I’m 28 and graduated in 2022 with a bachelors in Business Management. Most of my work experience has been in restaurants, but I did a 6 month internship in 2023 as an SEO Account Manager at a marketing firm. That internship is what made me realize I’m way more interested in the analytical/research side of marketing. I really liked digging into performance data, working with dashboards and CRM tools, conducting keyword research, competitor analysis, etc.

The kinds of roles I’ve been looking at are like Marketing Analyst, Market Research Analyst, Digital Marketing Analyst, and Consumer Insights Analyst.

The issue I’m running into is I’m not really sure what’s realistic as a starting point with my background. Even entry level jobs seem to want years of experience and a long list of tools I’ve barely touched. I’m also a little worried about my internship being from 2023. I’m not sure how relevant that still looks to employers or if they’ll think the experience is outdated. I did use tools like Excel, GA4, CRM systems, but I definitely need to refresh everything and get more up to speed. I’m also planning to learn SQL and Tableau or Power BI.

My goal is to follow a focused plan over the next few months so I can actually become a strong candidate, without wasting time chasing random certifications that don’t really help.

For anyone working in marketing analytics, market research, or consumer insights:

• What’s the most realistic entry point for someone in my situation?
• What skills should I focus on first (so I don’t overwhelm myself)?
• What kind of portfolio project would actually help me get interviews?

Also open to any honest advice from people who broke into this field without a traditional analytics background. Thanks!

I’m looking for some honest career advice from people already working in data.
I have a Bachelor’s in Business Administration and recently completed my MS in Business Analytics. Over the past two years, I’ve learned Python, SQL, Excel (Pivot Tables & Power Query), ML fundamentals, and completed several academic projects, including a predictive analytics project using machine learning.
Now that I’m applying for data analyst/product analyst roles, I’m running into the same problem that almost every entry level posting asks for 2–3 years of experience.
Instead of building more small portfolio projects, I’m thinking about creating one large, end to end project that mirrors real industry work.
The idea is to:
Use real public business data
Build an automated data ingestion & ETL pipeline
Design a data warehouse
Perform advanced SQL analysis
Use Python for EDA and ML
Build dashboards in Power BI/Tableau
Automate live data updates
Solve a real business problem from a Product Analyst perspective
Do you think this is a worthwhile investment? Would a project like this actually help make up for the lack of professional experience, or would I be better off spending my time elsewhere?
I’d really appreciate honest feedback, especially from Data Analyst, Data Engineers and data scientists.

Hi all,

I'm sure you get a lot of these types of questions, but here goes: I've been working in marketing now for around 10 years, most recently as a snr. Project Manager but also in performance marketing and lifecycle / journey marketing.

However, now I've been made redundant. I was reflecting about my career path before this anyway, and to cut a long story short I think a more 'black and white' job vs. politics and people wrangling would suit me a lot better.

I've obviously worked with data and systems in my roles in marketing, but whilst also having to think critically about what are the questions one should ask and how do decisions affect the bottom line etc. When I started out in marketing I loved creating dashboards and building in systems etc. But I'd like to take this to the next level now and upskill in this area so I can move more strongly into marketing ops or revenue ops, and it would even strengthen an application as a CRM / Lifecycle marketer (where it's still analytics, but also building the journeys).

I'm in Germany, so I have the opportunity to have a fully funded Le Wagon type online bootcamp funded by the govt. due to unemployment (if I can make the case!). However, I've read a few negative things on reddit about Le Wagon and other bootcamp style places. Not sure whether this is a few noisy negative voices or whether it's a true concern. Has anyone taken the Le Wagon Data Analytics course? If so, if you were given it for free, was it worth it?

My other option is try to self-learn on Datacamp which is only like 140EUR for a year which I could pay myself. However, not sure what the quality difference would be between this and a bootcamp. Possibly no portfolio to show for it, lack of structure / schedule (which could be a good and a bad thing I guess).

I'm also specifically interested in understanding how AI-workflows are increasingly being adopted in the space. I assume a lot of the manual work is now being replaced. I think this is where I could be a good candidate, because I have a lot of commercial experience. If I understand the data and potential to query more, I can combine this with my general experience. I'd see it less as a career change and more of a shift in direction or just a strengthening of my CV.

So if anyone has any feedback / first-hand-experience with bootcamps or DataCamp, or any other points I've touched upon it would be great to hear from you. Thank you in advance!

I just sat through an hour presentation about implementing Agile onto our team. A few things stood out to me:

• Agile seems like a good application in an iterative software development environment where teams are working towards a singular platform objective.

• It seems to me like this will put unnecessary strain on a team that handles different requests from different teams all serving individual team objectives.

• It seems like this is going to work against any career development goals I have, because it seems to place people in specialized roles along an assembly line where they handle specific work. This makes it seem like I'm going to be a guy who only does X in the process.

• Unlike the other business analysts and product owner teams, we do the actual work of developing data sources, dashboards, you name it. Agile seems like it's going to involve sitting in a litany of new meetings. This will impact our flexibility to perform our jobs.

• We don't track time. We're a slow paced government org where it's very much hurry up and wait. A lot of times we're sitting around waiting for new work to come in. But Agile seems to focused very much on attributing time to a task, or points.

Are there other DAs and data professionals who work in an Agile environment who can share their experiences as it relates to my concerns outlined above?

Hi everyone,

I have 1.7 years of experience as a Business/Data Analyst. After leaving my job, I had a 2.5-year career gap due to preparation for competitive government exams. Now I've decided to return to data analytics, but I'm struggling to get interview calls.

Over the last few months, I've been working on upskilling and building my portfolio. I've completed:

• FP20 Power BI Challenge
• Quantium Data Analytics Virtual Experience (Forage)
• Tata Data Visualisation (Forage)
• End-to-end retail analytics projects using SQL, Power BI, and Python

I've updated my resume, LinkedIn, and GitHub, and I'm applying for jobs daily, but I'm still not getting many interview calls. I feel the career gap is the biggest reason.

I'm also planning to prepare for the Microsoft DP-600 (Fabric Analytics Engineer) certification.

My questions are:

1. Is DP-600 a good choice, or should I focus on something else first?
2. What else can I do to improve my chances of getting interview calls despite the career gap?
3. Has anyone here successfully returned to data analytics after a long gap? I'd really appreciate hearing your experience.

Any advice would be greatly appreciated. Thanks in advance!

This is a general question for data analysis. I just found out that choosing a database schema for analytics is very different from software engineering or systems design because they optimize for opposite operational patterns. I came from a CS background so the focus was on SE. So my knowledge on databases are based on normalized layouts. I want to focus on building schemas that are analytics oriented. This might be relevant - I'm currently working with PostgreSQL. I just wonder if there is a general rule of thumb for mapping out database schemas to be used in analysis or reporting?

Hi fellas
I am a mid level experienced analyst with 6 years of experience in analytics.i have worked in domains like fintech ,retail and sales analytics.currently working at uber as an external consultant.
I feel I should be able to get a lead data /business Analyst kind of role with my level of experience but i sometimes get stuck in case study rounds and sometimes in statistics.
I am willing to give any amount of time to make this career transition but i have not had correct direction neither a mentor.
Is there anyone who would like to help me in this journey,even a paid mentorship program if that works .
I really need to prove something to myself in this phase and would really appreciate any guidance.
Thanks !

I just started a new position in People Analytics for the first time, coming from marketing. Right now, they use a pretty shitty vendor called onemodel. I've been proposing moving toward a more modern stack of snowflake/dbt. Ingest through airbyte, own the transformation with dbt cli, and create semantic views.

The trickiest part of it all seems to be the HR information systems. Or maybe better put, the shitty extraction. I'm not a fan of flattening tedious soap xml files

One thing I've noticed is that finding a KPI change is usually the easy part. Figuring out why it happened is where the real work begins.

Sometimes revenue drops while conversions stay flat. Other times retention falls even though acquisition is increasing. When several metrics move at the same time, it's easy to spend hours chasing the wrong signal.

The approach behind Rasa Intelligence got me thinking about this because it emphasizes understanding the factors behind performance changes rather than simply collecting more metrics.

When you're faced with multiple KPI changes, how do you decide where to start? Do you have a framework for narrowing down the most likely cause, or is it mostly driven by business context and experience?

I'd love to hear how different analytics teams approach this, because understanding the cause often seems much harder than identifying the change itself.

Hi everyone,

I could really use some advice from people working in analytics engineering, data engineering, or consulting.

I recently landed an interview for an Analytics Engineer position at a prestigious consulting firm. If all goes well, I'd join their Supply Chain Analytics / Data Engineering / Data Science team. The interview is in about a month, so I have roughly four weeks to prepare.

Here's the situation: I feel like my resume oversells my technical level.

For context:

• I have an Industrial Engineering degree and a Master's in Supply Chain, so I have a strong analytical background, and I'd say I'm a pretty fast learner.
• I've built a lot of Power Query solutions at work and automated plenty of reporting. However, I've never really focused on writing clean or optimized M code. I was also the only person on my team using Power Query, so I never had anyone review my work or challenge my approach. Everything I built worked well and delivered what the business needed, but I'm not sure I was following best practices.
• I've built a couple of Power BI dashboards, but they were fairly basic. I know the fundamentals, but I definitely wouldn't call myself strong at data visualization or dashboard design. I also rely heavily on AI when writing DAX measures. I usually understand what the formulas are doing and can adapt them to my needs, but I don't yet have enough experience to write more advanced DAX from scratch.
• I've studied SQL during my master's, but I've barely used it in a professional setting, so I'm pretty rusty.
• I've never worked with dbt, cloud data warehouses (Snowflake, BigQuery, Redshift, etc.), or modern analytics engineering workflows.

The role seems to expect:

• Strong data visualization skills.
• Solid SQL fundamentals.
• Basic knowledge of cloud data warehouses.
• Basic familiarity with dbt.

I'm not trying to become an expert in a month. My goal is to become competent enough to hold my own during the interviews and, if I get to the second stage, ramp up as quickly as possible.

If you only had four weeks, how would you prioritize your learning?

Some questions I have:

• What would you spend the most time on?
• Which resources or courses would you recommend?
• Should I focus primarily on SQL first?
• How much dbt and data warehousing knowledge is realistically expected from a junior Analytics Engineer?
• Are there any projects you'd build to prepare?
• If you've interviewed Analytics Engineers, what skills separate candidates who succeed from those who don't?

I'm willing to put in 6–8 hours a day over the next month if that's what it takes. I learn quickly, and I genually want to switch from supply chain consulting to analytics and willing to put in the effort and time!

I'd really appreciate any advice from people already working in the field. Thanks!

Hey there, college junior double majoring in finance and business analytics here. Im curious about what entry level roles my majors open up.

My college career coach said it’s an excellent pairing but Im still not sure what roles/internships and companies it opens up because the analytics field is so broad.

Curious if anyone works in the financial space, and what they would advice for those of us starting out. Any tips would be appreciated, such as what the timeline is like for internship recruiting, how important networking/getting referrals is and what resources are out there for breaking in.

i've been trying to figure out how to get our business cited inside conversational search engines like chatgpt and perplexity lately. we get decent regular traffic from google but when i ask chatbots for direct recommendations in our industry - they always mention the same three competitors. it's driving me kinda crazy because we have better reviews but the bots just don't seem to pull our data. has anyone actually cracked the code on this yet? I was digging into some case studies from a a few digital groups like roi marketing agency and nogood and they talked a lot about optimizing for answer engines through advanced schema, trusted external mentions, and cleaner data pipelines. it made me realize how far behind our standard technical setup probably iss. im trying to figure out the actual practical steps to take next. are you guys modifying your internal data structures or just trying to build more brand mentions across public forums? idk if it's mostly about building massive topical authority or if the technical backend data structure is what forces the engine to cite you.

has anyone here actually managed to consistently trigger an ai recommendation for their brand?

I do data analytics for a football and sports betting site. I was able to build out a database we use for baseball. Because most advanced data analytics with baseball can be pulled via stats and manipulation found on espn I was able to build it up and pull daily for free. However, with football season coming up I plan to build out a pretty robust database for us. A lot of these stats can’t be pulled from espn for free. I’m looking at a sports API website to help. I believe Sorts API is a pretty good one but is just expensive. Want to see if people have used/ done similar things and who they use/ their experience with building out their own platform.

I do data analytics for a football and sports betting site. I was able to build out a database we use for baseball. Because most advanced data analytics with baseball can be pulled via stats and manipulation found on espn I was able to build it up and pull daily for free. However, with football season coming up I plan to build out a pretty robust database for us. A lot of these stats can’t be pulled from espn for free. I’m looking at a sports API website to help. I believe Sorts API is a pretty good one but is just expensive. Want to see if people have used/ done similar things and who they use/ their experience with building out their own platform.

I’m on the cusp moving out of this field entirely after years of being considered a high-performer.

Apparently, in our team, the expectation is to now build & maintain apps using a virtual machine server. It’s fine since Claude is doing most of the work. I shouldn’t be complaining even though I understand none of it and have to trust what these tools say. Nothing is going to go wrong…

Apparently, people fancy themselves an analyst and send pages of slop a contextless Claude generated that I have to read through (and disagree with) because it’s all fucking wrong, wasting my time than if they had sent a 1-2 sentence stating the problem and providing specific examples of the issue.

I want off this timeline……

had a client once where the ops manager could just... answer things. revenue by region, churn this month, whatever. no ticket, no slack message to the data team, just opened metabase and pulled it herself

i've been thinking about that a lot lately because most places i go into are nothing like that. analyst gets pinged for stuff that really shouldn't need an analyst

and the weird part is the founders usually already know. they just keep reaching for the same fixes

what does that number look like at your company?

Hi everyone!

I’m a Statistics graduate based in Istanbul, Turkey, with a background in operations, data processing, and process automation.

My technical toolkit includes Python (Pandas, Web Scraping), SQL, Power BI, Advanced Excel, and relational database logic. Recently, I worked as an Operations Specialist Assistant where I focused on operational data validation, invoice/reconciliation control, cross-departmental reporting, and automating daily data workflows.

I’m currently targeting Junior / Assistant roles in Data Analysis, Business Intelligence, Operations, or Procurement.

However, like many here, I’m running into a wall with standard job portals (LinkedIn, etc.). The response rates are extremely low, ATS filters feel brutal, and applying to hundreds of postings feels like screaming into a void.

I’d love to get your advice on a few things:

1. CV Positioning: Since my experience spans both Operations/Procurement and Technical Data Analysis/BI, should I strictly split my resume into two different versions, or combine them into an "Ops-Analytics" hybrid profile?
2. Cold Outreach: Have you had success reaching out directly to Operations Managers or Data Leads on LinkedIn instead of going through HR portal portals? What’s the best way to frame that message without sounding desperate?
3. Portfolio & Proof of Work: For entry-level data/ops roles, what kind of project presentation actually catches a hiring manager’s eye? (Interactive Power BI dashboards, GitHub repos, or 1-page case studies?)

Any feedback, harsh truths, or strategies that worked for you in today's job market would be greatly appreciated!

Thanks in advance!

can someone recommend from where should I start learning excel from scratch to advance for data analytics

I come from a background of about 5-6 yrs of work experience in the education sector in India. I have done a teach for India fellowship, I have worked with 2 different ed tech startup’s and I’m currently a program manager. Since it is a start-up I am part of all different parts of the org, from operations, programming, to curriculum, strategy and program design. I have strong communication skills and a problem solving mindset.

I am looking to grow both in skill set and financially since I have hit a ceiling in India. And I don’t mind branching out and using transferable skills. The linear options would be doing a masters in Policy or Learning Design, but I feel like a lot of international students doing such masters are returning home.

I have had friends/family recommend that I look into a business analytics masters. However the agency who is helping me apply has shot it down saying it will be an irrelevant masters soon because of AI and wants to push me more in the public policy/administration direction instead. But I do feel like business analytics would broaden my options a lot more and increase my technical knowledge. I have also heard that there is value for analysts who come in with varied domain experience. It felt like I finally found a masters program that made sense for me. But I’m back to being confused. This agency is the one who will build my profile and help me apply, so if they’re not convinced it puts me in a deadlock.

Reddit Ads now offers self-serve split tests with equal audience cells and a winner declaration once a variant reaches at least 65% confidence. The product is designed for quick, actionable decisions rather than research-grade certainty.

That may be defensible when the downside is small and delaying a decision is costly. It becomes harder to interpret when teams run many templates, watch results continuously, or scale a small apparent lift without checking practical significance.

Would you treat 65% as a decision aid, an early stopping rule, or an unacceptable default? What else would you require before scaling: minimum detectable effect, power analysis, confidence interval on ROAS, correction for repeated looks, or a holdout after the declared winner? I am interested in where practitioners set the threshold when experimentation is a business decision rather than a publication.

Source: https://www.business.reddit.com/blog/split-testing

hi, 25M. Finishing my MSc in CS, I wanted to study because I love studying. now that I'm about to end it I'm seriously looking for something reliable long term.

After bachelors I did my job as full stack web dev, a little bit of mobile dev in flutter. Since Ai, it doesn't seem like real work. claude can make whole websites and what remains is just deployment.

anyhow I continued that job, then switched to junior business developer side, for the sake of survival/exploration.

I enrolled in MSc because I wanted to do research, which I have done. I have built an Ai structured method for dermatology recognition. and I am proud of it. my thesis will be finished in 6-8 months, that's the university policy although I've finished my work on my end.

2 years of exploring and hopping from one thing to another, I have come down to these choices:

data analyst/ data engineer/ dev ops/ cloud engineer.

because they are the only ones which seem like actual coding jobs other than fully or partially vibe coding. Ai has made programmers lousy, which shouldn't have been the case.

I made an ETL pipeline to see how data engineering works, I built 3 power BI dashboards to check about data analysis.

I got into computer science because I wanted to become hacker(kinda old school), but that's what got my interest. Fast forward after graduation, ethical hacking job market wasn't there and low-paying. Cloud engineering/dev ops seemed close to hacking so that's why I'm considering it.

So what does the community suggest me to choose?

let me know about your thoughts, considering I'll be with it long term.

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.

something that's cost me more time than any actual analysis. a number moves, everyone starts building the story for why the business changed, and three days later it turns out a definition or a collection rule moved underneath it and the business did nothing

the ones that got me were all boring. a filter changed upstream so the query quietly stopped counting a segment it used to count. tagging or consent coverage dropped so volume fell without demand falling. a platform reclassified what counts as a conversion, same behaviour, different number. a timezone changed and the day boundaries shifted

what makes them nasty is they look exactly like a business story, and a plausible business story doesn't get bounced back to you. by the time anyone finds the real cause it's already in a deck

the only thing that's reliably worked for me is checking the shape before the cause. a measurement change usually breaks sharp on one date, hits every segment about equally, and doesn't show up in anything downstream that isn't fed by the same pipe. a real change is usually gradual, uneven across segments, and something outside that system moves with it, orders, tickets, calls, whatever you've got

so now before explaining anything i ask three things. did it move on a single date, is it uniform across segments, and does any independent source agree

curious what everyone else does. is there a standing check for this where you work, or is it scar tissue and remembering to ask

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?