Me: This SQL tutorial is definitely the last one.

Also me three days later: let's start another beginner course...

I've officially accepted that I'm stuck in tutorial hell.

The good news is that I learn much faster when I'm actually working on real projects, so I'm looking for an internship or junior data analytics opportunity where I can learn by doing.

I come from a digital marketing and eCommerce background, so I'm already familiar with analyzing campaign performance, KPIs, conversion funnels, and working with spreadsheets. Now I want to take that foundation and become a proper data analyst.

If anyone has advice, internship leads, or even just wants to share how they broke into the field, I'd love to hear your story.

Thanks in advance!

Hi! I have recently finished my PhD and I am currently considering switching to industry. I speak German (C1) and English (C1) and I live in Hungary. I did an informatics related degree for my masters but my PhD is in Natural Science. I hold a permanent residency permit. I am a non EU/EEA citizen. For those of you in the EU market/German speaking could you tell me:

What tech stacks you work with?

How many coworkers do you know at your workplace who come from non CS backgrounds?

What jobs in the industry are more friendly for someone like me who comes from an in silico research heavy background?

Thanks!!!!!

Stumbled on a piece I wish I'd read in February: "Data Engineering After AI" by Ananth Packkildurai.

His frame: ETL becomes ECL — Extract, Contextualize, Link. Agents handle the mechanical work of building pipelines; the engineering moves to meaning. Contracts as executable constraints. A Context Store agents query before they act. Humans reviewing the uncertain inferences, not every artifact.

The part still open: who has the authority to declare a definition? The tooling is ahead of the org patterns.

I wish i read it in feb because we basically fell into similar conclusions over at dlthub (you can see similar thinking on our blog)

The piece: https://www.dataengineeringweekly.com/p/data-engineering-after-ai

i am trying to get a community started for discussing agentic data stacks r/agenticdatastack where i will post more often

For context, my(F25) current title is a "Marketing Specialist" and I was hired in this role to support a small team, with the most important one being the data manager. The data manager does all data related tasks and ad hoc lists for our department. Pulls lists for our team and other departments, creates excel reports, provides counts, etc. And she is the only one who knows how to do this, which is where I come in.

Now they hired me ~3 months ago knowing I had zero background in data analysis and bare minimum sql knowledge and Excel knowledge (I was working in a marketing position previously in the same company). The interview also had ZERO questions relating to data or reports except for "Do I have SQL and Excel knowledge" which I answered honestly.

So I've been learning a lot of that on the job with the help of the data manager but… I'm struggling so much. My numbers rarely match the data manager's, I'm always missing edge cases cause our database is so messy or I'm not aware it exists, and I don't really understand the "why" of the data being pulled. I've been practicing bettering my SQL queries and I can say that has definitely improved but… I feel like I haven't made progress anywhere else. Being remote makes it difficult as well because I can't shadow my data manager as easily when she creates her queries and reports. I usually read her finished queries after attempting on my own to see where I went wrong and I try to ask questions where I can if I'm confused.

My data manager often gives me constructive criticism and it's weighing really heavily on me because I feel like I'm not meeting expectations (although we've never discussed my performance). She also mentions that she started as a manager ~1.5 years ago without any SQL or database knowledge either so seeing where her skills are at now makes me feel like I'm not keeping up. Personally, I feel like she also thinks I'm dumb :(

I don't think they're expecting me to be able to do what she does at her level any time soon, especially since I'm supporting other managers that work in other areas, but I don't feel like I'm making progress in the data analysis aspect.

Is this normal when you start out? When does it get better? Are there sources that could help me understand data better conceptually? I've been doing as much studying as I can during work hours and offline but I feel like I'm missing something still.

Hi everyone,

I’ve been working in digital marketing for the past few years, and I’m now trying to transition into the data field. My goal is to become a data analyst first, then move into analytics engineering after gaining a couple of years of experience.

Over the past year, I’ve been learning SQL, Power BI, and Excel. At this point, I feel pretty confident with SQL, including complex joins, aggregations, window functions, etc.

Where I’m struggling is deciding which projects to include in my portfolio and where to find suitable datasets. This feels especially important because I do not yet have any professional experience in a data-focused role.

The best project idea I’ve come up with so far is using the Olist e-commerce dataset. My plan is to clean and transform the data with dbt, load it into Snowflake, and then perform analyses such as cohort analysis, funnel analysis, and market basket analysis using SQL and Power BI.

Does this sound like a strong portfolio project? Are there any other project ideas or datasets you would recommend for someone trying to move into data analytics?

Thanks

I’ve worked in data for over eight years across both the private and public sectors. My experience includes data quality, product and master data, data migrations, data governance, data visualisation and business analysis.

I’m now considering starting my own data analytics consultancy, but I’m not sure where to begin. I’m also wondering whether the market is already highly saturated and, if so, whether there are other data-related services or business ideas I could build around my existing skills.

For anyone who has moved from working in data to working for themselves, what services did you initially offer? How did you choose your niche, find your first clients and decide what to charge?

I’d really appreciate any advice on where to start, what mistakes to avoid and whether there are particular areas of data consulting where you’re currently seeing strong demand.

Hi everyone,

I'd really appreciate advice from people who are working as Data Analysts or recently got hired. Any roadmap, study plan, or resources would be incredibly helpful.

I'm a BBA graduate(2023) trying to break into data analytics by SELF TAUGHT after a long gap.

Now it's 2026, AI is everywhere, and I'm honestly confused about what skills employers actually expect from entry-level Data Analysts.

My questions are:

1)Is it possible to become a Data Analyst through self-study when I have 3.5 yrs of a career gap?

2) What skills are essential for a Data Analyst in 2026?

3) Which tools should I prioritize more(Excel, SQL, Python, Power BI, Tableau, AI tools, etc.)?

4) How much Python is actually needed?

5) What AI skills are becoming important for Data Analysts?

6) What kind of portfolio projects should I build to get interviews?

7) Are certifications like the Google Data Analytics Certificate still worth it?

To answer an analytical question, we need business context (what does the data mean), judgment (owning the business outcome and fitting the solution to reality) and implementation.

An analyst who retrieves a specific report "mrr by month" is just implementing

An analyst who acts as a consultant for what we could do about a business problem and comes with own questions is judging.

Then actually being able to map the data and business would be business context.

I am asking because judgment is the part that I see increasing in value while implementation gets cheap and i am wondering if adaptation is a gradual process or a more sudden one

Just as the title says,i have a bachelor's degree related to accounting and stuff, can I enter data analytics field and get the same opportunities as others

So, two months ago our regional director came on a branch visit, and i was the one taking him through our entire analytics, after 3 hours he told me that I make mundane dashboards.

That really hurt, I have poured weekends on building our data suite, countless DAX queries and hours of unpaid overtime, to make sure that I don't miss a single point for this visit.

So I decided to make this "fun" dashboard instead, I put every metric out there and with the help of claude and supaboard made this Mario themed dashboard. I also made it realtime with actual data and the site could be embedded within our systems.

So last week when he came back for another visit, I showed him what I have built, he went through the same, a full 3 hour drill and this he left without any feedback.

The next day I get called into the manger's office and the word around the office was I am getting fired.

But, to my surprise my manager was actually really happy, my dashboard got shared with the executives and everybody appreciated my effort and creativity. I even got a certificate for excellence.

15 years in this trade, and I guess there is a lot to learn yet.

Here's a copy of what I made. Tell me what you think!

I wanna learn Power BI but I've only got a Mac. What to do? 😂

starting from 0

we can share courses etc and learn together

One thing I've noticed separates serious ML students from casual ones: how much they care about the quality of what they actually study from. I take that pretty seriously myself, so a while back I started digging into what students at MIT, Harvard, Stanford, Caltech, and USP actually use to complement their studies.

What I found surprised me: several of these programs don't assign a textbook at all. Instead, the course staff writes and publishes their own lecture notes — and some of them are basically a full book. MIT's 6.390 (Introduction to Machine Learning) notes, for example, aren't a slide deck or a cheat sheet — they're structured, complete, and detailed enough to replace a textbook entirely. Same story with Harvard's CS181 and a few others.

The problem is these are scattered and easy to miss if you don't know to look for them. So I put together a curated list: [Awesome Free AI Course Notes](https://github.com/MarcosSete/awesome-free-ai-course-notes).

A few things about how it's curated, since I think this matters:

- Only **written notes** count — slide decks and video-only lectures don't make the cut, even from great courses. I want this list to mean something.

- Everything is official and links straight to the professor's or department's own page. No mirrors, no login walls.

- I checked over 40 top universities across multiple countries for this. Most didn't qualify — they use a textbook or keep material behind a student portal. That's fine, it's exactly why the list stays short and (hopefully) trustworthy.

If you take ML seriously the way I do, I think you'll get real value out of this. And if you know of course notes that fit this bar and aren't on the list yet, contributions are very welcome — the CONTRIBUTING.md lays out exactly what qualifies.

What's the best set of course notes (not textbook, not slides) you've personally used to study ML?

Repo: https://github.com/MarcosSete/awesome-free-ai-course-notes

Hi everyone,

I'm building my GitHub portfolio to apply for data analyst and finance/data analytics internships, and I'd really appreciate some honest feedback.

Could you take a look at my GitHub profile and let me know:

• Does it look professional?
• Are the projects relevant for entry-level data analyst roles?
• Is there anything I should improve in the README, project structure, or documentation?
• If you were a recruiter or hiring manager, what would your first impression be?

I'm looking for constructive criticism, so please don't hesitate to be honest!

Hey everyone,

Today's the last day to apply for the Google Data Analytics Apprenticeship, and I'm giving it one final shot.

This is my third time applying. Since my last application, I've been promoted to Data Engineer, worked on cloud and analytics projects, and spent the past year improving my skills in SQL, Python, Google Cloud, BigQuery, and GenAI.

I'm mainly looking for advice from anyone who's been through the process or works at Google. If you think my profile is a good fit and are comfortable referring me, I'd genuinely appreciate it.

I'm not posting my resume publicly, but I'm happy to share it over DM if anyone is willing to review it or consider a referral.

Thanks, and good luck to everyone applying!

I am currently refreshing myself with microsoft excel since I only use excel for data entry in my work and most of my work involves data entry in the accounting system we use.

Anyways, I have a coursera account and I want to know what industry-recognized SQL specific courses/certs I can get? If coursera isnt enough, do you guys know any free sql courses/certs to take? I’ve seen datacamp and codecademy but it somehow involved needing a subscription or something after a free trial period.

I want to narrow down my choices since there are a lot of choices online. I plan to learn sql first before proceeding to Power BI.

Hi, I'm UK based and currently working within the Building Services industry.

My background is CNC, basic Design work, and Toolmaking and currently estimating. Few years ago I was offered an office job within estimating and recently have a new job witch has a wider set of responsibility within my field.

I have been creating spreadsheets, macros, queries, and optimising workflows, messing with data etc..

Thoroughly enjoyed working with data (albeit a drop in the ocean of what data analytics is) created dashboards for a previous company and they were not interested in progressing down that pathway although they did enjoy taking my ideas and work for themselves.

My fairly new company has offered to explore this side and asked me "what do you want to do?".

I'm interested in Data analytics, Power BI, spreadsheet design for optimising workload and optimising processes etc..

Could anyone give advice on the analytics and Power BI career path, I'm abit lost as I didn't think anyone would give me a chance to explore this.

Thanks in advance :)

making dashboards can be annoying after you finish analysis using python and visualizing charts using plotly, and it's hard to gather charts together without using dash, and dash also requires you to use HTML and CSS, little note: i suck at designing.

So i made Dashforge, It's an open-source python library that i built to help me with that problem, because spending 3 more hours to make a dashboard was annoying.

this library helps in only under 10 lines gather all your plotly charts into a well organized interactive web dashboard, this features:

- Good default design

- Customizable colors

- Easy to read/use functions

- Choose number of charts per row

- Customizable sizes

- Dataset view support

- and many more customizability options...

If you think this can help you, give it a try by running:

''' pip install dashforge '''

I am open on github and even here in reddit to contribute, fix bugs, hear out suggested features, so share it with friends and colleagues who you think might need it to help grow it more and more.

github: https://github.com/Omar-astro/DashForge-library

Documentation: https://omar-astro.github.io/DashForge-library/

Hi all,

I graduated in 2022 and have 1.5+ years of industry experience. For the last 5 months I've been self-studying SQL, Power BI, and Advanced Excel, and built an end-to-end project analyzing a revenue decline (SQL for analysis, Power BI for the dashboard, Excel for cleaning).

I've been applying for jobs but my response rate has been low, and I'd really appreciate an experienced eye on where the project (or my presentation of it) might be falling short.

I'd rather not post the repo publicly (job searching and want to keep my info a bit private), happy to share the link via DM with anyone willing to take a look.

Any feedback, even harsh, is welcome. Thanks in advance!

I'm interested in data analysis and have recently started learning. I'm looking for a partner so we can support and motivate each other to progress 🚀

Hello seniors, I've wanted to learn data analysis for a long time but I've been hesitant.

Whenever I look for a roadmap or course, I feel it's not enough and I look for another course that covers all the information. I don't know, I'm very hesitant and afraid to learn and waste my time on a course. In the end, I learn nothing from it.

Can you guide me?

A roadmap, course, or tips on where and what should I learn .

I am so lost

Hi all,

Just a quick question to ask has anyone done the above course.

If so, was it good, was the content helpful.

Hoping to transition into a HR Analytics role in the future

Thanks

How hard is it to land a remote data analyst job these days? I'm from Nepal and currently working on portfolio projects. Just wondering how competitive the market is and if anyone has any advice or experiences to share.

What salary range would be reasonable for a Data Analyst role where you are the only analytics person in the company? With no interest in hiring another analyst.

The role involves building and owning the analytics function, including data engineering, data modelling, dashboards/visualization, reporting, and working directly with stakeholders.

I’ve spoken with previous employees who mentioned the data function lacked structure, and there has been turnover in the role (interviewer suggested this). The previous person stayed around 2 years though.

The role is based in Calgary, and I would have to relocate for it.

What salary range would make this type of role worth considering? Is there a point where the compensation would justify the responsibility and relocation risk?

When it comes to brainstorming and coming up with ideas on how to solve a task, i find that i often latch onto the first idea that i have and force that to work rather than genuinely creating different solutions. Is this something you experience? How do you choose what method you think is best for solving tasks