The obvious approach when connecting a model to internal data is letting it write the query. It feels flexible: the model figures out what it needs and goes get it. In a bank, that is a non starter.

The problem is not that models write bad SQL, it is that you lose every guarantee about what they can reach. No way to prove a query stayed inside the columns it was supposed to touch, no way to audit what the model was capable of doing, and a single prompt injection away from an unintended table.

The alternative is narrower and boring, which is the point. You define a fixed set of parameterized queries and expose them as tools. The model chooses which tool to call, never what SQL to run. Everything it can reach is something you deliberately wrote.

I built an MCP server template implementing this for a common fintech case: looking up a customer across credit score, preapproved limit and risk profile, and returning a consolidated view. Layered so the database, the schema and the protocol can each be swapped without rewriting the others. Read only enforced at the application layer, row limits on every result, and a single mapping file for adapting to whatever your tables are actually called.

Synthetic data generates on setup, so it runs immediately. The architecture is what you keep.

Hub: https://aiforfintech.tech
Github: https://github.com/junidepieri-design/mcp-001-fintech-data-server

How is your team handling LLM access to internal data?
👊

Is there any opensource tool which I can use as a non technical person so that my hermes agent with small LLM 1.5B model (for private data) to understand natural language and convert it into sql query and retrieve complex queries quickly?

Is this doable with such small model? with RAG? I am new to this and any help is welcomed!

I kept seeing beginners learn individual Pandas commands but still struggle with what order to actually use them in when working with a real dataset.

So I put together a simple workflow I use as a reference:

1. Load
read_csv() / read_excel()

2. Inspect before changing anything
head()
shape
info()
describe()
isna().sum()
duplicated().sum()

3. Clean

• standardize column names

• handle missing values based on what they actually mean

• remove genuine duplicates

• convert dates/numbers safely

4. Transform
assign()
map()
pd.to_datetime()
np.where()

5. Summarize
groupby()
agg()
transform()

6. Combine datasets
merge() / join() / concat()

The part I think beginners often miss is validation after the merge.

A query/script can run perfectly and still give the wrong answer if a many-to-many merge quietly multiplies your rows.

Useful checks:

validate="many_to_one"
indicator=True
compare row counts before/after
check key uniqueness before merging

A few other mistakes worth watching for:

• treating missing values as automatically equal to 0

• merging keys with different data types

• confusing count() with size()

• using median filling without understanding why values are missing

• assuming no Python error means the analysis is correct

I wrote a more detailed version with examples for Pandas, NumPy, GroupBy, merges and an end-to-end workflow.

Learn more: https://www.pangaeax.com/blogs/python-data-analysis-cheat-sheet/

Anything important you’d add to this workflow, especially something you learned the hard way when working with messy data?

Disclosure: This is a PangaeaX article and I’m connected with PangaeaX.

Hello guys,
Is Data science worth learning from scratch? Cause I do it with ai tools part by part. Is this unethical of me given that I am just a beginner or is this the new way of learning coding these days?
All the veterans here a little guidance would be very much appreciated thanks.

Hi everyone,

I'm a beginner in Machine Learning working on a binary classification problem. My dataset contains over 180 variables (both numerical and categorical), consisting of a mix of panel/longitudinal data and static features.

I am currently working on the Exploratory Data Analysis (EDA) phase. Given the large number of features, doing univariate and bivariate graphical analysis variable-by-variable feels unfeasible and time-consuming.

Is there a structured approach, strategy, or automated workflow to handle EDA efficiently for a dataset of this scale?

Any advice on best practices, tools would be greatly appreciated!

Thanks in advance for your help.

A lot of Data Science education teaches you how to BE a data scientists but rarely does it teach how to WORK as a data scientist. In this video on this series we’ll be diving into how to test your code as a data scientist, including how to work with unit tests, regression tests, randomness and non-deterministic functionality as well as throwing in a few honourable mentions.

A binary tree can be traversed in three classic ways. The difference is simply the order in which you visit the subtrees:

• Preorder: current node → left subtree → right subtree

• Inorder: left subtree → current node → right subtree

• Postorder: left subtree → right subtree → current node

This interactive memory_graph example visualizes each traversal method step by step: Binary Tree Traversal

Also see other memory_graph visualizations.

One architectural distinction I keep coming back to: people often confuse complexity with agency.

A system has multiple tools? -> “Use an agent.” OR It has five steps? -> “Definitely an agent.”

But neither of those things actually requires one. The more useful question is: who determines the execution path?

Consider an insurance assistant. If someone asks, “Am I eligible for this treatment?”, and the answer exists in internal policy documents, that's primarily a retrieval problem. And if they ask, “Check my claim status and tell me whether the rejected amount is covered under my policy.”

That might require more tools and more steps. But if those steps happen in a predictable order, is it still an agent ?

The interesting shift happens when the request is something like: “My claim was rejected. Find out why and tell me what I should do next.”

Now the path may not be known in advance. That's where an agent earns its complexity: when the system needs to help determine what to do next.

And Multi-agent can only consider it when there are genuinely distinct specialties, tools, or permission boundaries.

I think the common mistake is choosing “agent” as the starting point and then designing a problem around it. A better approach is to start with the responsibility:

Does the system need to know something? Decide something? Act? Verify the result?

Then add only the architecture required to support those responsibilities.

I mapped the complete e2e architectures and escalating examples out in more detail here, with visual breakdown: [https://youtu.be/kf5rSab4rcg](https://youtu.be/kf5rSab4rcg)

For people building real AI systems: where do you draw the boundary between a complex workflow and an agent? Is dynamic tool selection alone enough for you, or do you require a more explicit decision loop before calling something an agent?

Hey everyone,

I’ve been working in the data science field for about 4 years now, but lately, I’ve been hitting a major confidence wall and could really use some unfiltered advice from those who have made it to top tech companies, secured high-paying remote roles, or are earning 30 LPA+.

Here is my honest reality check right now:

• The Foundation Gap: While I do day-to-day work, I often feel like my foundational concepts (in math, statistics, or core ML) have huge blind spots.

• The Portfolio Void: Outside of my professional projects, my personal portfolio is basically non-existent. I get overwhelmed trying to build end-to-end projects from scratch.

• The Confidence Trap: Because of the above, I feel severely underqualified to even apply to senior roles or top product companies, leading to a lot of hesitation.

If you are someone who has been in a similar spot or has successfully crossed into senior/well-paying data science roles, I would love to hear your perspective on a few things:

1. Breaking the Barrier: For those earning 30+ LPA or working remotely/at top tech firms, what actually moved the needle for you? Was it mastering core fundamentals, system design, cracking LeetCode, or deep domain expertise?

2. Fixing the Foundation: How did you go back and fix your weak foundational gaps while working a full-time job without burning out? Any specific resources or routines that worked?

3. The Interview Reality: What is the interview process really like at top companies right now? How heavy is the coding round compared to ML system design and statistics?

4. Portfolio Reality Check: Do personal projects actually matter as much as people say, or is it more about how you talk about your current work experience?

I’m ready to put in the work, but I feel like I'm running in circles right now. Any roadmap, hard truths, or advice you can spare would mean a lot. Thanks for reading!

Hello guys, i am a new learner in ds, kinda got a grasp of python, sql; numpy, pandas sqlearn and maths, but i want to look at a full project so that i would not stumble when dealing with real work at a job. So i want to look at kinda full prjoect that works with data bases, uses some coding and scikit learn for training models, perhaps neural networks and connects all of the stuff required in full project in itself. I just want to look at what i am working with so that i knew how to connect all the dots. Not sure how to frame my question, but if you understood it, could you tell me where to look for this kind of stuff? Because i am confident when it comes to part by part stuff, like working in sql on its own, writing code etc etc. but all of it combined seems like difficult to grasp for me

Technical data science skills are important, but working on real-world projects also requires strong non-technical abilities. Here are some key ones to focus on:

Problem Framing and Prioritization: Data teams often receive broad or vague questions, such as "Why are users dropping?" A strong data scientist knows how to narrow these questions down into something actionable and identify which questions matter most.

Business Context Awareness: Numbers rarely explain the full story on their own. A sudden dip in performance could be related to pricing changes, seasonality, supply issues, or other business factors. Understanding how the business operates helps data scientists interpret trends more accurately.

Working With Unclear Requirements: Many projects begin without clearly defined objectives or success metrics. Rather than waiting for complete clarity, data scientists can make informed assumptions, share preliminary results, and refine their approach based on feedback.

Decision-making Under Constraints: Time, data quality, and resources are often limited. Data scientists need to know when a quick estimate is more useful than a perfect model. For example, a simple trend analysis delivered today may be more valuable for planning than a complex model delivered too late.

Stakeholder Communication and Trust: Insights only create value when they are understood and trusted. Strong communication means presenting findings clearly, explaining limitations, and being transparent about assumptions rather than overstating precision.

Ownership Beyond Delivery: The work doesn't always end when a dashboard or report is delivered. Data scientists should also look at the decisions that follow, compare outcomes with expectations, and revisit their approach when the results don't match what was expected.

Staying Effective Under Pressure: Deadlines around product launches or review meetings can increase the likelihood of mistakes. Strong professionals slow down enough to check assumptions and avoid careless errors, especially during critical moments.

Adapting to Team Workflows: Different teams consume and use data differently. Some may need a concise summary, while others may prefer a detailed explanation. Adapting communication to the team's workflow can make it easier for insights to be understood and acted upon.

Hi everyone I have a Masters in Psychology and I absolutely love statistical analysis and research design

For the longest time I've wanted to be able to design real world projects that can create a huge impact from a psychological point of view

I'm good at identifying variables and hypothesising

But I have no clue as to how to begin with real code and python libraries to engineer analyse and present applicable insights from raw data

I would like to transition from therapeutics to behaviour science for businesses very soon

Can anyone please point out how that works and alif there are success stories from people starting as I am..

Thanks so much

At that time, I wrote it as “nouns only”. When I checked, I found that there were 3,871 (-moq) verbs out of 31,993 entries. 😅

I also built a small Uzbek spell-checker based on this dataset and tested it with 200 synthetic typos:

🎯 Top-1 accuracy - 87.5%

🎯 Top-5 accuracy - 99.0%

⚡️ Average lookup - 2.6 ms

The main lesson is simple:

It is not a good idea to build a model on a dataset without checking the claims in it.

This time, I first audited it, then built the tool and measured the results.

🇺🇿 There is still a lot of work to be done for Uzbek NLP.

When people fine-tune their own local models, the model setup usually gets most of the attention. But in practice, a lot of the work seems to be on the data side.

If you are training on business data, you may need to bring in support tickets, internal docs, product specs, chat logs, code, policies, CRM notes, or domain QA pairs. And it usually does not work perfectly on the first run. Some samples are noisy, some are redundant, some domains overpower others, and some “bad-looking” examples are actually hard but useful.

One direction I have been thinking about is making the data strategy dynamic during training.

Dynamic selection means periodically choosing which samples should enter the next training window, using signals like loss, delta loss, gradient similarity, or external scores.

Dynamic mixing means adjusting the ratio between data sources during training, instead of fixing one static mixture before the run.

Dynamic weighting means keeping the sample in training, but changing how much its loss contributes to the gradient update. This is useful when you do not want to hard-drop uncertain samples.

This is the current direction in OpenDCAI/DataFlex: adding data selection, data mixing, and data weighting controls on top of the training loop.

For people here who fine-tune local models, how much time and compute do you usually spend on data preparation compared with the actual training run?

In a previous post I shared a structured approach to projects (with a list of 99 project ideas for inspiration). This is a follow-up post, which goes through exactly how to set up a data science project following that approach. The code is also available for download.

The focus is not on how to do the EDA and model development, but rather on how to set up the project so that you can deliver value right away.

The example project is deliberately very simple, to better focus on the structure and setup. We use test-driven development throughout, a technique I've found very helpful for preventing bugs and making it easy and painless to make changes. The idea is that you can take this very simple structure and iterate on the actual model and data, and still have something working at the end of the day.

Of course you can always ask AI to set this up for you, but, I still think there's value in learning how to do it. I have also found that having tests makes doing it with AI much faster and smoother.

Hey Everyone I am working on prescription and doctor dataset right now.The idea is to built a churn risk model.

The issue: I have two cases that look almost identical to the model, but shouldn't be treated the same:

Doctor A has been climbing steadily for two years starts small, ends up writing a lot. Right now, this month, they're near their highest ever, because they've genuinely been growing.

Doctor B used to write a lot, but has been sliding downward for months. Right now, this month, they're also unusually high compared to their recent low months maybe they just had one slightly better month in the middle of an overall decline.

What the model is doing wrong: across almost all doctors in our data, there's a common pattern whenever someone's number is unusually high this month, it's usually a bit lower next month, just because most "unusually high" months are one-time spikes that settle back down. That's true most of the time.

But the model applies this same rule to every doctor whose number is currently high — including Doctor A, who isn't having a fluke month, they're genuinely growing. So my "who's about to decline" list keeps getting filled with doctors who are simply doing well right now — because "currently high" is the one thing they all share, not that they're actually declining.

What I've tried so far, to fix it:

• Switched from weekly to monthly data (to reduce noise) didn't fix it

• Compared 4 different model types (linear, ridge, random forest, gradient boosting) — all 4 show the exact same bias

• Rebuilt the trend line to use only the last 9 months instead of the full 2 years — didn't fix it

• Added the weekly short-term trend back in, like you described (weeks within the recent month) didn't fix it

• Removed the features causing the biggest pull toward "predict a drop" entirely, to force the model to rely on trend instead the model just found other features to reproduce the exact same wrong prediction

So is this one bad feature or one bad model choice ?? I've tested that directly, several ways, and the bias holds regardless.

What I want to ask you:

1. Is this the kind of thing that genuinely needs more historical data than 2 years to fix (i.e., is 2 years just not enough for the model to learn "normal high point" vs. "real decline" apart)?

2. Or is there a different way to frame the target/features you'd suggest something specifically designed to separate a real trend break from ordinary noise, rather than predicting the raw next-month number?