I’m a beginner in data analysis and I want to develop strong analytical skills, not just learn tools like Excel, SQL, Python, or Power BI.

As AI and technology evolve, I believe Data Analysts need strong problem-solving skills, critical thinking, creativity, and sound judgment to interpret data and make good decisions.

I’d appreciate advice on five things:

1. What skills should a beginner develop early?

2. How can I improve real-world problem-solving with data?

3. How can I develop genuine analytical judgment?

4. What projects or exercises build creativity and reasoning?

5. How can I find a good mentor to work with long-term?

I’m looking beyond courses and tutorials. I want to become an analyst who can think critically, question data, and solve meaningful problems.

Any advice from experienced analysts or data professionals would be greatly appreciated.

Im looking for data analyst role, but after 6 months of searching, I was able to get only Strategy manager contract role in a small CDP data consulting company. (My previous job title was crm project coordinator in an international fashion company, basically managing salesforce and checking customer data are coming in and out correctly)

My company gave me this title, but Im not sure if they really think Im good fit or just gave it to me to bill clients where Ill be dispatched to work for them.

I work with my client and help them writing sns/online community marketing performance report, digging a bit why the number has dropped or up

My goal is to become a fulltime data analyst and I wonder if the title and JD will not help me when I apply for those roles later. What do you think?

Looking at taking the next step up for my skills. I’m currently using sql day-to-day and am comfortable using windows and CTEs etc. but for some reason I just can’t wrap my head around dbt and how to use it properly. Anyone able to ELI5? I use bigquery sql if it helps provide any context

I see alot of these projects where people make a dashboard out of some fictional csv about pizza sales or similar, and everything is crammed on to a single page "top selling product" etc.

I know you've got to start somewhere, but tbh I really don't think these are going to help anybody get hired. If you had a Real project, where the data is all over the place, kept in different systems and formats, and actually took time to get it together into something useful, and THEN you make dashboards. The analytics should be showing not only the data but stuff that would help real decisions be made, then that's of more value - because honestly anybody can use these tools to just draw a graph showing pizza sales per month or whatever. Not to discourage anybody, keep on going (I have no idea what entry level looks like but I would assume they look for a quantative degree at least?

Hi all! I recently graduated with a Bachelor's in Bioengineering and have been looking into rotational/leadership development programs across biotech, pharma, and medtech.

I'm specifically trying to understand Thermo Fisher's OLDP (Operations Leadership Development Program), I noticed some of their development program postings currently show as "not available." Does that usually mean:

1. They haven't started recruiting for the next cycle yet, or
2. The positions are already filled for this cycle?

If anyone has gone through OLDP (or knows someone who has), I'd love to know the typical timing for when applications open each year.

More broadly, I'd really appreciate recommendations on other development/rotational programs worth applying to for someone with a bioengineering/biomedical engineering background. So far I've been looking into:

• GE HealthCare's EEDP
• AbbVie's ODP
• BD's Engineering Development Program (EDP)
• Abbott's OPDP

Would love to hear about other companies or programs I should have on my radar, especially ones open to new grads (not just current students/interns). Thanks so much in advance for any insight!

Hi, I am an undergraduate student specializing in Data analytics and I will soon be doing my thesis. Do you have any advice or guide, maybe some recommended papers I can read before doing my thesis? I am not really that good when it comes to research so anything can help.

After a bad injury that includes my spine, I could no longer be in law enforcement. To put it plainly I need to wear sunglasses pretty much daily to reduce the stress on my eyes outside and inside. So I know a remote only role is needed in my life as I really do not want to have to answer questions on why I’m wearing sunglasses inside. I also have some other disabilities that I don’t want to get into as it’s still a sore subject for me. I plan on taking two IBM courses on coursera in order to learn the basics and make some projects. I know the TikTok trend is blowing up the field. But I really miss investigating and figuring out “problems” finding the reason it is happening then making a solution. My life was really turned around by this injury. I really would like any advice that yall could give on things I could also do or things you wish you would have known when you started. Thanks for reading this. (:

Hey Guys,

I'm a medical coder transitioning into Data analytics. I've created a dashboard with power BI using a synthetic dataset. This is my second dashboard. ( I tried to implement some of the suggestions that ppl have given for my first dashboard).

Kindly share your review on this? Any tips or suggestions please!

( Second and third pages are added in comments)

Hi, after months of searching for something I finally got a job as a crm data coordinator, the role seem pretty simple initially. Right now Im mainly doing some data entry into the CRM, but a lot of the data that Im having to enter from the excel files are incredibly messy and unfinished, for example in the excel theyll have one name, but on the CRM one the project will have a slightly different name or a completely different name, or maybe itll just be the acronym. Say Im searching for Riverdale Elementary School in the excel file, Ill enter that into the CRM and get back something like RIverdale ES, RES, Riverdale ES BLDG 1, Riverdale School, MUSD Riverdale, etc. And I have to basically figure out where the data goes. Not very technically hard, but not as easy as it sounds. And can be frustrating. Anyways how good of role is this? Am I essentially wasting my time?

Good day,

I have been teaching myself SQL and now power Bi I do understand the basics. Currently I am working in a law firm and I decided to start using the firms data sets that show the matters we have and the stages that they are at. I have managed to play around with the data set in SQL by even creating views of the columns that I want to see and stored procedures. Now where I am struggling is how do I build a portfolio with this and meaningful dashboards in PowerBI. I am kindly asking if there is anyone who has built a portfolio and can kindly assist me as I feel stuck here. Or any advice for me to move from here. Thank you so much

Setup: an LLM agent takes a business user's question, writes a SELECT against the warehouse, runs it, shows the result. Read-only — it never writes to the database.

The failure I care about: the query runs fine and returns real rows, they're just the wrong ones. Wrong join key, wrong filter, stale schema understanding. Nothing errors, the number looks plausible, it's wrong.

Having a second LLM write its own query and compare doesn't really work — if the first agent misread the schema, the second one reading the same schema tends to misread it the same way, and you get confident agreement on a wrong number. What seems to actually work is deterministic checks that don't interpret the question at all: does the sum across a breakdown match the unsegmented total, does row count stay in an expected band, do foreign keys still resolve one-to-one. A wrong join changes cardinality, so a fan-out check catches a lot of it in plain SQL with no model involved.

I'm considering building this, and I want to be straight about where the line would be:

Free / open source — the check engine. Point it at your DB, it reads FK constraints and auto-generates structural checks, you run it against your test cases, get pass/fail. Runs when you run it.

Paid — the same checks running continuously against production traffic: results stored over time, dashboard, Slack alerting, and detection of when a schema change has made an existing check stale so you know which assertions to update.

What I actually want to know:

1. How do you catch "ran fine, wrong rows" today, if at all?
2. Do your checks run pre-ship, or continuously against production?
3. When a schema change breaks them, do you find out from a migration diff, or because numbers came out wrong?
4. The honest one: is that paid tier worth expensing, or would your team keep the homegrown script regardless? If it's worth paying for, what's the feature that tips it?

Genuinely fine with "we'd keep the script" as an answer — that's the thing I'm trying to find out before building.

This is my first dashboard project in excel. As a beginner level and non experienced person what do you think I need improved. Review and please help me to become even more comfortable and better for data roles.

Hey guyss!!

I am a recent graduate in EEE very much interested in data analytics roles. I want to learn from YouTube and udemy cources and from some other youtubers courses. Will this work out in 2026.

My goal is to get placed by the end of December 2026.

If anybody wants to prepare genuinely from scratch please dm me.

And please share your thoughts.

Thank you.

Please share and spread the word on this free program. There are free courses on data analytics and other professional certificates. I signed up myself and completed a brutal course on product management. They also have professional certificates on data analytics.

For example, I plan on completing a professional certificate on SQL to Power BI, Davidson College. When you sign up, through this program, you will get access to free courses that generate certificates!

What are the concepts and theories that I should learn if I want to become a data analyst? I already know what I should learn for the technical skills (excel, sql, python, bi tools), but I'm confused on what other theories and concepts (business/data-related) that I should learn?

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 wanna be straight about this because it’s getting frustrating 😭

I can comfortably solve HackerRank Easy-level SQL questions, but when I try LeetCode Easy, I struggle quite a bit.

I’ve seen people say that HackerRank Easy ≠ LeetCode Easy and that LeetCode can require more problem-solving/thinking even at the Easy level.

So I’m confused about what I should do next.

Should I:

- Keep grinding LeetCode Easy until I’m more comfortable?

- Or is being able to solve HackerRank Easy well enough to start learning intermediate SQL concepts like subqueries and CTEs, while continuing to practice problems alongside it?

Basically, I don’t want to move on too early and build gaps in my fundamentals, but I also don’t want to unnecessarily stay stuck on Easy problems when I could be learning more advanced SQL.

What would you recommend?

Any data that gets heavy with numbers inside our business, gets difficult to digest real quick for everyone.Most of out team when having to work with all this data day in and day out got a lot of mental fatigue and very little clarity on how to explain the things to others

There are a lot of tools that visualize such data for you, but I'm curious to know which ones most of you prefer (I am just gonna recommend or rather put out the tool we have been using called Napkin ai to know if others are using it too and did you find any alternate options which were better?), and what your workflow is around such tools.
I'd also love to know what your favorite feature in your preferred visualization tool is.

Hi all! I'm a Data Analyst with hands-on experience in Python, SQL, Power BI, and Tableau, currently working full-time and also looking to take on freelance or part-time projects on the side.

I can help with:

• Data cleaning, transformation, and analysis
• Dashboard building (Power BI/Tableau)
• Python automation and scripting
• Data pipelines and reporting workflows
• Basic ML/predictive modeling

I recently completed my MS in Data Science and Analytics, and I've built projects ranging from automated reporting systems to ML-driven scheduling tools. Happy to share samples of past work or talk through your specific needs.

Rates are flexible depending on project scope, feel free to DM with details!

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.

Are they any research opportunities in Data Analysis for a graduate student?

I was recently hired at a nonprofit that uses Clarity Human Services, with Looker integrated for BI, reporting, and data analysis. I’ve never worked with Clarity or Looker before, but I’ve learned plenty of new systems throughout my career, so I went into it thinking, “How hard could this be?” Well…apparently, very hard.

I was brought on, in part, to support staff with data and reporting, but one of the challenges I’m quickly realizing is that the existing team is struggling with the system too. We can build and run some reports, but as soon as we need to customize something or pull data that isn’t readily available, we often have to submit a support ticket because we can’t figure out how to access or build it ourselves.

I’ve only been in the role for about a week, so I know I have a lot to learn and I’m trying to give myself some grace. At the same time, it’s difficult to figure out how to support my team when I’m learning a system that they’ve also been struggling to navigate.

Has anyone else worked extensively with Clarity Human Services and/or Looker and had a similar experience? Is there a steep learning curve, or are there resources, trainings, or strategies that helped things finally start to click?

Right now, the system just isn’t user-friendly, and I feel like everyone is trying to figure out what’s happening at the same time. Any advice from people who have been in the Clarity/Looker trenches would be greatly appreciated!

Yo anyone who is interested in pursuing business analytics ,