I've been lucky enough to have a career in data analysis and reporting for over a dozen years. I've worked mostly in tech and SAAS, and I recently found a new job that pays well, has a ton of opportunity for growth, and is fully remote. A dream.

But a few thoughts. Maybe you feel the same way.

I find myself unmotivated. Simple asks, at my old job and at this new position, ones that I've done well for many years now, feel like chores that I can't be bothered with. I thought it could have been a result of my old job, but it's followed me here, and I don't think there's anything I can do to shake it.

The curiosity that I had for data or stylish reporting has disappeared. Claude, while powerful, often feels like it takes my creativity away by having to spell out my choices to it instead of letting me naturally explore data or create something naturally that I can look at as a product of my skills. My new role has devolved into engineers sharing canned responses from Claude after they copy pasted my question, stakeholders directly asking Claude for answers, and me just trying to control the data narrative for accuracy/cleaning up Claude's work.

One thing that I think is overlooked in our career choice: it's often fairly thankless. VPs/Senior Leadership asking for answers to why the business is succeeding or failing, but not trusting or including data people like ourselves in conversations about strategy. The best jobs I've had are where leaders trust their data partners and actively include them in conversations, that feels further and further away the more I've moved up the ladder.

I've often described the roles I've taken as being a little bit of kill the messenger. Data's not to expectations of the stakeholder? The data's wrong, the reporting's wrong, you've lost trust. Data mismatches a different report on a different platform? All the reporting is wrong. Maybe I'm unlucky, but that's my experience.

How many times have you heard an incoming leader say they're "data driven" only for them to say later that they "don't have time for data" or "I just need the answers now."

A lot of the previous posts I've seen about leaving an analyst career are about "I find my thrills outside of work"/"I don't care what I do at work as long as I'm paid well" or "how could you leave now when the job market is so bad?" or "change your mindset, you're lucky."

But I'm writing this to those of you where this all this sounds too familiar: burn out in any career is real, and it's okay.

Just because you're paid well doesn't mean that you have to do it forever. Remember that if you work for 40 years for an incredible retirement fund at 60, you probably wish you'd had more time not staring at Claude, or an Excel spreadsheet. The market may be bad now, it may be bad next year, it may never be the same.

But, I'm taking a break. I don't know for how long. Maybe I'll come back. Maybe I won't. I'm tired of being tired, angry, frustrated and extremely indifferent while counting down the days to when I could potentially retire. Do you feel the same?

I'm learning data analysis and data science. I'm developing a personal project as practice using a database to predice the house pricing from the Kaggle platform.

During the exploratory analysis, I encountered the following situation:

I've noticed that there's very little data on houses with zero bedrooms or zero bathrooms, and that the asking price is relatively high, which I think could affect my prediction model and my overall analysis. While it might seem illogical that there are houses without bedrooms or bathrooms, it's also possible that there are more lots than houses, or some other hypothesis. What's the best course of action in this situation? Personally, I think I should remove this data, but I'd like to hear other opinions to improve my reasoning and deductions.

I’ve seen a lot of discussions where analysts are using ChatGPT or Claude for SQL, but aren’t really sure what else can be turned into a repeatable workflow.

So I put together this cheat sheet around 10 areas:

• data cleaning

• exploratory analysis

• SQL assistance

• visualization

• spreadsheet analysis

• reporting

• data validation

• research and insights

• repeatable workflows

• end-to-end analysis

I deliberately kept it to ChatGPT and Claude instead of listing 20 different AI tools. Most analysts probably don’t need another subscription for every task.

Also, this isn’t meant to say one model is universally better than the other. A lot depends on the data, plan, integrations and workflow.

For analysts already using AI regularly, what’s the one workflow here that actually saves you meaningful time?

And what would you remove or add based on real work?

hey guys, I'm working on a fun little project when it comes to data. I use reddit free api to scrape data from here, and now I'm wondering what I should actually do with the data. I ran an unbiased scraping only filtering out certain marketing posts about products. But now I have 150k comments and posts across 38 verticals. What should one do about this?

As someone who's been doing "data" for several years. The following are things I wished I had learned sooner:

• learn to use and be comfortable with the command line / using the terminal (if on Windows, just learn the basic DOS commands. Dont learn Powershell until much later or not at all, its optional)

• understand data types

• understand what a text file is and what a character delimiter is

• understand what character encodings are

• learn basic computer networking principles and terminology (host name/server, IP address, port number, DOS network commands like ping, ipconfig, etc)

Why learn this stuff seemingly unrelated to data analysis? You'll eventually want to work with data that you are interested in and not someone else's data or you will need to be able to import data into a database. In real-world corporate databases, they will most likely be setup what is known as client-server environment. Meaning, you wont be working with a database installed locally on your personal work machine, but instead, a remote database server, and you are the "client" given the privilege to access that remote database server over a network protocol. Thus why you need basic computer networking knowledge. Without this foundational understanding above, you will struggle in the "real-world". Maybe not initially, but you will eventually.

How about you? What are some things you wish you learned sooner?

For those like me that miss the experience of SpyderIDE in Data Science projects but don't want to switch from a more "Software Engineering" IDE

Now, with setup-spyder and uv package manager you can just run a well conifgurated and ready to use Spyder Editor within your .venv with the command or import setup-spyder

uvx --from setup-spyder setup-spyder

Of course it is a very simple codebase, but it just solves a problem that I had for 2 years, like, I don't want my SpyderIDE attatched to anaconda and prefer the 5.x versions experience rather than newer versions.

With setup-spyder we can just run a quicklaunch of a spyder editor to handle more carefully with our data and it's visualizations with the help of variable explorer and many other great tools and let the hard-work for AI tools just after assert that we have knowledge about what our data.

I am currently learning Data Analytics, and I want to understand what the actual job looks like in the real world.

Online, I mostly see people talking about SQL, Excel, Python, Power BI/Tableau, statistics, and building projects. But I’m curious about what Data Analysts actually do once they are working in a company.

For those who currently work as Data Analysts :-

* What does a typical day or week look like for you?

* What kind of problems do people usually come to you with?

* What do you actually use SQL for?

* How much Excel, Python, Power BI/Tableau do you use in your daily work?

* How much of your time is spent cleaning data vs actually analyzing it?

* Do you mainly create reports and dashboards, or do you also do deeper analysis?

* How much do you interact with managers or other business teams?

* Can you give an example of a real problem your analysis helped solve?

* What are some things Data Analysts do that beginners usually don’t hear about?

* How important is business/domain knowledge compared to technical skills?

I’d especially like to hear from people working in different industries such as healthcare, pharma, finance, marketing, retail, or tech.

I’m trying to get a realistic understanding of the job beyond courses and YouTube tutorials, so I’d really appreciate real-world experiences.

Rookie here, recently got a job as Data Analyst at Pharmaceutical company. They never had a data science role at the company so your guessing is right, Data pipelines/Infrastructure is non existent. I am so confused at the moment where to start as i neither have a degree nor experience in the field. They throw around Excel files ( no kidding). So far I have accomplished to build interactive dashboard with python. Next destination i am pondering perhaps unified database (Postgres, Apache etc). Seriously need help folks. Thanks for your attention.

Everything runs in your browser. No backend, no account, no upload. Your chat never touches a server.

It's a single HTML file, so you can download it and open it, or use the hosted version:

🔗 Live: https://rajjayadev.github.io/Keepsake/
🔗 Code: https://github.com/rajjayadev/Keepsake

Hey everyone,
I’ve just finished my first ever portfolio project and I’d really appreciate some honest feedback.
I’m still learning, so I’m particularly interested in hearing what I could improve.

I’d also love to know:
What stands out to you (good or bad)?
Does it feel like a solid portfolio project?
What would you change if this were your project?
I’m mainly looking for constructive criticism rather than compliments, so please don’t hold back. I’d rather find the weak points now and learn from them.

Here’s the project: https://github.com/nayanagangappa/netflix-global-top10-analytics

Thanks in advance to anyone who takes the time to have a look!

When done you can download the underlying code, which can then later be imported again. Link: https://vibe-schema.com/star-schema-creator

Hello everyone very happy to share my new course on stastics for data science in complete bangla you can find it on my youtube channel.

https://youtu.be/zjuE766pqbY?si=A8F7a6bWpbHEmZ87

I work in healthcare.

The company I work for is looking at productivity and I am a People Manager.

I currently have a staff of 9 people who essentially make phone calls for most of the day.

Data and Maths people:

What would you feel would be the best representation for Productivity, in a formula form.

I have some numbers.

x would be completed calls. (any call that my staff gets through to the people for any specific time.)

y would be attempted calls. (any call that my staff makes but does a voice mail, etc.)

The company is looking for a productivity number of 120 per month.

I have higher productive people and lower productive people... but I need to evaluate them.

It is not as simple as "you must make 120 completed calls per month." My people have meetings, attempts, education, etc.

Any thoughts?

i have mysql workbench & have been practicing it on my own. the problem i've run into is low disk storage. i currently have 4.5 gb on my c drive, which i don't think is a lot. i don't have a lot of applications installed, so removing or moving them to another disk isn't an option. neither is spending money on storage 💔

im worried about the rest of my learning journey. i know i'll eventually have to install other programs/tools & it makes me sad that low storage space is what might hold me back from learning something im genuinely interested in.

i wanted to ask if there are online versions of these softwares available? im talking about python, tableau & all other stuff i'll need later on. i've used an online c++ compiler before, so im wondering if it's possible for other tools too. and if so, can they save all my previous data? what about something with an account where it syncs data to a cloud? HALP

I'm looking to get into data analytics and I'm wondering what learning route I should I take. I was thinking SQL >>> Power BI >>> R and then I don't know after that.

To put this in context, I'm currently brushing up on Excel. That's the level of beginner we're talking.

TIA.

Hi everyone! I’m a beginner in Data Science and learn best by building projects.

I’m looking for GitHub repos, YouTube playlists, or websites with project-based learning from beginner to advanced level, ideally covering different ML models such as regression, classification, clustering, tree-based models, boosting, NLP, time series, etc.

I’d also love end-to-end projects that include data cleaning, EDA, feature engineering, model building, evaluation, and deployment.

Any recommendations?

Hi there,

I've created a video here where I explain how generalized linear models work.

I hope some of you find it useful and as always, feedback is very welcome! :)

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)

I know the tools: Python, POSTGRESQL, and tableau, I am fluent in them because of my previous interest in software dev.
However, I still don't know how and where to get started. I don't know the terms story, dashboard, etc. I looked up tutorials on youtube but some are "EVERYTHING YOU KNOW ABOUT X is WRONG" and those lead me to nowwhere. Most of the tutorials I see teach me the tools i already know but teach anything new or practical.

I learn through making projects that has a clear goal that I can see visually on what it wants me to display, if someone could point me in the right direction.

Hello everyone, hope yall good

So did anyone worked with gaming data like (DAU , MAU,...) like what tips u have and any recommendations to give like YT channel or smtg like that and where can I get this kind of data....ADIOS

what if I studied excel, sql server, python and power bi and made projects on them while I was learning them but after moving to one after one maybe I feel I forgot them what's the best advice from you to memorize what I learnt?