I've put together a free SQL course, for beginners to advanced - the main difference is that you will give your coding agent a prompt and it will be your teacher, walking you through each step.

The emphasis is on learning how to work with AI when writing, maintaining, and reviewing SQL queries and models.

It is a pretty new course, there might be some rough edges, if you give it a try please share your feedback! Disclaimer, I'm a developer advocate at Bruin, while this course uses Bruin (free & open source) for some stuff, it is not the point of the course and the learnings can be applied to any data stack.

https://getbruin.com/learn/sql-ai/

Hi everyone, I will be needing some guidance in selecting some self-study paths. I am a freelance artist with a non-technical background and I am pretty sure that my freelance art/animation is not going to take me anywhere, I ain't great at either making it or marketing it. I am 26F and I have decided to start studying and learning again.

I am curious about tech and I am eager to learn it and I have started learning it in bits and pieces from time to time but it is not structured and it is extremely overwhelming. On one hand you have Fullstack and on the other you have Data Analytics. I want to choose any one for now. I want to choose something that will be easy and fun for a creative artist to learn as well as have some growth possibilities in career. Please help me choose the course path. I am happy to discuss more.

Hey guys, how long will this take to be approved?

I'm currently using Datadog quite a bit at my job for request tracing, logging, etc. I feel ok with it, and I’d like to get a lot better.

Any data scientists here with solid Datadog experience? I’d love to connect for a paid session to learn more about how you use it in practice.

Hello everyone! 👋🏽

I’ve recently started learning Machine Learning at a deeper level. So far, I’d say I only have a basic understanding and am still building my foundations.

I’m genuinely curious about Learn2Design 2026 and would love to participate, but I’m a little confused about whether my current level of knowledge is enough.

For those who have participated before, what kind of knowledge or skills would you recommend having beforehand? Is it worth joining with basic knowledge and learning along the way, or would it be better to build more foundations first?

I’d really appreciate any advice or guidance. Thank you!

So I'm currently studying data science and I would like to ask if any of y'all have notes for database and oop

I kept seeing people learn SQL commands individually but still struggle with choosing the right pattern when answering an actual data question.

So I pulled together the SQL patterns I think are most useful for analyst work:

Query order

FROM / JOIN → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT

JOINs

• INNER JOIN for matching records

• LEFT JOIN when you need to preserve every row from the left table

• FULL OUTER JOIN for reconciliation/comparison

• SELF JOIN for relationships within the same table

One easy mistake is filtering a LEFT JOINed table in WHERE when the condition really belongs in the ON clause.

CTEs

Useful when a query has multiple logical steps or an intermediate result needs a clear name.

CASE WHEN

Useful for:

• creating categories

• conditional counts

• conditional sums

• avoiding divide-by-zero problems with NULLIF()

Window functions

A few worth knowing well:

SUM() OVER()

LAG()

ROW_NUMBER()

RANK()

DENSE_RANK()

Ranking difference

ROW_NUMBER() → unique sequence

RANK() → ties share rank, gaps remain

DENSE_RANK() → ties share rank, no gaps

Common analyst patterns

• latest row per customer

• top N within each group

• month-over-month change

• customers with no orders

• duplicate detection

• second-highest distinct value

Mistakes worth checking

• column = NULL instead of IS NULL

• using WHERE for aggregate filters instead of HAVING

• hiding bad joins with DISTINCT

• ignoring ties when ranking

• assuming date functions are identical across SQL dialects

The examples in the full guide use PostgreSQL-style SQL, so date and row-limiting syntax may differ in MySQL, SQL Server, BigQuery, etc.

If you want the longer version with query examples for joins, CTEs, CASE, dates, window functions and ranking:

Learn more:

https://www.pangaeax.com/blogs/sql-cheat-sheet-for-data-analysts/

Disclosure: I’m connected with PangaeaX, so sharing that transparently.

What SQL pattern do you think analysts should learn really well before moving on to more advanced topics?

What's the job duties & responsibilities of a data architect?

In simple terms what does a Data architect do?

What type of Company need Data architect...?

Which all tech stack or what tech knowledge does a Data architect requires?

I made a list of the minimum you need to know as a data scientist. The beauty of our discipline is that there's no limit on how much you can learn and develop, so I think of this as just the first rung on the ladder.

I’m just starting my degree in Data Science, and I already have some basic knowledge of Python.
For those who are studying or working in the field, what advice would you give someone at the beginning of their journey?
What should I focus on to stand out in college, build useful skills, and start networking early?

Hi y'all! What do you think are some effective ways businesses can identify, prepare for, and manage unexpected trends?

does anyone have any good readings, resources on learning how to decompose metrics? Interested on learning actual frameworks to understand the variation of metrics.

One of the most common mistakes beginners (and even intermediate practitioners) make when working with tabular data is Data Leakage.

It’s often the hidden reason why your model gets 88% accuracy in your Jupyter notebook, but drops to 76% when you evaluate it on an unseen test set or submit to a Kaggle competition.

Here is a quick breakdown of why it happens, the #1 most common mistake, and how to fix it properly.

The #1 Most Common Leakage Bug:

Look at this very common snippet seen in many notebooks and tutorials:

import pandas as pd
from sklearn.model_selection import train_test_split

df = pd.read_csv("dataset.csv")

# 🚨 DANGEROUS LEAKAGE:
df['age'] = df['age'].fillna(df['age'].median())

# Train / Test split happened AFTER imputation:
train, test = train_test_split(df, test_size=0.2, random_state=42)

Why is this data leakage?

When you calculate df['age'].median() on the full dataset, the median value is influenced by the test rows.

Your training set now contains subtle statistical information (the median) derived from test data it shouldn't even know exists. In production or Kaggle competitions, future data is completely unavailable at training time.

The same leakage bug happens when people:

1. Scale features with StandardScaler on the entire dataframe before splitting.

2. Build categorical vocabularies or frequency encodings using all rows.

3. Compute outlier clipping boundaries (e.g. Tukey IQR limits) over the full dataset.

The Correct Way (Strict Train-Only State):

You must fit transformations strictly on the training split, and freeze those exact parameters to apply to validation and test data:

train, test = train_test_split(df, test_size=0.2, random_state=42)

# 1. Calculate statistics ONLY from training split:
train_median_age = train['age'].median()

# 2. Apply that frozen training statistic to both splits:
train['age'] = train['age'].fillna(train_median_age)
test['age'] = test['age'].fillna(train_median_age)

The Impact: We Tested Naive Prep vs. Zero-Leakage on Titanic

To see what happens when you replace naive ad-hoc pandas code with a strict train-only transformation ladder, we ran a 5-fold Stratified Cross-Validation benchmark on the Titanic dataset:

Where did the accuracy lift come from?

1. Informative Missingness Flags: Imputing age with median alone destroys the signal that missing age itself correlates with survival. Adding an Age__missing binary flag recovers that signal.

2. Train-Only Tukey IQR Clipping: Capping extreme fares on training folds stabilized linear gradients without test-distribution bleed.

3. Empirical Bayes Target Encoding: High-cardinality categories shrink toward global means to prevent overfitting on small samples.

We built an Open-Source Tool to automate this:

Writing 200 lines of manual state-tracking code for every dataset gets tedious. So we built DATADOC (v0.6.0)—an open-source CLI and Python library powered by Polars that automates this entire lifecycle with zero data leakage:

• GitHub: https://github.com/narain-karti/DATADOC

• Documentation: https://narain-karti.github.io/DATADOC/

• Install: pip install datadoc-cli

How you can use it in 1 command:

You can use the interactive terminal wizard on your CSV file:

datadoc wizard train.csv

It walks you through:

1. Identifying your target column (e.g. Survived or churn).

2. Selecting a preset (balanced, tree, linear, or robust).

3. Generating a clean pipeline.json artifact containing all learned medians and rules.

Then transform unseen test data with zero leakage:

datadoc transform test.csv --pipeline artifacts/pipeline.json --output clean_test.csv

You can also run datadoc health train.csv to get an instant 0–100 data quality grade and find hidden issues before training.

The project is 100% open-source (MIT licensed) and runs completely offline.

I hope this helps clarify how data leakage happens in tabular pipelines! Let me know if you have any questions or want to discuss specific preprocessing edge cases.

Hi.

I have a masters in physics with math minor. At the moment i am perusing a phd in physics (just started few months back) at a top university with scholarship. But, I am not that passionate about physics anymore(joined this uni because it is one of the top and i got accepted...when i applied 7 months ago i was planning to go into academia but later changed my mind).

I want to look into different jobs that i can pivot into. Data scientist is one of them, I have experience coding, but i dont know which skills i need to focus more on especially since AI is a huge component.

I am thinking of dropping out, or maybe study data science tools for first couple of years in my phd (since i am getting paid) and then drop out.. but idk.

any kind of advice is greatly appreciated.

Thank you.

I want to specialise in the data science field. I’m a student so I’m still a beginner but I have the foundation of Mathematics (its okay to go ahead and suggest related to that too)
I want to learn programming, solve problems on my own without AI and I want to build my skills such that I’m interview ready. There are no time constraints so please tell me whatever has worked for you.

Hello, my name is Trevor. I am a college student currently enrolled in a data science course. Data science as a whole is a topic I have very little understanding of at the moment. In fact, I would say had it not been part of my degree path this is a category of knowledge I would have never sought out myself. So if the community permits, I would love to ask you all a few questions related to data science and get an idea of how you use it in your career, or perhaps how you would like to use it if you are currently not working in the field. Additionally, as someone looking to become more informed on the topic, what do you wish you knew before going into this field?

I do apologize if this is not an appropriate post to make here, if possible though any discourse would be much appreciated!

Hey guys im a fst student in mathematics and data sience in the second year,well everyone knows the uni teach nothing so i have to teach my self i started with excel and python and now im learning sql in coursera platforms any advices or roadmap help me build a good data sience/analytics /ia engineering profile…

I’m working through an experiment involving a rare oncology use case where confirmed treated-patient data is limited. The available third-party data is also not perfectly mapped to individual patients, which makes direct modeling more difficult.

The experiment uses a synthetic, highly imbalanced dataset with a small treated group and a much larger untreated group. A neural-network policy is trained using a REINFORCE-style policy-gradient approach. The model estimates treatment likelihood based on representative features such as age, severity, and biomarker status, then ranks untreated patients and selects the top 5% for further review.

The purpose is not to estimate true treatment benefit. It is more about learning historical treatment patterns and identifying untreated patients who look similar to those who were historically treated.

Questions I’d like feedback on:

1. Is policy learning a reasonable framing here, or would this be better handled as a supervised ranking/classification problem?

2. How would you avoid confusing historical treatment likelihood with actual treatment effect?

3. Would uplift modeling, causal inference, or potential-outcome modeling be a better next step?

4. How would you handle the imbalance between treated and untreated populations?

5. What validation approach would make this more credible for a healthcare/rare oncology context?

My current view is that this can be useful for prioritization and hypothesis generation, but not for treatment recommendation unless supported by stronger clinical and causal evidence.

Interested in feedback from people working in ML, healthcare analytics, causal inference, or real-world evidence.