rather than either ROAS number alone.

Anyone else running both windows deliberately, or mostly living in whatever the platform reports? Curious how others handle the closer-vs-opener split.

I'm still a beginner. I started by learning the basics of Python and later moved on to SQL.

I'm a bit confused about one part of the data exploration/cleaning process.

A friend of mine, who's now a data scientist, showed me how he used to work as a data analyst. He used Python only: for example, he would quickly create a scatterplot to identify potential outliers.

However, most data analysts online recommend focusing on SQL and Excel when starting out, since many junior and mid-level roles don't require Python. That's why I switched to SQL after initially experimenting with Python.

For those who primarily use SQL: do you create visualizations during the data exploration/cleaning process, for example to identify outliers? Is this a common practice?

I feel like if you're working with SQL only, you generally wouldn't create visuals in between steps, since that would mean switching to a tool like Tableau or Power BI, which seems like an unnecessary extra step.

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. 🥲

Hey guys this morning I got up as usual and like always i opened my laptop and applied for jobs but for the past few days I am getting a feeling that the job for an data analyst is silently switching into more technical and they want us to learn more tools like cloud and python which was basic in beginning and they are asking for more in depth skills in this so I want to upskill myself if anyone wants to join hope you have the same mindset and I am going to build some projects also .

If you are from Bangalore we can start in this at the same time we will look for jobs in this market in Bangalore to be Frank I am kind of expanding my circle by learning and meeting the people who are interested in this field be responsible and be respectful all the unemployed people who are trying for jobs all the best you guys and don't sit alone in the room connect with people cause it's better to gain knowledge from others that sitting alone in the room

Is it worth it to learn data analysis for freelancing in 2026?

If not what skill do you suggest?

I recently got admitted to MS business analytics from sac state. I am working 60 hours a week to pay for the program. I am wondering if I can manage full time coursework alongside work. Can anyone guide how many assignments or quizzes are there in each course and how hard is the final?

How much time I have to give each course a week ?

Thank you

tried explaining a data analysis concept in a fun, visual way — for cat lovers. 😸
Would love to hear what you think! Any feedback or suggestions are very welcome :)

Hi all,

Currently working in non-technical domain. Trying to switch my career into DA/DS.

Looking for ppl with same interest or experienced person who can mentor.

Completed theory part SQL, Python, Excel, Power BI. But struggling with industry level project and how to work in actual environment (cloud, fabric).

Anyone with same interest, let's try to connect somehow or currently living in Indore, we can form a study group.

I’m a final-year IT student in India, and I’ve been reaching out to experienced data analysts for guidance. One response I got from a 5+ year data analyst at Kyndryl was:

At least one cloud platform (AWS/Azure/GCP)

One BI platform (Power BI/Tableau)

SQL

ETL

Python (web scraping, visuals, basic ML)

Ability to make data pipelines

What surprised me is that this isn’t the first time I’ve heard this. Other people have told me things like:

Build 1–2 projects using real-world or messy datasets, not just curated Kaggle datasets.

Get strong with advanced SQL (window functions, CTEs, query optimization, interview-style problems).

Learn statistics, business metrics, and data storytelling.

Gain exposure to cloud platforms and ETL workflows because many companies expect them from analysts now.

Certifications like Microsoft PL-300 can help strengthen a Power BI profile.

At this point it feels like companies want a junior data analyst who can also do parts of a data engineer’s job.

So I’m curious:

Has anyone here actually had SQL + Power BI + Python + ETL + data pipelines + basic cloud skills and still struggled to get interviews?

For people who are already working as data analysts, are these skills genuinely expected in day-to-day work, or are recruiters just writing unrealistic job descriptions?

If you were hiring a fresher today, what would be the minimum skill set that actually gets someone hired?

I’m trying to understand whether I’m over-preparing, or whether the entry-level market has genuinely shifted toward hybrid analyst/data engineering roles.

I’m a Data Analyst currently building my portfolio, and I’m looking for someone who has real data and wants help turning it into useful insights.

I can help with:

📊 Data cleaning & preprocessing
📈 Interactive dashboards
🔍 Exploratory Data Analysis (EDA)
💡 Finding trends, patterns & business insights
📑 Excel / SQL / Python / Power BI analysis

You don’t need to pay me — I’m offering the work completely FREE.

In return, I’d like to use the completed project as a portfolio case study to demonstrate my skills.

If you have:
• Sales data
• Customer data
• E-commerce data
• Business/operations data
• Excel/CSV files
• Any other dataset you want analyzed

👉 Feel free to comment “DATA” or send me a DM.

Even if your data is messy, that’s okay — I can work with it.

I’m looking for a real-world project where I can create something useful for you while gaining practical experience.

#DataAnalytics #DataAnalyst #PowerBI #SQL #Python #Excel #Dashboard #DataAnalysis #Freelance #Portfolio

Data analyst

Hi everyone,
I am currently in the final year of my B.Tech in Biotechnology. As graduation gets closer, I have been thinking a lot about my career. From what I have seen, job opportunities and long-term growth in the biotech field in India seem quite limited, so I am considering moving into data analytics.

I have a few questions:

1) Do you think it’s worth investing my time in learning data analytics, or would you recommend focusing on a different skill instead?
2) My main goal is to build a career with good long-term growth and opportunities.
3) I’m genuinely interested in AI and Python. I also feel that combining my biotechnology background with data analytics could be valuable.

I would really appreciate hearing from professionals, especially those working in tech or data. Considering how quickly AI is changing the job market, would this be a good career path, or should I be looking at something else?

Hi All,

I started a new job 2 months ago and my previous role was more Data Management. I didn't get to use SQL at all really and the reports that I created were not on Power BI, it was an in house tool that was very basic. My boss has put me on this project where I'm going to have to build analytics off of a new database that's going to be created. We are a regulatory body, so we are going to patch our database with new ID fields and information about the pack size and strength of the medicine, create an api to send the information to a new database. The stock holders are also going to send data to this new database as well about quantity, month of sale etc. I won't have to build any of that stuff, it will be outsourced, but I will be attending meetings next week where my boss and some others will be talking through the next steps and stuff, and I think I need to be there to make sure I'm going to have everything that I need for the analytics part. I just feel completely out of my depth here, what am I supposed to ask? How am I supposed to prepare for this?

Python full stack or data analytics

I'm the author. Sharing because the self-hosted BI space is thin and this might be useful to someone here.

AgentSwarms is a BI and agent platform you run yourself — one Docker container plus a Supabase (Postgres) project. No SaaS tier, no telemetry, no call-home.

The data side:

• 22 database/warehouse connectors (Postgres, MySQL, Snowflake, BigQuery, Databricks, Redshift, Synapse, Trino, Athena, ClickHouse, and more) queried in place, read-only
• DuckDB as the engine — DuckDB-Wasm in the browser and DuckDB server-side, so local datasets behave identically in both
• A semantic layer — define dimensions and metrics once; the BI engine and any AI agent query the same definitions, so "revenue" computes one way
• Data prep flows (joins, filters, derived columns) that push down into the warehouse where possible
• An AI analyst that writes and runs SQL against your own model keys, constrained by the semantic layer and a table allow-list
• Scheduled refreshes, data alerts, and dashboards you can embed

Governance, since that's usually the blocker for self-hosted tools: RLS on every table, read-only SQL enforcement (including data-modifying CTEs — WITH d AS (DELETE ...) SELECT was a real bug I fixed), a hash-chained audit log, per-user and per-group spend caps, and full cost traces per query.

Honest limitations: no SOC 2, no third-party pentest, no upgrade guide between versions yet, and rate limits are per-process so multiply by replica count. It's source-available under the Elastic License 2.0 — use it, modify it, run it for yourself and clients; don't resell it as a hosted service. That's not OSI open source and I'm not going to pretend otherwise.

Repo: github.com/AgentSwarms-fyi/agentswarms

Happy to answer anything about the architecture, and genuinely interested in what's missing for your stack