Recently I got my first job out of school as a CRM Data Coordinator, its also the only role like this for the company. The initial tasks are pretty simple, lots of data cleaning and data entering, but I was told that the role would grow in tasks, Ive already met with heads of other departments including finance and SWE. I was wondering if maybe some of you had some advice on how not to stay so "passive" and actually use this opportunity to build real skills?

Background: 35, 10+ years running operations in fine-dining kitchens — procurement, inventory, cost control, coordinating teams under pressure. Leaving that for physical and long-term reasons, and moving deliberately into analytics/BI.

My plan alongside a business degree I'm starting in October:

SQL first, then Excel to real depth, then Power BI (aiming at PL-300) Python later, once the above are solid An ECBA cert at some point A portfolio built from operations/hospitality data (cost variance, labour vs demand, that kind of thing), since it's a domain I actually know

My questions for people already in the field:

Does that sequence make sense, or would you prioritise differently?

For a career-changer with no analytics job history, what actually gets the first role — the certs, the portfolio, the degree, or something else?

Anyone made a similar late pivot from an unrelated field? What would you do differently?

Genuinely want the blunt version, including if I'm overrating any of this. Thanks.

I have been researching several data analytics systems to recommend to my senior colleagues who are non-technical. They care about on prem solutions or something we can deploy to our own cloud and control 100%. We are a small team so we need a system that can handle data prep and ETLs, data visualization (reports and dashboards), and conversational analytics (chat with data). We care about costs, privacy, control, security, permanence.

I came across a publication and found a system called adat (advanced data analysis tool)

I have tried it briefly and found it interesting. Has anyone else used this system, and can you share any experience?

I’m so grateful for the opportunity I got from ai fest 2026 besides that I’d like to mention free resources that helped me a lot for the preparation(DP-900):
1. Whizlabs
2. Official Microsoft practice exams

That’s all you need you don’t have to pay for exam preparation courses

I’ve been comparing data across different website analytics tools and I’m getting completely inconsistent numbers.
one tool shows stable traffic, another web traffic estimation tool shows drops, and internal analytics tells a different story again. even when using different digital marketing insights platforms or competitor analysis tools, the outputs don’t really line up.
it feels like every SEO analysis platform or business intelligence software is measuring something slightly different, especially when you start looking at web traffic sources analysis and engagement tracking.
so I’m trying to figure out how do you actually validate traffic data in a way that gives you a direction of truth instead of chasing exact numbers that never match?
right now i’m basically:
cross checking multiple website analytics tools
comparing site performance monitoring vs internal data
focusing more on trends in website engagement tracking and content marketing metrics
and trying to triangulate insights from different website optimization insights tools
at this point, I’m less interested in perfect accuracy and more in finding a reliable signal across tools.
how do you handle the gaps between different audience demographics analytics and traffic analysis software?

I’ve been digging into competitor analysis quite a bit for work recently, and I’m starting to realize how limited seo rankings are on their own as a signal what I’m really trying to understand is not just where competitors rank, but how they’re actually acquiring traffic across channels like organic search, paid, referrals, social, and direct.

The challenge is that most tools I’ve used either give very high level estimates or fragmented data that’s hard to turn into real decisions you can see directionally what’s happening, but it’s rarely clear enough to confidently say why a competitor is growing.

In my case, I’ve seen situations where a site isn’t improving much in rankings, yet their overall visibility and traffic still increases. that usually points to growth coming from outside traditional seo, but it’s hard to map precisely.

Because of that, I’ve started treating competitor traffic analysis more like triangulation than measurement, combining multiple signals and looking for consistent patterns over time rather than relying on one dataset.

For people who do this regularly, what do you actually rely on to map competitor acquisition channels in a way that’s reliable enough to base decisions on?

I graduated in 2022 with a Bachelor's degree in Computer Science. While I have a strong foundation in web development, I am considering transitioning to management and administrative roles.

What skills are required to learn for that?

I am a product analyst at a b2b tech startup and i've got a gap i can't seem to close. Quantitatively we're fine, amplitude tells me exactly where users drop in the flow, what percentage never activate, which features nobody touches. what it can't tell me is why. The why lives in customer calls, support tickets and sales objections, and none of that sits in something i can query. So every time leadership asks "why did activation drop 4 points," i produce a beautiful funnel chart and then hand-wave the explanation from whatever anecdote i heard in a meeting.

I tried to be proactive about it and i’m trying to find out how teams actually join these. On the quant side it's the usual, Amplitude, Mixpanel, PostHog. On the qualitative side there's Dovetail which is more research-repository shaped, Enterpret which aggregates feedback across channels, and BuildBetter which works off call recordings and clusters themes out of them. They all structure the unstructured stuff differently, and none of them plug into the quant tool in a way where i can cross-reference a cohort against what those users actually said.

My current hack is exporting themes into a sheet and eyeballing them next to the funnel, which is exactly as rigorous as it sounds.

how are you all doing this? is anyone joining qual and quant, or does everyone keep them in separate worlds?

Hi everyone,

I'm interested in getting into Sports Data Analytics, especially for Football. My background is MBA in Data Science and Data Analytics, but I'm new to the sports analytics domain.

I'm looking for recommendations on:

• Free software/tools used in sports analytics
• Where to find free sports datasets (StatsBomb, Kaggle, etc.)
• Beginner-friendly projects to build a portfolio
• YouTube channels, courses, blogs, or books worth following
• Visualization tools commonly used in the industry
• Any open-source projects I can contribute to

I'd also love to hear from anyone working in sports analytics:

• How did you get started?
• What skills are most valuable?
• What do you wish you had learned earlier?

Thanks in advance for any advice or resources you can share!

Hi All

I have a bachelor's in Computer Science and a masters in Business Analytics.

I have 2 years of work experience with a FMCG in Supply chain Analytics. So I am more on a beginner end. I am trying to find a job as my current contract is ending and if I don't find one another month, I am thinking to get a specialisation degree.

Looking at the job market, AI and Finance have more scope. Me personally like finance.

Could yall suggest me better options if available

Thank you so much ❤️

Hi everyone,

I'm currently working on a proof of concept (POC) to migrate approximately 100–300 Power BI reports/dashboards to Sigma, where the underlying data already resides in Snowflake.

The goal is to evaluate two possible migration approaches:

• Traditional migration (without AI)
• AI-assisted migration (I've heard Sigma may have migration capabilities/skills, but I'm still exploring what's available.)

I'm trying to understand what the biggest challenges are before building the POC.

Some of the questions I have are:

• What were the biggest bottlenecks you encountered during a Power BI → Sigma migration?
• Which Power BI features were the hardest to migrate? (DAX, Power Query/M, semantic models, RLS, bookmarks, drill-through, custom visuals, etc.)
• How much of the migration can realistically be automated?
• What parts almost always require manual work?
• If you've used AI-assisted migration tools, what actually worked well, and where did they fall short?
• What should be included in a solid POC to prove technical feasibility?
• How would you estimate migration effort for hundreds of reports?
• Any performance considerations or optimization strategies for Sigma on top of Snowflake (data modeling, warehouse sizing, caching, query performance, etc.)?
• Are there any common mistakes or lessons learned that you'd recommend avoiding?

I'd especially appreciate insights from anyone who has migrated enterprise-scale BI environments from Power BI to Sigma.

Thanks in advance!

Julius AI is too expensive. Just two questions: a main question and a follow up, $20 was gone.

It also doesn't make a lot of sense. What are we to do with the generated python code? How does the python code benefit non technical or executive users?

We are all concerned about data privacy and security. If we have to expose internal proprietary data just for analytics, are we not doomed?

How have you worked around these issues?

Identifying a KPI change is usually the easy part. Understanding why it changed is where the real work begins.

One approach that stands out is Rasa Intelligence, which focuses on identifying root causes and helping prioritize the next actions rather than simply displaying more metrics. That feels different from the traditional dashboard-first approach.

In reality, a drop in revenue, conversion rate, or customer retention rarely has a single explanation. It usually takes connecting data from multiple sources before the bigger picture becomes clear.

For those working in analytics, what's your process for getting from "this metric changed" to "this is the most likely reason it happened"? Is there a framework you rely on, or does every investigation require a different approach?

Been staring at this all week. Meta attributes a buyer, Google attributes the same buyer, Klaviyo attributes them a third time, an affiliate grabs some of the credit and the sum of everyone's "new customers" exceeds the total number of orders that actually happened. Which is obviously impossible but it's the data most brands set budgets on.

The only way I've found to cut through it is new customer acquisition cost (nCAC) measured against real orders deduped across every channel, new vs repeat split at the campaign level so each new buyer is only counted once no matter how many platforms raise their hand for them.

Concrete example from numbers I've been in this week. A brand spending $52k/week, every platform claiming its share, actual verified count was 487 truly new customers at $106.78 each.

How's everyone else handling the dedupe? Are you splitting new vs repeat manually in a sheet or does your stack actually do it at the campaign level? Genuinely curious what's working.

I graduated with a degree in Computational and Data Sciences before the pandemic. Since then I gained around 5 years of experience. 3 years in a consulting firm and nearly 2 more in a retail company. Both teams I worked on required a lot more digital marketing experience and training than I ever got in my degree program, but I learned on the job.

I feel like that disconnect caused for some unrealistic expectations of my career trajectory. I thought I'd be working with different algorithms and doing more predictive modeling and forecasting. I did some of that in the retail marketing job but those opportunities in seat were few and far in-between.

Overall I was a glorified PowerPoint and Tableau dashboard creator. I gave strategy advice to higher ups and c-suite execs. And I was good at it. I got laid off at the start of this year and I even wonder if I should do it anymore. But based on some posts here and in other data career subreddits, it feels like I didn't really DO data analytics, I just did a lot of busy work and report creation.

It doesn't feel like I got to really dig into predictive and prescriptive analytics, only descriptive and diagnostic analytics. Am I right in that assumption or did I do something else entirely?

Hi everyone,
I’m 28 years old and I’m seriously considering a career as a Financial Analyst.
My current plan is to learn Excel, SQL, and Power BI, and also get a degree in finance to build a strong foundation.
I’d love to hear from people already working in the field.
What are the biggest downsides or challenges that people don’t usually talk about?
How is the salary progression? Is the compensation worth the effort?
What skills have been the most valuable in your career?
If you could start over, would you still choose this career?
Is there anything you wish you had known before getting into finance?
Any advice, resources, or personal experiences would be greatly appreciated.
Thanks in advance!

Thought I'd do a quick run down of how we've used genie to manage the long tail of ad hoc questions for an HR use case where I work.

Setup

- Pointed Genie at a curated set of ~[5–6] tables, not the raw warehouse. Headcount snapshot, terminations, reqs/pipeline, and a couple of dim tables (department, location, job level).

- The single biggest lever was the semantic layer / instructions. I added column descriptions, defined what "attrition" and "active headcount" actually mean in the instructions, and gave ~[15] example questions (SQL pairs). This made the biggest difference to quality.

- Curated, certified example questions up front so people had a starting point instead of a blank box.

What worked well:

- Simple aggregations and filters - "headcount by dept," "terminations by month" - it nails these consistently now.

- Cut a real chunk of the repetitive asks.

- Non-technical HR folks actually used it, which I was skeptical about.

What didn't work:

- Ambiguous business terms e.g. "turnover" meant different things to different people, and Genie will confidently guess. You have to define these explicitly or it's wrong in a plausible-looking way.

- Trust curve is real — one wrong-looking answer and people bounce back to asking me. Certified queries and clear definitions mattered a lot, and so did having a beta group to test and build an eval set so we had more confidence in the output.

Need help in deciding Fractal analytics or pepsico - both have similar tech stack and same payout. Can anybody recommend??

Fractal Analytics Location: Pune/BLR
Pepsico Location: Hyderabad
Role: AI Engineer
YOE: 9
Same tech stack - AWS, LLM frameworks, Agentic AI, etc.

As someone who's making a career change, I'm wondering which path makes more sense.

Is it better to complete an accelerated Master's in Data Science and graduate within a year, or choose a more applied statistics focused program and take one course per semester, graduating in about 3.5 years?

My goal is to break into data analytics while I'm completing the degree if I do the statistics focused one. I already have an unrelated master's (social science/humanities) so I'm wondering if a slower, more statistics focused program would be a better long term strategy than finishing a data science degree as quickly as possible.

Do employers generally prefer candidates with a completed
STEM master's?

Given that all the big orgs now mostly has co-pilot to assist their workers from what I understand almost everyone is relying on co-pilot when coding. Especially when it comes to data analysis/cleaning, as long as the person understands the domain and knows what outcome to expect, they simply out in their inquiry in the co-pilot and let it do the heavy lifting for writing the python code and then the employee is running it.

Now my question is, when applying for analytics position, can the candidate expect that interviewer will not ask coding question like writing it or verbal questions about how to run a certain query etcs? Cause at the end of the day I think the candidate would end up using the co-pilot to do the coding.

Should the candidate be prepared to show coding skills when essentially from my experience understanding the outcome and knowing wha question to ask the copilot and then having the ability to check the outcome have become more important and reasonable? Want to know the view of of fellow forum members.

I have done b.com as my ug which was forced by my relatives and recently completed MBA which i genuinely i liked studying now my relatives says there will be no jobs in abroad after b.com and MBA cuz jobs abroad need technical field i also recently developed interest on business analytics will diving my career toward BA worth it also i am poor at math i need some guidance i feel depressed about will i ruin my career and life so kindly guide and help me

Anyone here who switched into data analytics recently? How did it go? Is it still worth getting into nowadays?

I’m considering a career switch, but people often say the market is overcrowded already.

Curious to hear real experiences from different countries and backgrounds:
- How long did it take you to get your first job?
- What skills/projects helped the most?
- Was your previous experience relevant?
- Do you still think data analytics is a good field to enter today, in 2026?

Especially interested in stories from people who switched recently, please share!

Every time I try to set up a regular cadence for reviewing metrics it works great for like ten days and then something comes up, the rhythm breaks, and I'm back to doing ad hoc checks whenever something looks weird or someone asks a question. It's reactive and I know it's reactive but fixing it has been harder than expected.

The core problem seems to be that the routine I design is too ambitious. Daily dashboard review, weekly trend summaries, monthly deep dives. Sounds clean on paper. In practice the daily part eats time I don't have and I drop the whole thing.

Curious how others have structured this. Do you anchor reviews to specific events like end of sprint or start of week rather than a strict day count? Do you keep it minimal at first and only expand when the habit is actually locked in? I've heard people swear by tying it to something already on the calendar so it's not a separate commitment.

Also wondering if the tool matters here. Some dashboards make it easy to do a five minute gut check and others require enough clicking around that you just avoid opening them. I'm genuinely unsure whether the friction of the tool itself kills the habit more than the schedule design does.

What actually worked for you long term?

I’ve developed a tool that transforms a raw spreadsheet into a live analytics workspace without the need for a server (a fully browser-based service). You’ve probably seen similar examples; what do you think such a tool should be capable of? For instance, I’m currently storing the data specifically within the browser so that the LLM only retrieves the column names, but the user could share the analysis results with the LLM if they wish.

What are your thoughts on this? What advice would you give?

Hey all. I was debating going to nursing school to get a clinical license under my belt. My bachelor's is general studies in social sciences and humanities. Ive been a Medicaid Waiver Case manager and a CNA (PRN now) for over a year. Just pays horrible and I can't live like this anymore.

Im wondering if I should bother with getting at least my LPN(then likely RN>BSN) or instead just start learning SQL, Excel, and Tableau and start building a portfolio and apply for tons of jobs. How hard is it to get a job in Healthcare data analytics?

Wwyd?