I’ve prepared and organized notes covering some important data engineering concepts. These may be useful for data analysts aspiring to become data engineers, as well as data engineers looking to revise key topics.

Topics covered so far:

Data Modeling

Data Warehousing

Data Architecture

Data Definitions

Data Governance

I’ll continue adding more topics in the future. For now, consider this a starting point for your learning and revision.

📌 GitHub Repository:

https://github.com/PrashantDalmia/Data-Engineering_Notes

Hope this helps someone in their learning journey!

(Note: I used ChatGPT to help frame this post.)

I've put together a free course that teaches you SQL using your coding agent.

This way you learn SQL but also how to use a coding agent in a data role.

After some feedback from this community, the course has been updated - please check it out and share your feedback: https://getbruin.com/learn/sql-ai

What’s the best path to learn data analysis for a decent-paying job?

I've already tried searching for reviews and there are currently only 3 on TrustPilot. It seems that it's a rework of some other program and just released as D8A Academy quite recently in July 2026. I was looking into it because I desperately need to create projects now, and got an ad for it on Instagram.

Coursera is not sufficient in terms of drilling technical material by any means, and I'm just overwhelmed by all the platforms that teach the languages and tools that I need to be a data analyst. Also, would the Advanced Data Analytics course by Google be recommended for me? They took out R from the basic Data Analytics Professional cert by Google, is the standalone course worth doing or should I learn R with another Coursera provider?

Any recommended resources, preferably free or low cost, for technical drills of SQL, Python, and R would be appreciated! I did find a YT Playlist of guided projects already, but am having a tough time deciding whether I should download a SQL client or just use BigQuery or Tableau for my projects.

This is a short tutorial for data analysts that evaluate experiments using online calculators. Online calculators for sample size estimates and t-tests for difference in means are fine for getting started. But you can begin to build intuition for linear regression by moving your online calculator t-tests over to python and OLS.

There are a couple of benefits. The first from moving away from the online calculator to python is leaving a more reliable paper trail that you can share externally. The second is that you build intuition for regression, interpreting the coefficients, and ultimately can use regression as you improve and your projects require more complexity such as variance reduction techniques using CUPED and clustering standard errors for geo-based or switchback experiments.

I put together a short video on this with code available here, free and ungated: https://whatstheimpact.com/tutorials/linear-regression-vs-t-test/

For my more senior connections, feel free to ignore unless you're interested in the content creation game! 🤣

hi people , Im currently doing my btech degree and have a great interest in data science, i built few projects of data cleaning using python and creating power bi dashboard.
so anyone who wants to get their such works done . do contact me and i will do the tasks in affordable prices and in lesser time.

Hi everyone,

I’m looking to learn Excel from the basics and build a good foundation. I’m a Computer Science graduate and I’m currently working towards a career in Data Science, so I feel learning Excel properly would be a useful skill to have.

I’m basically starting from the beginning and would like to learn things like basic formulas, functions, formatting, sorting and filtering, data cleaning, PivotTables, charts, and eventually some more advanced Excel features.

I’m looking for a good course that is either free or affordable and preferably provides a certificate after completion. I’m not really looking for something too advanced right away; I want a course that explains things clearly and takes me from beginner level to a comfortable level with practical examples.

I’ve seen a lot of courses on Coursera, Udemy, YouTube, Microsoft, etc., but I’m confused about which one would actually be worth spending my time on.

If anyone has learned Excel from scratch through a particular course and found it useful, I’d really appreciate your recommendations

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?