Hey guys I just finished my Google certification and got offered a job as a data scientist at a small bank (Goldman Sacks, you probably haven’t heard of it). They’re offering me $200K, work from home, unlimited vacation, and a company car. Is this pretty typical for a starting salary or should I hold out for something better? I’m willing to take a pay cut for some experience but I’m worried they’re exploiting me. thanks

Hey everyone, I wanted to share a tool that's been a game-changer for me: TaskBotAI (www.taskbotai.com). It generates Excel formulas and SQL queries based purely on your plain English instructions. No more hours spent on Google trying to figure out complex formulas or queries!

Just type something like "Get the average sales per month for 2022" and TaskBotAI will generate the appropriate formula or query for you. It's like having a personal assistant for all your Excel and SQL needs!

Give it a spin and let me know what you think. It's saved me a ton of time, and I hope it can do the same for you. Cheers!

Ive been overemployed since 2020. These companies are not your friends, your coworkers are not your friends. I have worked 2 remote data/BI jobs from 2020-2021 and 3js from 2020-2023. Layoffs happen, no business can be trusted to not screw you over. If i were you all id be looking to do the same. Rant over...

Heres the general idea on how ive been getting OE to work for myself. I seek out remote jobs that require industry expertise in a certain field where i can automate my job with SQL,DAX,PLX. These codes can be used over and over again by just changing out a few parameters to meet a business need. I dont tell anyone without anonymity and no one is the wiser. Last year i raked in > $200K living in a LCOL area. This year will be around 250-280K.

Take no prisoners, get out there and make money

I have created a dataframe called semi_df in previous cells. but when I close and open the same python file in my jupyter notebook the cells below cannot read the data frames in the cells above them i have to go to the cells and hit shift +enter again and again so that the cells below can read the code above them...

I recently accepted an offer for a data analyst role (small team would consist of me one other analyst and a manager) at a retail company. I know the range for a what a data analyst gets paid varies greatly. But is 95k on the low or high end?

In this economy I’ll take what I can get lol but just wondering!

I am an analyst with a heavy data cleaning and reporting load and I have been running AI assisted Excel tools for about 6 months now. Most of what I read online is basically marketing, so here is the honest version.

Actually worth it:

- Data cleaning speed. Dedup, standardization, reshaping that took 20–30 min of manual work now takes a couple of minutes. This is the real killer app.

- Not needing VBA for one off automations anymore.

- Quick anomaly/trend spot checks on big sheets before I commit to a deep analysis.

Still annoying:

- Platform maturity. The good tools are still Windows first and the free tiers are basically useless.

- You still eyeball the important numbers before anything goes out but honestly I would review a junior analyst’s output the same way, so that is more on me than the tool.

Where I landed: I am using Mica Excel local AI that runs inside Excel, plain English commands, actually edits the sheets and plays the process back live so you can see exactly what it did instead of trusting a black box. A few colleagues at work use it too. Everyone is still a bit cautious, we all do a manual review pass before anything ships but nobody’s gone back to doing it all by hand.

My verdict: yes, worth it for cleaning and automation, and I’m keeping it. Question for the sub: for those of you on these tools long-term — did the value hold up after the novelty wore off? And what’s your actual workflow for trusting the output?

Hi guys,

I'm just getting into Excel and analysis. Just how much ChatGPT is helpful, reliable and precise when it comes to tasking it with anything regarding Excel?
Are there any tasks where I should trust ChatGPT, and are there any tasks where I shouldn't?

Does it make mistakes and can I rely on it?

Cheers!

Hi everyone, as the title suggests would having a pink balayage look unprofessional in our industry?

I know this is a silly question, but I’m a trans individual and I don’t know if having a rose gold hair color on top of being trans ruin my chances of getting another job lol.

For reference, I’m currently a WFH data analyst but on a contract which will end in August. I have 3 years of experience, but I’m just worried about people labeling my style as “unprofessional”.

Thanks for the help!

I'm new to this ... read somewhere Power bi is not on MacBook and excel is shit on mac ! Anyone who uses macbook here !

The secret life of SQL caps... revealed!.The great SQL CAP-ital debate: a choice, or a relic of the past?.

For years, I've seen developers passionately argue for or against writing SQL keywords in all caps..Some argue it improves readability, making keywords stand out from table and column names..

Others, like the Skeletor in this meme, find it an unnecessary chore, especially with modern IDEs that beautifully highlight syntax..But did you know why this practice even started?.

It's a fascinating peek into SQL's history.

.Back in the early days of SQL, when terminals were green-screen, monospace text was the norm, and syntax highlighting was a futuristic dream, distinguishing between keywords and identifiers was genuinely difficult.

.Capitalizing keywords was a pragmatic solution to enhance readability in a visually limited environment..It wasn't about style; it was about clarity.

.So, while today's sophisticated tools might render the "all caps" rule obsolete for some, it's a testament to the ingenuity of early developers solving real-world problems with the tools they had.

.It's a quiet nod to SQL's legacy, a subtle reminder of how far we've come..What are your thoughts?

Do you embrace the caps, or do you let your IDE do the heavy lifting?

#data #datascience #dataanalysis #dataanalyst #dataanalystjob #datajobs #datasciencejobs #python #pandas #seaborn #plotly #SQL #database #programming #coding #techhistory

Figured this has been asked before but thought I might ask it anyways. Can I land an entry level Data role with the google certification?

Im working as a graduate data analyst on 28k in England, outside of London. I was just wondering if anyone knows at what point I’m no longer a graduate anymore (is it like 1 year or 2…). Also once I’m not a graduate what salary should I expect for my job?

I did a degree separate to anything data related. I am starting a degree in data analytics next year which lasts 3 years and doing it through the company.

I had no prior experience before getting this job but now I’ve automated the 2 processes in the business with python, done few power bi prokect and overall data cleansing. I am the only person doing anything data related in the company so my responsibilities span over a lot of the company

Hi everyone. Do you think that with the explosion and development of artificial intelligence today, it will have an impact on the data analysis industry? Will this boom mean the industry will need less data analysts?

Why do data analyst jobs require python, SQL and R despite the several no-code, high quality and feature rich GUI based tools available today (e.g. Power BI, KNIME, Talend, List goes on) which can sort out 80% of your use cases, which can bring you data visualizations looking much much better than whatever you carved up using 100 lines of python code and which can extract data from 80% of the types of data sources out there?

Hello! I tried to post this on r/statistics but the automoderator blocked. I would appreciate tips from other suitable subreddits.

Many of you must already know this, but Brazil is in chaos after the elections.

I'm brazilian (sorry about my english btw) and our supreme court already warned us that accusations of fraud in the elections can lead to prison. Believe it or not, this is how our "democracy" works. So I don't know if we can do anything about it without mobilizing this information in other countries.

I'm not from the statistics/data field and that's exactly why I'm here. Below is an analysis posted anonymously that points out signs of fraud. There is a PDF file in the link http://brazilwasstolen.com/ (I hope they dont take it down, but I think they will and soon). The downloads of the data is the first option and the presentation in english is the second one.

Basically, our elections are electronic and we have a lot of models of ballot boxes. The ONLY ballot box that is auditable is the 2020's model, as shown by the report, Bolsonaro has significantly less votes than Lula in the previous models (non-2020 and unaudible).

There is a huge movement here to ask for help, to create more analisys or even recreate this one (if anyone would like to do it).

Some more context about the situation: Alexandre de Moraes is a judge of our Supreme Court that has been manipulated the media in favour of Lula's campain in this elections (this was the same court that "buried" Car Wash accusations against Lula in the past). There is a lot of news inside and out of Brazil that tells another story, but a lot of people here are protesting because; we believe there has been a fraud and want another election; we don't want a criminal running our country (again); we don't want to end up like our neighbors (Venezuela, Argentina...).

Anyways, maybe this won't reach a lot of people, but we have to try.

I’m very uncomfortable with AI. I haven’t ever used it in my personal life and I do not plan on using it ever. I’m skeptical about what it is being used for now and what it can be used for in the future.

My employer is a very small company run by people who are in an age bracket where they don’t really get technology. That’s fine and everything. But they’re really pushing all of us to use AI to see if it can help with productivity.

I am stating that I’m uncomfortable, however I do need to also explore whether this can even benefit my role whatsoever as a data analyst.

For context, in my current role I am not running any Python scripts, I am not permitted to query the db (so no SQL), I’m not building dashboards. Day to day I’m just dragging a bunch of data into spreadsheets and running formulas really. Pretty archaic, it is what it is.

Is anyone else dealing with this? And is there any use case for AI I can explore given what my role entails at this company?

Do you think this is useful? I have 2.3 million facts, 1.8 million price bars, and all of the FRED data.

I’m new to data analytics and stuff

Ngl already got the basics n stuff down for python pandas is there any need to learn SQL? Since I already learnt pandas .

A bunch of people asked me how I built the Excel dashboard that got me 1st runner-up at a recent competition, so I made a video walking through the whole process.

Steps I followed:

Generated synthetic sales data using AI (Python script → CSV)

Imported into Excel, cleaned it (handled blanks, fixed formatting)

Built a Pivot Table

Added a Pivot Chart + Slicer

Arranged everything into a final dashboard

I also planted a deliberate mistake somewhere in the process — partly to make it more useful as a learning exercise, partly to see if people can catch it before I explain it. Curious if this community can spot it faster than my Instagram/LinkedIn audience did 😅

Happy to share screenshots/more detail in the comments if that helps narrow it down.

Checkout profile for video(Not for promotion, just for learning)

Project: Employee Attrition

I just finished my tableau dashboard and would love some honest feedback about it before I put it on my resume.

It consists of 3 interactive dashboards.

• Executive Overview : KPIs, attrition rate, employee distribution

• Attrition Analysis : Salary, department, and absence-related insights

• Employee Performance & Risk : High-risk employees, overworked analysis, and department risk levels

The dashboards include navigation buttons to navigate from one dashboard to another.

Would also like some advice on how to get into a Junior Data Analyst Role.

Working through a personal project analyzing ~11 months of water-quality sensor data (river monitoring buoy, ~30k readings, 10-30 min intervals), and ran into a good lesson about validating findings before trusting them.

The setup: turbidity (water clarity) readings looked mostly stable, except one week where it spiked hard and stayed elevated for 7 straight days.

What I did before trusting it:

1. z-score flag on daily means (|z| > 3) → flagged that week

2. Rebuilt the check in SQL with a HAVING-filtered aggregate query, completely independent logic → same week

3. Wrote a SQL window-function streak detector (partition/reset-group technique), zero reference to the earlier results → same exact 7-day window

4. Weeks later, refactored the whole thing into a tested Python module and reran it fresh → same result again

Four different methods, at different times, landed on the identical window. At that point I was confident the anomaly itself was real, not a fluke of one method.

Where I still got it wrong: I assumed the cause was probably a rainfall/runoff event. Published that interpretation.

Later, going back through my own gap-analysis, I noticed the anomaly window started the instant the sensor came back online after a 30-day equipment outage. A second, smaller anomaly also bordered a separate outage. That timing is at least as consistent with post-outage sensor drift or biofouling as with rainfall, and my dataset genuinely can't distinguish the two.

Had to go back and correct the interpretation across the whole project once I caught it.

Takeaway I keep relearning: validating that a pattern is real (multiple independent methods agreeing) is a completely different exercise from validating why it happened. I conflated those two the first time.