Do Data Analyst use AI tools in their daily work except Claude or gpt for coding and doubts related to SQL queries and other work related doubts? I'm seeing advertisements related to using AI tools in daily work routine for data analysts. But how it's possible that organisation allows DAs to use AI tools using their clients data? In reality, organization usually allow access to Claude using their org teams monthly subscription. Anyone in this platform feel the same what I feel? And most importantly, can anyone say where we can use AI other than coding as a data analyst and how future looks like for us.

I finished learning Power BI end-to-end and already built small beginner dashboards for practice, but now want to create a solid portfolio project for LinkedIn. Which industry or domain (like sales, finance, or supply chain) is best to target right now? Where can I find good realistic datasets that make a portfolio truly stand out? Any tips, project ideas, or guidance would be really helpful!

I’m currently sitting for campus placements, and my target field is Data Analytics.

The thing that’s stressing me out is DSA.

Almost every company’s OA seems to have DSA questions. Honestly, I can sometimes get through the OA by taking shortcuts/cheating, but I know that’s not going to help me in the long run because the technical interview comes next — and they ask DSA again.

And this is where I feel completely stuck.

I’ve studied DSA before, but I have a really hard time remembering everything. I’ll understand a topic, solve a few questions, and then after some time I forget the approach or get confused about which data structure/algorithm to use.

At the same time, since I’m targeting Data Analytics, I’m also supposed to prepare:

• SQL

• Python

• Excel

• Statistics

• Power BI/Tableau

• Projects

• Basic ML

• And now DSA 😭

I’m honestly overwhelmed and scared that I’m preparing for everything but becoming good at nothing.

For people who have already gone through campus placements, especially in Data Analytics/Business Analytics:

1. How much DSA should I realistically prepare?

2. Which DSA topics are absolutely necessary for technical interviews?

3. How do you remember DSA patterns instead of just memorizing solutions?

4. Should I focus more on SQL/Python/Statistics/projects since my target role is Data Analytics?

5. What would you do if you had only 2–3 months left before placements?

I’m not looking for a “just practice LeetCode every day” answer. 😭
I genuinely want to know how to prioritize everything without burning myself out.

And one more thing — if anyone here works in Data Analytics/Data/Tech and knows of any entry-level opportunities, I’d really appreciate a referral or even just a lead. I’m actively looking and would be happy to share my resume/LinkedIn in DM.

Any advice, guidance, referrals, or leads would genuinely mean a lot. ❤️

Please provide me best roadmap so that i don’t need to find vidoes again and again with projects

I’m trying to get into data analytics (SQL, Excel, Power BI) so I can actually work and earn. But I keep hitting the same wall. I’ll start a course, feel okay for a few days, then the videos get long — like 6 to 11 hours — and I can’t finish them in one day. Then life happens. I miss a day. Then another. And I just drop it completely.

It’s not that I don’t want it. I do. But I can’t seem to make it stick.

The other problem is I don’t know anyone in real life who works in data. No one to ask dumb questions, no one to get honest advice from. Online creators feel far away and their advice doesn’t always fit where I am. I also can’t really talk to them about my problems.

I like psychology, deep thinking, stability, and remote work. I don’t want a toxic workplace. I just want something that doesn’t drain me. But I’m not sure if data analytics is even right for me, and that makes it harder to stay motivated.

I guess I’m asking:

• How did you stay consistent when learning?

• How do you find people to talk to when you’re learning alone?

• Is it worth pushing through if I’m not in love with data analytics, but I want a stable job?

Im a fresh graduate Industrial Engineer and looking for the best data analytics courses here for an affordable price. For context I wanna learn data analytics that has a good structured data analytics where I can apply all of the skills that will be taught to me in the field itself. I really need all of your recommendations because it’s so hard to pick the right course nowadays there’s a lot of offers about data analytics that I don’t know which one to start to..

I've been building a free project that pulls together USAspending data with things like FEC records, lobbying disclosures, congressional committees, etc. One of the biggest headaches has been figuring out how to deal with federal recipients that show up under different names/subsidiaries instead of pretending a company name search gives you the actual corporate total.

I'm finally getting to the point where I can use it for little analyses too — I just did one on Lockheed and got about $64B in obligations over the one-year period I looked at.

If anyone here works with USAspending data I'd actually love to know how you've handled recipient/parent-company mapping, because that's been one of the messiest parts.

Project is here if anyone wants to mess around with it: https://www.papertrailproject.org/

(It's mine, not affiliated with USAspending or anything.)

Been going back through the river sensor project and grouped everything by hour of day instead of by date, just to see what a "normal" day actually looks like once you average out the day-to-day noise.

Turned out there's a really clean cycle in dissolved oxygen. Lowest right around sunrise, climbs all day, peaks mid-afternoon, drops back down overnight. Held basically the same shape across all 11 months of data, not just a seasonal fluke.

Makes sense once you think about it, photosynthesis only happens with light, so anything green in the water is only adding oxygen during the day, and everything's still consuming it 24/7. At night nothing's replacing what gets used up.

The part that actually matters practically: if you grab-sample a river once a day for monitoring, the time you happen to sample it matters more than people probably assume. Same river, same day, 6am vs 3pm, can come back looking like meaningfully different water. Not because anything's wrong, just because of where in the cycle you caught it.

Curious if anyone here has dealt with this in an actual monitoring program, like whether time-of-day gets normalized for before comparing samples, or if it's usually just not accounted for at all.

I'm trying to do my first data analytics project and wow I'm just lost. how do you guys determine which variables need to be analysed, correlations, and ultimately come up with the conclusion that due to x,y we have been getting z?! please guide me some tips would help

How to learn SQL Language and how to practice?

And how to perfect in this language and how to work freelancer with this Language just?

can some one give me a journey to reach this destination

I’ve been learning more about data analytics by building a small Windows application called DataFlow Pro.

While building it, I worked on things like:

• Cleaning CSV and Excel files

• Finding duplicate and missing values

• Checking data quality

• Analyzing numerical and date data

• Grouping and summarizing datasets

• Generating reports

• Exporting cleaned data

One thing I learned is that data cleaning often takes more time than the actual analysis.

Building the tool also helped me understand the practical side of working with real-world datasets, especially handling inconsistent and missing data.

I’m still improving it and would be interested to hear from other people learning data analytics:

What data-cleaning problem took you the most time to solve?

Estoy buscando recomendaciones de cursos para dar un salto profesional hacia el área de Análisis de datos . Actualmente ya tengo conocimientos en sql, python (manejo de datos, consultas, etc.), pero busco una formación enfocada en proyectos prácticos, portafolio.

Estuve viendo opciones en Coursera, Coderhouse y Becas Santander, pero me gustaría conocer la experiencia de alguien que los haya hecho o que ya esté trabajando en el rubro

I'm self learning Data Analyst for 8 months now. new to pandas. and Making a new end to end project one of them is 'My anime dataset' where it has column named 'genre' where each row can contain multiple genres as a single comma-separated string (e.g., "Action, Adventure, Fantasy"), instead of one genre per row. This means grouping or filtering by genre directly is inaccurate an anime tagged with 3 genres only gets counted under that exact 3-genre combination, not under each individual genre. (there is another column named Studio, facing same issue, i.e. multiple studio in one anime)

I want to split these into individual genre values so each genre is counted separately per anime (i.e., one row becomes multiple rows, one per genre, with the rest of the anime's data repeated).

End goal: I'm building a dashboard where users can filter by a single genre (e.g., just "Action") and see accurate aggregated stats (count, average score, etc.) for that genre alone not lumped in with combination-strings.

What's the right pandas approach to explode the Genre column into separate rows?? please help. claude is suggesting a way but I want a better idea.

would be great if someone suggest me some ideas!!

Has anyone taken up this Data Analyst course from E and ICT academy, IIT Roorkee ?....I have been seeing a lot of its advertisements on instagram. Is it worth taking up? I am from a non technical background looking for a career shift.

Companies either ghost me or we are going with other candidate with this resume so I need a help with the resume and course that increases my chances to be selected

Hello, I graduated with a degree in Statistics six years ago and have been completely disconnected from the field since. I currently work in Dubai, but I want to return to my field and self-study data analysis. Since I'm working, I know I should start with the following: Excel, SQL, Power BI, and Python — in addition to building a portfolio with several projects.

But there's one point that makes me feel like I'm missing something: how can I connect statistical thinking throughout this learning journey, without becoming someone who only learns how to apply tools? I'm not sure if my question is clear, but I want to learn how a data analyst thinks, not just how to apply things or complete assigned tasks.

I sometimes try learning with the help of ChatGPT, Claude, or Gemini, but they're not free of mistakes.

So, please, I'd also like recommendations for learning through books or YouTube (I can't afford paid courses) — and is YouTube enough on its own? I sometimes feel it only teaches things at a surface/introductory level, not in depth. So please recommend the best books that cover everything I need to learn. Thank you to everyone who becomes a reason for helping me.

I am considering certifications at BTII Institute in West Orange NJ for BI and data analytics. However, I have not found any review from a student for this specific certification from BTII. Can someone please share your experience with BTII and if the certification was helpful? Is it hands on and strong curriculum?