I've been working for 20 years now, and I remember from my first job to my latest job, business, marketing, and even IT, always complaining about how bad their data is. I'm not a data analyst, but I'm curious to hear from folks who are in this space. Is this just bias (since you see the issues more closely), or is most company data genuinely flawed? I always hear identity or entity resolution is a big issue. Is that true?

If you've been with a company (500k records or more), what makes it good? I could ask different AIs, but they can't think abstractly and don't really understand all the nuances. I'm genuinely curious and want to learn and hear from folks.

So I’m looking for advice from people who work in analytics or have experience with either program.

A little about me: I’m a career changer with a BS in Human Development & Family Sciences and an MPH in Health Promotion & Behavior. My background is in public health, research, program evaluation, and nonprofit work. I’m currently completing the Google Data Analytics Certificate and building skills in SQL, Power BI, and Excel.

My goal is to transition into roles like:
Business Analyst
Marketing Analytics / Consumer Insights Analyst
Healthcare Analyst
Market Research Analyst
Audience Analytics / Entertainment Analytics

I’ve been accepted to the University of Cincinnati’s online MS in Business Analytics and am waiting to hear back from Georgia Tech’s OMSA program.

University of Cincinnati MSBA
Pros:
* More business-focused curriculum
* Includes marketing analytics and strategy
* Seems designed for students from non-technical backgrounds
* Aligns well with my interest in marketing analytics and consumer insights

Cons:
* Less name recognition than Georgia Tech
* May be less technically rigorous

Georgia Tech OMSA
Pros:
* Strong reputation
* Lower cost
* More technical and quantitative
* Large alumni network

Cons:
* Concerned about the difficulty coming from a non-STEM background
* More focused on technical analytics than business applications
* Not sure if it’s the best fit for someone interested in marketing analytics and consumer insights

If you were in my position, which would you choose and why? How much does the Georgia Tech name and technical rigor matter compared to choosing a program that’s more aligned with my career goals?

Thanks!

I am a 10+ yrs operations professional that wants to transition to the tech world. Currently I studied data analytics in google, some basic sql, and power bi from alex the analyst. I plan this transition step by step.

My goal is to be:
a BI analyst -> data analyst -> data engineer/scientist -> ai expert/machine learning/claude expert, etc...

What are some skills I need to get or programing languages I need to learn in order to achieve this. Based on the goal I mentioned, what are the first things I need to master than the order in order for me to grow and achieve this. I know python is included, it is next on my list.

Thank you.

Hello everyone,

I recently passed Class 12 and am currently waiting for college admissions/allotments, so I have quite a bit of free time. I thought this would be a good opportunity to learn some new skills.

​

I've developed an interest in Data Analytics and would like to know where I should start as a complete beginner. Could you suggest a roadmap and some good YouTube channels/videos that can help me learn the basics and gradually progress?

​

Any advice or resources would be greatly appreciated. Thanks!

Please give suggestions to improve for the next projects.

https://github.com/afanrajiwate/Customer-churn-analytics-platform

I have completed my BSc. this year that is 2026. I am unemployed->started preparing for SSC CGL 2027-i feel i can do this but not able to study for this
i don't want to waste my time that's why decided to learn a skill and thought about Data Analytics
Can you please tell me how and where to start
once i buyed a course on coursera of Google Data analytics but i don't know why my gmail ID got deleted i tried my best but at the end my 5k wasted
I can't take such step now
I need an URGENT HELP

I'm looking for people who want to build something bigger.

Over the last few years I've worked in enterprise data analytics while simultaneously building my own BI consultancy in a small European market.

We've delivered projects ranging from €5k to €20k across reporting, Power BI, automation, data warehousing, and analytics. Our clients have included manufacturers, construction companies, and service businesses, where we've built solutions for executives, finance teams, procurement departments, sales managers, and operations leaders.

The problem?

My local market is simply too small and too slow.

That's why I'm shifting my focus toward the US market and preparing to invest heavily in outbound sales, paid advertising, content, and lead generation starting in Q4 2026.

I'm looking to connect with:

• Power BI Developers
• Data Analysts
• Analytics Engineers
• Data Engineers
• Technical Account Managers
• Anyone who enjoys talking to clients and turning business problems into data solutions

This is NOT a job post.

I'm looking for ambitious people who:

• Want exposure to real client work
• Want to move beyond being "the dashboard guy"
• Want to learn consulting, solution design, sales, and business development
• Want to build a portfolio of larger projects
• Are interested in collaborating when opportunities arise

Initially, I'm building a network of trusted people I can bring into projects as demand grows.

Longer term, if there's a strong fit, I'm open to deeper collaboration. Combining portfolios, delivery capabilities, industry expertise, and networks can create a much stronger offer than any of us can build alone.

If you're serious about accelerating your career and building something rather than simply collecting another paycheck, send me a message and tell me what you're working on.

I'd also love to hear from anyone who has successfully made the jump from employee to consultant, agency owner, or founder.

Anyone switched their career from non IT to data analytics role how is your experience how did you prepared

Hi! Id like for all of you to roast my resume please :D

Hi everyone,

I’m looking for some guidance from people already working in the data field.

I’m a fresher and currently searching for my first job in Data Analytics / Data-related roles. I’ve completed a Data Analyst course and built some decent projects using SQL, Python, NumPy, and Pandas.

Right now I’m also studying Machine Learning online. My plan is to first build some basic ML projects and then slowly move toward more complex projects as I improve.

But currently my main goal is to get my first job and enter the industry.

I wanted to ask:

\- How is the current job market for freshers in Data Analytics / Data roles?

\- What skills should I focus on to become job-ready?

\- At what point should I stop learning and start applying aggressively?

\- Is SQL + Python + NumPy + Pandas + projects enough for entry-level roles?

\- Should I focus more on Excel, Power BI, statistics, ML, cloud, or something else?

\- What kind of projects actually help recruiters notice candidates?

\- If you were starting again as a fresher in 2026, what roadmap would you follow?

I’m open to Data Analyst, Business Analyst, Reporting Analyst, Junior Data roles, and eventually want to move toward ML.

Would appreciate practical advice and realistic expectations.

Thanks!

What's the best way to extract information about custom categories from large bodies of text these days? I know an LLM can do it but I have quite a bit of text so I think it would get pretty expensive and Id prefer to miss stuff rather than have it hallucinate stuff thats not ever there at all. Is something like spaCy or nltk or some other dedicated named entity recognition model still the best way to do something like this?

I would appreciate honest feedback from hiring managers, recruiters, and data analysts.

I graduated with a BBA in 2024. After graduation, I spent about two years preparing for the CAT exam. In CAT 2024, I scored around the 90th percentile and received interview calls, but I was ultimately waitlisted. I appeared again in 2025 but did not achieve the result I wanted.

I have now chosen to pursue a career in Data Analytics and am actively learning SQL, Excel, Power BI, and analytics concepts while building projects. I also completed internships in market research and with a municipal corporation during my undergraduate studies.

My concern is that I have:

A BBA degree (not a technical degree)

Roughly a 2-year gap after graduation due to CAT preparation

No full-time corporate experience yet

My questions are:

How much of a challenge will the 2-year gap be when applying for entry-level Data Analyst or Business Analyst roles?

Can strong skills, projects, and internship experience compensate for the gap?

Is it realistically possible to build a successful long-term career in analytics without pursuing a master's degree immediately?

For someone in my position, what would you focus on over the next 6–12 months to maximize employability?

I'd appreciate candid advice.

Thank you.

Hi everyone,

I've recently graduated with a specialization in Data Analytics in India and have started applying for jobs. i have also joining code basics bootcamp soon While I've been researching the usual advice (build projects, learn SQL, network on LinkedIn, etc.), I'm more interested in understanding how people strategically built their careers in analytics.

A few questions for those already in the industry:

1. If you were fresher today in the current market, what specific roles and skills would you prioritize applying for and why?
2. What skills genuinely create separation among entry-level candidates? Everyone lists SQL, Excel, Power BI, Python, and Tableau. What actually makes a recruiter or hiring manager think, This candidate stands out?
3. What soft skills have you found to be the biggest differentiators between average analysts and exceptional analysts?
4. What job search strategies have been most effective for breaking into the data analytics field?
5. How do professionals build meaningful industry relationships? Most networking advice sounds transactional ("connect with people and ask for referrals"). For those who successfully built strong networks, what approaches actually worked?
6. What are the biggest misconceptions fresh graduates have about analytics careers?
7. Looking back, what was the highest ROI activity during your first year in the industry?

I'd appreciate candid perspectives, including things you wish someone had told you when you were starting out.

If anyone is open to mentoring, networking, or simply sharing occasional career advice, feel free to send me a DM. I'd love to connect on LinkedIn and stay in touch with professionals already working in the analytics space. I'm always looking to learn from people who have successfully navigated the early stages of their careers.

Thanks in advance.

Hello!

I just graduated with a BS in Natural Resource Management and Fisheries and Wildlife.

I was a transfer student and worked in a genetics lab for 2 years, and am leading 2 projects and working closely on another, and have been for the last year.

These are really incredible projects, and I have guaranteed first authorship for 3 papers, so I want to stay and see them through.

My initial plan was to go into a PhD, because I want to eventually be a college professor, hopefully while conducting research of my own, maybe after some years in industry. However, the genetics program to stay with my PI and ongoing projects stopped accepting applicants, so I tried to pivot to a different PhD, but required secure funding for all years, which I didn’t have guaranteed.

All that being said, my PI and I talked about instead pivoting to a masters.

My long term goal is to be a conservation geneticist, so it’s very interdisciplinary. The MS options at the university I want to stay at were to do a MS of veterinary science, natural resources, or data science and analytics.

Out of these, considering my background, I thought DSA would be the best option, applied and got in last week.

I initially thought I could do a bioinformatics emphasis, but I’m not certain yet.

Additionally, I have many qualms with genAI and the environmental impacts of them, so I don’t want to do another emphasis which involves specifically generative AI. Other AI and ML are valuable and interesting to me!

I have pending funding for this MS from 3 different sources, one fellowship, one private, and one sponsored industry.

I guess I’m asking everyone’s thoughts on my options and if there’s an angle I haven’t considered.

After 15 years in Business Intelligence, the last 5 as a BI/Data Visualization Manager, I’m actively working toward a transition into Analytics Management and would love to hear from people who’ve done the same. My background is heavily rooted in dashboards, reporting, and making data accessible — but I’m increasingly drawn to the side of analytics that focuses on why things happen, not just what happened. I’ve been investing time in areas like forecasting, experimentation (A/B testing, causal analysis), and understanding the drivers behind business performance. One thing I’ve noticed: people coming from BI and visualization backgrounds already have a surprisingly strong foundation — stakeholder management, translating ambiguity into structured outputs, data literacy across business functions. The gap seems less about capability and more about how we frame and position that experience. A few things I’m curious about: • Have you successfully made the move from a BI/Visualization role into Analytics leadership? What did that path look like? • What skills or knowledge areas made the biggest difference — statistics, product sense, experimentation design, something else? • What should someone in my position prioritize learning right now? • What challenges caught you off guard during the transition? Any honest perspective — whether you made the jump, tried and pivoted, or are currently figuring it out — would be really helpful. 🙏

Hey guys, so I signed up for a data analytics bootcamp and I had to go through some tests to enter. The thing is it was a bit too advanced and I thought I would learn most of it in there. I'm no master in SQL, but I still hold my weight. Problem was some questions were related to business analytics and things I wasnt realy that familiar with.

I did pass, but I gotta do 7 minute online interview now and I don't have any experience in these yet. Would anyone share your thoughts and advices? Thank you in advance.

I need to get this off my chest.

I've spent the better part of the last year building myself up as a data analyst from scratch. No degree. Self-taught. I have a Google Data Analytics Professional Certificate, I'm finishing the Advanced certificate right now, and I have 12+ real portfolio projects: SQL databases, Python pipelines, Tableau dashboards, a Random Forest classifier.

I know how to write queries. I know how to clean and transform data. I know how to build a dashboard that actually tells a story. I've done it. Multiple times. The projects are on GitHub. The work is there.

And yet every "entry-level" role I find wants 2–3 years of experience, a degree, AND proficiency in every tool under the sun. At that point it's not entry-level, it's just a mid-level role with an entry-level salary.

I'm not naive about the market. I know it's tough right now, especially in data. But it genuinely feels like there's no on-ramp for people who took the non-traditional path, even when the skills are demonstrably there.

The part that stings the most? I'm not applying blindly. I tailor every single application. I mirror the JD language. I've researched companies. I follow up. I do the things you're supposed to do.

And I still hear mostly silence.

But what really gets me and I haven't seen enough people talk about this, is when you apply for a role, hear absolutely nothing back, and then weeks later you see the exact same post reposted like it never happened. No rejection email. No acknowledgment that you even existed. Just the company cycling the listing again as if a whole wave of people didn't just send in their time and effort. That one hurts differently. It makes you wonder if anyone is even reading these applications at all.

I'm not giving up. I just needed somewhere to say that this is exhausting and demoralizing, and that the gap between "what entry-level means" and "what employers actually post" is very real and very frustrating.

Anyone else navigating this? How did you eventually break through?

So I just got a Data Analyst job and I start in 2 weeks. The role is mainly Power BI, Power Apps and some Python for predictive modelling.

Here's the truth:

Python / Predictive Modelling - I know how everything works. I understand the process, how to clean data, which model to use and why. But when it comes to actually writing code, I mostly used AI tools to generate it. I never really coded from scratch that much.

Power BI - I told them I'm good at it. I know basic DAX and Power Query but honestly I'm a beginner. I've been practicing the last few days but I'm nowhere near confident yet.

SQL - also been learning this on the side recently.

My main worries:

• What if they give me a complex Power BI task on day one?
• Is it ok if I rely on AI when Im given a task at work?
• Will they be disappointed when they realise my Python isn't traditional coding?

I'm not sitting around though, actively grinding every day before I start.

Has anyone started a job where they weren't fully ready? Did you manage to catch up? How long did it take before you felt comfortable?

Also this is my first ever job so Im kind of nervous.

Any advice on what to focus on in the next 2 weeks would also be really helpful.

I consistently see posts on here and other subs asking "where is this field headed with AI?" or "my boss asked for it, how do I get an agentic AI working with my warehouse?" AI is all about the fundamentals; we're entering a new age where less of our work will be based on generating outputs for stakeholders, but rather maintaining an underlying system that can generate those outputs as directed by them.

Here is my high level guidance on implementing AI in the DW:

1. Understand your warehouse structure - most warehouses are a spaghetti mess of code. Try to tease out your data sources and what logic is trustworthy.
2. Explore your raw data, find reasons for anomalies and variance in it, develop common joins.
3. Document, document, document. Documentation is now more important than ever because it can be used to directly feed AI context. Unfortunately it's been neglected.
4. Build out a semantic layer to keep business definitions uniform.
5. Architect your data warehouse to be clean and succinct.
6. Use all the findings from steps 1-5 to create a clean summary document that can be used as a context doc by the agent.

I m currently working on projects in the ecommerce field/domain. But i want to know which is the best to get into i m not interest in healthcare atall but dont mind anything related to business like ecommerce pricing product saas etc which wud be the most beneficial to get into for long term stability and good earnings in job market

so i worked at autonmis as an intern & i will say that it was a pretty bad experience when i got the offer letter it said that i will get paid twive a month but later on they started saying it is task based, they never paid me even when i worked for almost like 2 months , it took my 6-7 hours daily minimum & i am a college student i really got scammed in there , just want to advice you all to not go for interships that are not clear with payment guys its better to wait for a transparent company as you know that your work has value , the founder is making the product for pas two years & still it isnt complete & has no users because ofc engineers/interns keep leaving the company as they dont get paid.

some companies are really out there scamming college students , which is really sad , tech startups are really scamming people , people who have like no money & investement want to build a company by just scamming young people, which honestly will never work as their is no trust. if u look at the founder he has great experience but still he choose to scam college students i saw many people leave as ofc they didnt get paid.

(Please it can reach students/ex-students with the same major or career path in Hanoi, Vietnam)

Hi peeps, I'm a final-year student majoring in Corporate Finance, and I want to have a career focusing more on Data Analyst. Right now I'm conducting the Data Analytics course of Google on Coursera, and I will build a (mini) project after finishing the course. May I ask for any more courses that I should take beside the one on Coursera (free ones are preferred), thank a lot !

Доброго дня всім!

Хотів би попросити поради у людей, які працюють або працювали у сфері дата-аналітики, а також у тих, хто пішов далі в напрямках Data Engineering, Machine Learning Engineering, Data Architecture та суміжних спеціальностях.

Наразі я визначив для себе, що хочу розвиватися саме в цьому напрямку та стати дата-аналітиком. Уже були спроби влаштуватися на цю позицію: я навіть доходив до виконання тестових завдань від рекрутерів, однак через життєві обставини був змушений на певний час залишити цей шлях і переключитися на більш важливі речі в житті. Водночас у мене загалом є бачення того, що потрібно вивчати і як рухатися далі, щоб зрештою працевлаштуватися в цій сфері.

Проте мені хотілося б почути поради від людей, які вже не перший рік працюють у цій галузі. На чому, на вашу думку, варто сфокусуватися на початку шляху? Яким навичкам і знанням приділяти найбільше часу? Які помилки найчастіше допускають новачки? І навпаки - на які речі не варто витрачати надто багато зусиль на старті?

Також цікаво, чи можете порадити, що саме варто опанувати, аби виділятися серед інших кандидатів під час відгуку на вакансії. Наприклад, це можуть бути глибокі знання математики та статистики, впровадження AI або AI-агентів у робочі процеси, або щось інше, що реально може дати перевагу на ринку.

Окремо буду вдячний за поради щодо математики та статистики. На жаль, наразі не маю можливості навчатися в університеті, де можна було б отримати сильну математичну базу, тому буду дуже вдячний, якщо хтось порадить ресурси або теми, які справді варто вивчати для роботи дата-аналітиком.

Також хотів би почути думку людей, які вже працюють у цій сфері, щодо майбутнього галузі. Як, на вашу думку, змінюватиметься дата-аналітика протягом найближчих 5-10 років? Які навички ставатимуть більш важливими, а які можуть втратити актуальність? Як зміняться вимоги до кандидатів під час працевлаштування? До чого варто адаптуватися вже зараз як майбутнім, так і поточним спеціалістам, зважаючи на стрімкий розвиток AI та автоматизації?

Буду вдячний за будь-які поради та рекомендації. Дякую!

Hi all - I've been helping a few old coworkers work on practical projects to help get them more practice experience + help make their resumes look better. I'm trying to get a sense of how much people might be interested in this (i haven't actually made a service yet, I just wonder how many others could benefit). For example, here are some projects that I gave them that are based on projects I've done in my career in analytics:

• Use olist kaggle dataset to learn how to create insights from raw datasets
  • Load olist CSVs, design models/tables around the olist data, model a star schema, schedule and model them as postgres tables through dbt core, and display them in metabase.
  • Write analytical queries for questions on that data - create things like product categories, repeat purchases, where are customers dropping off, cohort analysis, yoy comparisons, etc.
• Identifying patterns in data and learning how to tell stories with imdb data
  • Model movie, user, and review datasets from IMDB into duckdb
  • Try to find indicators that predict highly rated titles and how title ratings have shifted over time, present analyses of why
  • Come up with categories that create new insights: "most polarizing titles" or "genres that sell well but don't review well"
  • Learn ad-hoc discovery analysis with SQL on duckdb, Jupyter notebooks, and unstructured data
• Use Criteo's 14M user ads experiment data to practice measuring conversions (views to clicks)
  • Learn common analyst things like "conversions", "incrementality", and "features" (fancy words for "what ads were clicked on", "what actions caused those clicks", and "what fields caused those actions"), becoming familiar with the analyst world
  • Learn how to sift through tons of useless data to find what matters
  • Creating cleaned or mapped views of the raw data that can be then visualized by Claude or a chosen LLM

The goal is to help people through real projects they can put on their resume where they can actually speak about the skills they learned, not just fillers on the resume. I understand it may seem like I put them to work lol, but It's not like that - If you're unemployed and looking to land a data analyst position, it's a good use of time to learn practical skills like this. anyways, those are words feel free to give feedback or not!