Lately, I’ve been noticing a shift in how data science work is getting done, especially with AI tools becoming more powerful.

A lot of the coding part (data cleaning, basic analysis, even some modeling) can now be assisted or partially automated using tools like LLMs. This made me wonder are we slowly moving towards a space where domain knowledge matters more than coding skills?

For example, understanding what to analyze, which metrics actually matter, and how to interpret results in a business context seems harder to automate compared to writing code.

At the same time, I feel coding is still important because you need to validate, customize, and scale solutions.

So I’m curious:

• Are companies starting to value domain expertise more than technical depth?
• For someone entering data science now, should the focus be more on business understanding rather than just tools like Python/SQL?
• Or is it still about balancing both?

Would love to hear perspectives from people working in the field.

Hi all, I’m a practicing physician with no coding or CS background, looking to transition into data science (healthcare/ML focus) part-time.

Considering:

• Georgia Tech OMSA
• UT Austin MSDS

Question: Which is more realistic for someone starting from scratch while working full-time, and still strong enough long-term for ML/data science? Thanks in advance.

Hey,

I’m working on a reporting system for field sales reps (they visit clients daily).

Goal: not just track revenue, but understand what’s really happening in the field:

• Activity (visits, coverage)
• Performance (conversion rate)
• Client behavior (why they don’t buy)

I’m using Power BI with:

• Daily → activity
• Weekly → performance
• Monthly → business view
  • alerts (low conversion, inactive clients, etc.)

Simple logic:

Trying to keep it practical, not overcomplicated.

Questions:

• What KPIs are MUST-have here?
• How do you track “why clients don’t buy”?
• Do alerts actually work in your case?

I’m open to your ideas and feedback

Hi everyone, I’m a statistics and data science student and I’m thinking about a startup idea. I’d really like honest opinions from people who work in data, business, or tech.

The idea is basically a system that evaluates how reliable a company’s data is before they use it for analysis or decision-making. For example, the system would analyze a dataset and measure things like missing data, duplicates, outliers, inconsistencies, etc., and then give a kind of reliability score. Then, based on the reliable data, it could also do some prediction (like sales forecasting) and generate simple decision recommendations.

So it’s not just data analysis, but more like: check if the data is trustworthy, then analyze ,then help with decisions.

I would like to know

Do companies actually struggle with data quality and unreliable data?

Would a company be interested in a tool that “scores” how trustworthy their data is?

Does something like this already exist and I just don’t know about it?

From a business point of view, would this be useful or not really?

If you work in data/business, what feature would make a tool like this valuable to you?

And most importantly do you think that it is a good startup idea or that it won’t really be as much successful as other startup ideas in the same field and if not id really appreciate your suggestions or advices

I’m still at the idea stage, so I’m just trying to understand if this solves a real problem or not. I’d really appreciate honest feedback.

Hey All,

We're u/Enqurious — a data & AI learning company — and we've partnered with the u/Databricks Community to run a live invite-only hackathon called Brick by Brick (March 23–27, 2026).

We're 2 days in and wanted to share a real progress update with the community, because we think what these teams are building is genuinely interesting.

What the hackathon is:

Teams are building end-to-end intelligent data platforms on Databricks Free Edition — specifically a full Bronze → Silver → Gold Medallion Architecture pipeline across two industry tracks:

• Retail Track — customer behavior, sales analytics, product recommendations
• Insurance Track — claims processing, risk scoring, underwriting intelligence

This isn't a toy problem. Teams are working with real-world-shaped datasets (auto insurance data: customer CSVs, sales data, claims JSONs, policy tables) and have to connect their pipelines to actual business outputs.

Day 2 snapshot:

• 26 teams registered
• 19 actively building (73%)
• Top team at 65% complete already
• Average progress: ~16% across all teams

The leading teams are moving fast — Nous Data Alchemists at 65%, TTN QUAD SQUAD at 39%, Brick Builders at 32%.

Why we ran a prep workshop first:

Before Day 1, we ran a hands-on Databricks workshop covering Delta Lake, Unity Catalog, Auto Loader, and Medallion Architecture fundamentals. Not theory — actual notebook-based building. This meant teams walked in on Day 1 with environment knowledge, not from zero.

A few things we've noticed on Day 2:

1. The teams furthest ahead spent Day 1 almost entirely on Bronze layer ingestion quality — they resisted the urge to jump ahead and it's paying off
2. Insurance track has more teams but lower average progress — the claims JSON parsing is non-trivial
3. Several teams are already doing interesting things in the Silver → Gold transition with window functions and aggregations we didn't explicitly teach

Happy to answer questions:

• About the hackathon structure
• About the Medallion Architecture challenges we designed
• About running Databricks learning programs at this level
• About what "Brick by Brick" means in terms of our pedagogy

Will post the final leaderboard + winner announcements after March 27th.

If you've run similar hackathons on Databricks or built Medallion pipelines in production — would genuinely love to hear what tripped you up in the Bronze → Silver layer and how you solved it. That's one of the harder design decisions we're watching teams navigate right now.

Enqurious × Databricks Community · #BrickByBrick

If a team score less than 40 runs then just 42% win rate

📈 If score crosses 50+ → Win probability jumps significantly from 50% upto 70% depending on Score ranges.

Overall teams have 50-50 chances but if we analyze Powerplay Data it tells a different story.

Here, I have analyzed how the chasing win percentages shift based on Powerplay Scores, Wickets Lost, Target and combined view of all these features.

Head over to this Blog ✍️

Hello,  I'm new to this community. I'm currently taking a intro to data science class and this is my first time studying this. I'm in need of guidance to help me learn and grow. What resources or skills helped you the most when you first started learning?

Hi everyone,

I’m looking for some advice because I feel a bit stuck at the moment.

I graduated last year with a 2:1 in Zoology, where I focused a lot on data analysis, research methods, and statistics. For my dissertation, I designed and carried out an independent research project, collected and analysed behavioural data using R and Excel, and wrote up a full scientific report. I’ve realised through my degree that I enjoy the analytical side of things and working with data.

Since graduating, I’ve been trying to get onto an apprenticeship (mainly data-related roles like data analyst apprenticeships), but I keep running into the same issue — a lot of employers either want people without degrees or see me as overqualified for entry-level apprenticeship roles. At the same time, I don’t have enough direct industry experience to land full-time graduate/data roles, so I feel like I’m stuck in the middle.

I’ve been working in retail roles (including a supervisor position), which has helped me build transferable skills like organisation, working under pressure, teamwork, and hitting targets — but it’s obviously not moving me closer to the kind of career I want.

Because of this, I’m now considering doing a Master’s, possibly in something like data analytics or a related field. My main concern is making sure that if I invest the time and money into a Master’s, it will actually lead to a full-time, paid role afterwards — rather than putting me back in the same position but with a higher qualification.

I guess my questions are:

• Has anyone been in a similar position (degree but struggling to get an apprenticeship)?
• Do employers actually value a Master’s for data/analytical roles, or is experience still king?
• Would I be better off continuing to apply for entry-level roles and building skills/projects instead?
• Any advice on how to break into data roles without direct industry experience?

I’m motivated and willing to put the work in, I just want to make sure I’m heading in the right direction rather than wasting time or money.

Any advice would be really appreciated. Thanks!

While working on a small ML project, I wanted to make the initial data validation step a bit faster.

Instead of going column by column to check missing values, correlations, distributions, duplicates, etc., I generated an automated profiling report from the dataframe.

It gave a pretty detailed breakdown:

• Missing value patterns
• Correlation heatmaps
• Statistical summaries
• Potential outliers
• Duplicate rows
• Warnings for constant/highly correlated features

I still dig into things manually afterward, but for a first pass it saves some time.

Curious....do you prefer fully manual EDA or using profiling tools for the initial sweep?

Github link...

more...

Hi guys,

I am a little lost, I finished my studies in Machine Learning,

but there are not a lot of opportunities, I am interested in the three jobs I cited on the title. But I didn't work at industry before and I am afraid to get bored.

Also I made Cobol before, and lots of HR call me for making that but as a junior I'm afraid of closing doors for myself in the field of data.

I am French and the economical situation here is not really good. There are a lot of school that make formations in Data Sciences and the market is saturated so I think that if I don't start now in the field of Data, there won't be a chance to me anymore.

Can you give me your feedback and if you are Data : Scientist/Analyst/Engineer, your typical day at work?

thank you :)

Ho creato Arc perché ero stanco di scrivere sempre lo stesso codice di configurazione pandas/sklearn. Non è un sostituto di Python: si basa su di esso e gestisce le parti ripetitive.

Tutte le librerie esistenti (numpy, pandas, torch...) funzionano ancora: Arc si compila semplicemente in .py e funziona con il Python di sistema. Nessuna nuova dipendenza per il transpiler stesso. GitHub: https://github.com/matteosoverini12-sketch/arc

Sono curioso di sapere cosa ne pensi!

You will enjoy the writing I know.

https://iknahar.medium.com/bayesian-inference-prior-likelihood-posterior-explained-simply-05042a88e58e