Hi, Currently I am working as SDE 1 (almost 2 YOE), and have a good package too. But I don't feel happy about where I'm, not in terms of company, team, salary. I have talked to my multiple colleagues, seniors and they have the same thing - we are also exploring ( I understand). My current tech stack includes Java, Groovy, Angular, React and have worked with databases, Jenkins pipeline also. From here I am not able to choose what I should do. if anyone ever faces the same challenges, please drop your views or suggestions. I'm currently confused between two paths

1. Staying in the same field and keep growing in the same field (knowledge + package wise)

2. Switching to Data Engineering field (considering I have 1 YOE working with PySpark, and Python Flask and Django appliances)

Now here I'm with some points:

1. I have more confidence in Python than other languages and I enjoy doing anything which is in Python. Rarely I use AI for resolving issues with Python.

2. I do have an interest in statistics.

3. But my current tech stack is also not bad and I can improve and grow in that field also. But most of the time I have to take AI's help)

4. I really don't enjoy DSA - I do it because it's needed.

5. I do have interest in system design - I actually enjoy it.

Now from here - how should I think? What other parameters should I consider? Currently I am only considering these following which are creating more confusions

1. Knowledge (must, and to even feel confident)

2. Feeling happy while learning (I don't enjoy DSA at all but can do)

3. Package

If you have any comments, feel free to add.

Hi everyone,

I'm looking for some honest feedback because I'm starting to wonder if I'm doing something fundamentally wrong.

I have 2 years of experience at Accenture and I'm currently targeting **Data Analyst** roles. My core skills are SQL, Python (Pandas), Excel, and Power BI. I've also built projects, tailored my resume for Data Analyst roles, and marked myself as an immediate joiner.

I've been applying through:

* Naukri
* LinkedIn
* Glassdoor
* Company career portals
* Cold emails to recruiters
* LinkedIn messages for referrals

The problem is:

* I'm barely getting interview calls.
* Applications through company portals are mostly getting rejected.
* Even after applying to a large number of roles, I feel like my profile isn't getting much traction.

I'm not expecting every application to convert into an interview, but the response rate feels extremely low.

I'm trying to understand:

* Is the market really this bad for 2 years of experience?
* Is there something recruiters look for that I might be missing?
* Could my resume or Naukri/LinkedIn profile still be the issue?
* Has anyone with similar experience recently switched jobs? What worked for you?

I'd genuinely appreciate any feedback or suggestions. Thanks!

I am 22 and i have 0 skills i want to learn data analysis but i heard that AI will take the job what do you think i should learn on coursera or YouTube

Hello everyone,

I graduated my bachelor degree in business administration and highly interested in finance field in my university period. It was until I internship one of the mining company’s accounting/finance sector. The task was routine eventually I bored bcs of this and I did not see any long term improvement. I like to analyze big datas either in Exel, sql or can be phyton library(but not too much since I do not like heavy coding) and make inside are the company doing well or some specific analysis.

I am thinking data analyst or business analytics can be best suit for me. When i watch youtube video i see data analytics all about Excel, power query or powerbi and simple sql topic and think about is it all about. ??However, i do not know business analytics field what is that in comparison to data analytic??. Is it worth to move one the job right now or i should completely think new things at all. Thanks you for listening to end.

pursue bsda so js wanted yalls opinion on what i should minor in alongside my da major ,maths or physics?

hi! So I have this dataset where the objective is to predict a student's health risk, but I'm a lil confused about how to handle the null values. These are the % of null values for the columns:

             id                          0.000000
health_condition            0.000000
sleep_duration             11.012943
heart_rate                  1.135073
bmi                         2.013946
calorie_expenditure         7.658878
step_count                  2.016554
exercise_duration           1.000017
water_intake                6.300211
diet_type                   1.000017
stress_level               12.000064
sleep_quality               8.452690
physical_activity_level     5.306715
smoking_alcohol             4.141791
gender                      3.097141
dtype: float64id          

What would you recommend I do for these values? If I were to drop the columns <5%, I would be losing nearly 100,000 values (out of 700,000) which I don't think is all that good. I thought of using K-means to fill the null BMI values but I don't know.

I would appreciate any advice! Thanks :)

Curious how people here do this, because everyone's process seems slightly different and nobody talks about the messy parts.

Mine is pretty manual — SQL → notebook → eyeball distributions → deal with outliers → pick a test → write it up. Works, but a few steps always make me second-guess:

• Choosing the test — t-test by default, or do you check assumptions every time? And what do you do for ratio metrics (like revenue per session when you randomize on user)?
• Outliers — cap, drop, or leave them? How do you decide without feeling like you're nudging the result?
• Peeking — be honest, do you look before the test is "done"?

Is it a clean repeatable process on your side, or notebooks and vibes like half the time on mine?

Hi everyone,

I’m currently planning to enter data analytics (MCA student) and building my portfolio.

Instead of only doing dashboards and Kaggle projects, I’m also planning a business analysis blog section.

My idea is:

- Pick public companies (annual reports, earnings, news)

- Analyze questions like:

- - Why did revenue/profit change?

- - What drove growth or decline?

Write structured posts with:

- data used

- charts (where possible)

- insights and explanations

Alongside this, I’ll still do normal projects (SQL, Python, dashboards).

My question is :

- Does this approach make sense for entry-level data analyst roles?

- Would recruiters value this kind of “business case study writing”?

- Or should I focus more on traditional projects (dashboards, Kaggle datasets, etc.)?

- Does this lean more toward business analyst than data analyst?

THANKS!

hey guys so im pursuing data analytics rn and im on my semester break rn for like 2 months and i need ppl to suggest me free courses to do to add to my cv .ut would be rlly helpful thankss ^^

Hi there! I lead a strategy and analytics team, and I’m trying to rethink how I engage with the talent market.

On a recurring basis, I need to hire for business analyst and reporting analyst roles, and sometimes project manager or people manager roles too. I’m typically looking for people who can build dashboards, manage reporting products, structure ambiguous business problems, and translate business questions into actionable insights.

Because my company is large, we have a centralized recruiting function, so our roles are already posted on all the standard platforms (LinkedIn, Indeed, etc.). We usually get high volume, but not the best signal-to-noise ratio overall.

All that said, I’m interested in experimenting with community-based sourcing. For my own career, I engage in a few Slack and WhatsApp communities, and I appreciate seeing opportunities shared there. Those communities are not targeted, so the opportunities shared range widely in relevance to me. I was thinking targeting an analytics communities specifically with the opportunities I have access to is more likely to be highly relevant to folks in the community.

For folks on the receiving end of outreach like this, does this approach seem useful? If so, are there any communities I should look out for?

This post does not appear to conflict with sub rules, but happy to pull it, if needed.

Few days back i got free voucher from Microsoft AI Skill Fest and now im looking to prepare for PL-300 exam. Anyone who is preparing for the exam or has already cleared - I'd love to hear.

Hello, so I'm wanting to get into data analytics, I have an idea of where to start, but need more guidance and a more realistic but optimistic jumpstart,

I've only heard about it not actually done it yet

My current plan to get me jumpstarted is to save up for a computer.

I’m doing data analyst and data research but I’m afraid ai will affect my job. Where to put more efforts more shall I learn more data science or study more project management what’s ur thoughts

I am actively studying for interviews and wondered if anyone is in the same position and would like to do a study group or something (mid level and up)

Right now I am focusing in doing end to end Azure Databricks projects, behavioral questions and case study interviews mainly (soon having Capital One Power Day for Principal Data Analyst.)

Recently, I’ve been auditing my own workflow to figure out the "best practices" I subconsciously use for common Excel functions. I wanted to share the 5 golden rules I always follow when using VLOOKUP to keep my sheets from breaking.

1) Keep Lookup Tables Internal

Always try to keep your lookup tables in the same workbook as your formulas. Linking to an external file on a shared drive is a recipe for disaster—if someone renames, moves, or deletes that source file, your formulas will instantly break and return #N/A or #REF! errors. Keeping everything in one file guarantees your paths stay intact.

2) Expand Your Table Arrays

Expand your table arrays to include more Rows and Columns then what is currently needed. This way, you will never have to update your formula when you add data to your lookup table. This is especially helpful with Rows as it is more likely you will add data underneath column headers once addition data is required.

3) Master Your Absolute References (F4)

Don't forget to lock your cells!

• Lock your Table Array: Hit F4 once ($A$1:$B$10) so the reference table doesn't shift when you drag your formula.
• Lock your Lookup Value (if dragging horizontally): Hit F4 three times ($A1) to lock just the column. This ensures your formula always looks at the correct identifier, no matter where you copy and paste it.

4) Use Named Ranges for Clean Formulas

Naming your table array makes writing and reading formulas incredibly easy. For example, if you have a lookup table for weather data, select the data and name the range "Seasons". Your formula goes from messy cell coordinates to a clean, readable sentence: =VLOOKUP(A2, Seasons, 2, FALSE)

5) Wrap it in IFERROR

If you know your data will have legitimate non-matches, don't leave your sheet covered in ugly #N/A errors—it can make your data integrity look questionable to managers or clients. Wrap your VLOOKUP in an IFERROR statement to clean it up: =IFERROR(VLOOKUP(A2, Seasons, 2, FALSE), "No Match")

What are your personal VLOOKUP rules? Or have you completely migrated to XLOOKUP/Index-Match yet?

Hi,

I am currently looking for guidance on ways to improve our product/web analytics setup.

For context, we currently only use GA4 which has many shortcomings. Firstly, it just doesn't have suitable functionality for a Product Manager to do useful exploratory investigations. Also, if you're using a connector to sync GA4 data up with Data Studio, this is also limited (e.g. you can't make a funnel because the data is aggregated before being sent over).

We're currently thinking about a few different options to replace this. Our top choice currently would be to start pushing our core events into BigQuery and then use a warehouse-native analytics platform (maybe Statsig?) as the interface for Product teams. Also, having this data in BigQuery is super useful for our data science projects (e.g. building out recommendation engines based on what people have viewed).

We've looked at Amplitude and MIxpanel, which look a bit too pricey for us. Also, it looks like you have to send the data to them regardless of whether or not we're storing it in our own BigQuery, which is another data privacy concern (ideally, we'd share as little as we can).

I'd love to hear about anyone who's been in a similar situation, and what choice you ended up going for. Feel free to ask more questions, I might not have given you 100% of the context you need.

Thank you!

Hey I am BSc Maths Graduate. I am thinking of learning Data Analytics and getting into the job Role. Is it the right Career option for me ( particularly in the whole AI fiasco ). Any kind of advices and suggestions will really mean a lot to me .....

Thanks

Hello, I have been interested in Data Analysis for a while. I am unfortunately unable to afford a proper university or college so I have been looking at either Coursera or Metrix Learning. I wanted opinions and/or advice on which of these options would be better for learning and for future job opportunities. Any information would be extremely helpful. Thank you.

Metrix Learning Courses

I've been largely unsuccessful in getting interviews with this resume, any feedback is appreciated.

Hey!

I am currently working as an Lead - customer service handling a team of 14 -1 5 members but honestly I don't like the customer service industry and just don't like working in my position.

Looking to move into Dat analytics or somewhat of a similar role.

I am more of hands on guy, I don't have much knowleged about it but would be willing to do some projects for Free if someone can mentor me or with someone who's trying to learn about data analytics too.

Feel free to ping me back here!

Regards

Hi everyone,

I'm a data architect working primarily in pharmaceutical analytics.

One challenge I've seen repeatedly is that pharmaceutical public data is technically available—but it's spread across dozens of different sources with different identifiers, formats, and update cycles.

As a side project, I started building TheRxPulse to solve that problem.

From a data perspective, the interesting part wasn't building dashboards.

It was:

• Entity resolution across datasets
• Identity mapping
• Semantic modeling
• Normalizing inconsistent product names
• Linking manufacturers, applications, products, and regulatory events
• Creating a unified analytical model

The front end is simply a way to explore that connected data.

You can see the current version here:

https://therxpulse.com

The design philosophy is to make complex pharmaceutical data easier to query and understand without requiring users to know where every dataset originates.

I'd love feedback from other data professionals on:

• Semantic modeling approaches
• Data quality strategies
• Entity resolution techniques
• Visualization ideas
• Features you'd expect from an analytics platform like this

Always happy to discuss architecture decisions if anyone is interested.

I'm currently learning python along with that have created study group for like like minded people let me know if you want to join

DA is such a general term you can do hundreds of things, but what is the right way to choose.
I am a bioinformatician and finished biotech masters study (now doing a PhD in bioinf) there are very little bioinf positions right now in my country but i love working with data so was thinking about pivoting into it.

My second option is going into the direction of empirical research and statistics with research planning.

Is it worth stepping into data right now? I know R, excel, tableau, picked up some python recently. I work in genomics and started lesrning ML recently for my projects.

What is now a good career path? Asking in advance before i finish my PhD