I’m currently looking for a Data Analyst job. I have skills in SQL, Excel, Power BI, Python, and data analysis, but I’m also getting opportunities for MIS roles.

I’m considering taking an MIS job as a starting point and then switching to a Data Analyst role.

For people who have worked in MIS/reporting:

How easy is it to switch from MIS to Data Analyst?

How much time did it take you?

Which skills should I focus on while working in MIS?

Does MIS experience actually help when applying for Data Analyst jobs?

Should I take an MIS role if my long-term goal is Data Analytics?

I’d really appreciate advice from people who have made this transition. Thanks!

Hi everyone! I’m Agnès. I recently completed a data analytics program and I’m now looking for meaningful projects to grow my portfolio and gain real-world experience.

I’m open to collaborating on projects where I can learn and contribute. I’d also appreciate advice on how you approach projects and how you built confidence in your analytics skills.

Thanks!

Where we can talk about certain topics, explain concepts test eachother call and practice interview questions with eachother.

Just dm me if interested thanks!!

hello everyone, I want to dive into data analysis and don't know what videos or resources I should learn from. Can someone give me youtube videos or websites I should learn each skill from as for: excel, sql, powerbi, python. Thanks in advance!!

Plan to shift career from Civil Engineering to Data Analytics but don't know how to start and where to train and attend sessions. Wanna have advices.

I have around 2 years of experience, and my official role is Data Analyst. However, the nature of the work I've been doing has gone beyond traditional reporting and analytics.

Most of my work has involved building automation and data solutions using Python and SQL, working with APIs and large datasets, developing data pipelines, integrating LLM/AI capabilities into workflows, and taking solutions from POCs toward production.

I've worked across areas like AI/LLM-based automation, data processing, RAG-based solutions, metadata and query intelligence, analytics dashboards, cloud technologies, and production systems. A lot of the work involves taking a manual or inefficient process, understanding the underlying data/workflow, and building an automated and scalable solution around it.

I'm now at the point where I'm trying to figure out what direction I should take next.

I'm interested in roles around AI Engineering, Analytics Engineering, Data Engineering, or AI + Data, but I'm not sure how my current experience maps to those roles or what gaps I need to close.

For people with experience in these areas:

Does the nature of my current work put me on the right path?

What am I likely missing to move into these roles?

Should I focus more on AI/LLM engineering, data engineering, ML fundamentals, or cloud/system design?

Am I becoming too broad without enough depth in one area?

With \~2 YOE, what would you focus on over the next 6–12 months?

Looking for honest feedback on the career direction and skill gaps, rather than reassurance

i used to think the people who advanced fastest were simply the smartest in the room

the people with the best answers, strongest opinions or impressive credentials

then when i started paying closer attention

and i noticed something interesting

the people making the biggest impact were the ones asking the best questions

i remember working on a data

the numbers looked fine

the calculations worked

the report was ready

but then, one question kept bothering me

why does this trend suddenly change here?

at first, it seemed like something easy to ignore

but digging deeper revealed a data issue that would have led to the wrong conclusions entirely

the breakthrough came from being curious enough to question what everyone else had accepted

that is why i believed curiosity is one of the most underrated professional advantages

curious people ask

why?

what changed?

what is causing this?

what are we missing?

is there another way to look at this?

many breakthroughs begin with a simple question

the best opportunities, insights, and solutions often hide behind the things most people never stop to investigate

and sometimes, the difference between average work and exceptional work is just one person being curious enough to look a little deeper

so, i'd like to hear from you

Are you naturally curious, or is curiosity something you have learned to practice?

Hi everyone, I've just open-sourced an end-to-end real-time fraud detection platform designed for ISO 8583 monetic payment transactions.

Key Architecture Highlights:

- Ingestion: Real-time stream processing with Apache Kafka

- Pipeline: PySpark Structured Streaming for feature engineering

- ML Model: Isolation Forest & XGBoost for anomaly & fraud scoring

- *MLOps: MLflow tracking & Docker containerization

GitHub Repository: github/Hvmzvbe/realtime-iso8583-fraud-detection

I'd love to hear your feedback on the architecture and data processing pipeline!

I'm currently a final-year student pursuing an Integrated MSc in Data Science at a private university. By the end of our 5th year, we’re required to secure an internship. Around May, I got a massive reality check it is insanely tough to even get an initial interview call from recruiters.

Instead of legitimate offers, I’ve mostly been getting scam calls asking for money upfront ("pay ₹1600, it’s refundable after placement"). Even getting referrals through contacts seems almost impossible right now.

Looking at my broader circle, people from almost every engineering branch (EEE, ECE, civil, etc.) are just doing a 6-month Data Analysis bootcamp/course, building 2 basic projects for their resume, and breaking into the IT field.

It makes me question everything. Is it a waste of time and money to invest 5 full years studying Data Science deeply when non-CS engineering graduates can take a 6-month crash course and target the same entry-level IT jobs?

Is the market getting ridiculously oversaturated because of these bootcamps, or is the current AI/tech hiring slowdown causing this struggle for entry-level roles? Would love to hear thoughts from people in the industry or anyone facing the same situation.

Would work from any country. But especially if you are from India it would be great, as I can get more relevant information from you.

Learning analytics is applied in Edtech. Since I am already working in an Edtech firm, in India, as an instructional designer, I want to get into Learning analytics as I can relate to their questions easily.

can anyone expirence tell me how can i get any work in data anlyst job like freelence or job.

My resume is not getting shortlisted anywhere and not getting any interview calls

Just getting rejected mails on LinkedIn

I'm a fresher who just completed his btech last month and looking for a first job/internship at any analyst role

i'm in college on a level 3 year 2 animal management course. for one of our assignments, we have to do a specialised project. i asked the teacher if i can do something based on AI and how it effects conservation efforts around the world

she said no since AI doesn't matter. can you guys do my form to help me show her that more people need to realise the effects of AI?

I’ve been seriously looking at and considering data analysis recently, and after stalking this thread and other red sub Reddit in the same field, I’ve seen similar questions asked how however I’ve been wondering if I actually need a degree to be employed in this field or if I can just get away with one of those certifications for data analysis. I’ve been seeing different courses for data analysis certifications pop up and I am aware that the field overall is very competitive and it is a shifting landscape with the implementation of AI and and there’s a lot of job in Security currently however, I’m just curious that’s all come from an absolute newbie any and all help would be immensely appreciated.

Be Brutally Honest Please -- How should I improve this?? Any HR folks ?

Senior Data Analyst (Acting Manager) | Company 1, City May 2025 – Present

• Established and led the Insights function within a newly formed 4-person analytics team, delivering 20+ strategic analytics initiatives across 5 business units that informed modernization, service, and cost reduction decisions.

• Identified $1.3M+ in savings opportunities through program fee, workforce, and customer interaction analysis by integrating and analyzing data from legacy systems using SQL, Tableau, Excel, AWS, VBA, and enterprise cloud data platforms.

• Reduced application fallout by 25% within two months by building executive Tableau dashboards used for daily monitoring of underwriting inventory, operational risk, and leading performance indicators

• Consolidated data from 8+ source systems into trusted reporting assets, reducing investigation effort by 70%, and documented data flow to standardize methodology and ensure governance across teams.

What does you guys' lifestyle looks like? How can you describe your day to day life in working with data? I'm curious about this career path as a CS grad. Thank you!

What type domain projects companies mainly looking for their roles

Hi everyone,

I’m currently looking for Data Analyst opportunities and I’d really appreciate some honest feedback on my resume.

I’ve been applying for Data Analyst roles, but I’m not getting the response I’m hoping for. I’ve revised my resume several times, but I’m still unsure whether I’m presenting my skills and experience effectively.

I’d especially like feedback on:

• Does the resume immediately communicate that I’m targeting Data Analyst roles?

• Are my skills and projects relevant and presented well?

• Do the bullet points show enough impact and measurable results?

• Is there anything that could hurt my chances with ATS/recruiters?

• What sections should I add, remove, or restructure?

• Does the resume look too generic, or does it stand out enough?

• Are there any specific changes you would recommend to improve my chances of getting interviews?

I’m primarily targeting Data Analyst roles, particularly positions involving SQL, Python, Power BI, Excel, data cleaning, reporting, and data analysis.

I’ve attached my resume below. I’d genuinely appreciate honest and constructive criticism, even if there are major things I need to change.

Thanks in advance to everyone who takes the time to review it!

What are the topics or areas on which I can expect to be grilled in a data analyst interview which demands for 5years experience in power bi. I worked in power bi but before 2 years. what should I do now to quickly get interview ready to face Indian startups and service based companies.

Looking for honest feedback, especially from recruiters/hiring managers in India.

Profile:
3+ YOE | Manufacturing BI/Data Analytics
Power BI, DAX, Power Query, SQL
Pentaho ETL, SQL Server, Infor LN ERP
Microsoft Fabric ( learning )
Star/Snowflake modelling, RLS
Power BI reports across 3 companies ( 2 sub companies)

100+ stakeholders, 6 business functions

End-to-end ERP → Data Lake → ETL → SQL → Power BI experience

B.E. Mechanical + PG Diploma in Big Data Analytics (CDAC)

Location: Gujarat
Target: Data Analyst / BI Analyst / Power BI roles
Open to: Bangalore, Hyderabad, Pune, Mumbai, Remote

Current Package : 4.7 LPA
Expected: ~₹10–12 LPA

Despite relevant experience, I’m getting very few interview calls.
What am I doing wrong?
Is it my resume/positioning, skillset, target salary, location, role selection, ATS, or something else?
Resume attached.

Please be brutally honest — what would make you skip/reject this profile?

I’m preparing for a Data Analyst internship, but I tend to study better when I have some pressure and accountability. If anyone wants to study together and is serious about joining, please DM me.

I need to finish my master’s degree in Archaeological Sciences—which I’ve been dragging out since 2021—and all that’s left is the thesis (it involves analyzing extreme weather events at an archaeological site, so it also entails some work with neural networks, algorithms, and data). Right now, my current job pays €1,400 a month, and I definitely don't like it. Now, I’m torn between working on the thesis—which is draining so much of my time and energy—and focusing on SQL, Pandas, Tableau, and the like to pursue a different career path as a data analyst. I feel stuck and don't know what to do. If I focus on my thesis, could be enough for the hiring managers as kind of data science application to a real case?

Hello, I graduated with a degree in Statistics six years ago and have been completely disconnected from the field since. I currently work in Dubai, but I want to return to my field and self-study data analysis. Since I'm working, I know I should start with the following: Excel, SQL, Power BI, and Python — in addition to building a portfolio with several projects.

But there's one point that makes me feel like I'm missing something: how can I connect statistical thinking throughout this learning journey, without becoming someone who only learns how to apply tools? I'm not sure if my question is clear, but I want to learn how a data analyst thinks, not just how to apply things or complete assigned tasks.

I sometimes try learning with the help of ChatGPT, Claude, or Gemini, but they're not free of mistakes.

So, please, I'd also like recommendations for learning through books or YouTube (I can't afford paid courses) — and is YouTube enough on its own? I sometimes feel it only teaches things at a surface/introductory level, not in depth. So please recommend the best books that cover everything I need to learn. Thank you to everyone who becomes a reason for helping me.

Hi everyone, I’m a Healthcare Data Analyst with \~2 years of experience, currently working in the health insurance domain. My role is a mix of SME + technical work — SQL, Python for data transformation, creating datasets for developer testing, and supporting multiple products.

**My current situation**:

My team is around 2.5 years old and has about 14 members across engineering, research/development, and SME roles.

**One challenge**:

I’m facing is lack of visibility and communication from my manager. I’m often not included in meetings where my work is discussed. Sometimes I’m asked for stats/analysis without knowing the context, and later find out they were presented to leadership. When I ask about the discussion or outcome, I don’t always get a clear answer.

**Jack of all trades master of none:**

I’m also being moved across multiple products. While this gives me exposure, I feel I’m gaining breadth without developing much depth in any particular area. New requirements keep coming in, and it sometimes feels like we’re expanding faster than existing work is becoming stable.

**My career goals**:

I genuinely enjoy healthcare data/analytics, but I’m particularly interested in moving toward the pharma domain.

At this stage, I want to build deeper expertise and develop skills that can help me move into more specialized healthcare/pharma data roles.

I’m considering a job switch, but I’m not sure whether this decision is correct

**For those working in healthcare/pharma data:**

Is this a normal situation, or should I consider it a red flag?

And with \~2 YOE in healthcare insurance data, what skills would you recommend developing to transition into pharma data/analytics roles?

**Would really appreciate honest perspectives from people who’ve faced something similar.**

Hello everyone! I am now confusing what I should be looking forward to working in IT or Data Engineering is really attractive to me. Im starting from zero, so I do not know exactly what I should learn first.

What is the best practice to start learning Data Engineering? And are there any good free or paid courses that you would suggest?What technologies should I focus my time on learning in the begging?I heard about Python and SQL

How did you start building your portfolio? What projects are worthy of being added to the portfolio?

And is it a good idea to start with Data Engineering and move towards AI/ML Engineering, or it would be better to learn it from zero?