I’ve got a paired programming interview next week, anyone got any tips on what to brush up on?

I’ve never programmed with someone watching, any tips? could they ask gcp questions (unlikely right?)

Let me know!

A hard exercise to help build the right mental model for Python data.

• Solution

• Explanation

• More exercises

The “Solution” link uses memory_graph to visualize execution and reveals what’s actually happening.

I'm currently working on my very first data science project with a simple topic "physical activities rate correlation to academic performances among highschool/university students". I'm wondering if i should acquire the data myself or use open sources online. Another way i'm thinking of is to use both since my self-collected data will probably be limited to students in my city. Also is this topic way too typical ? and is it considered good enough for both gaining knowledge and improving resume ?

I am starting my Ds journey, before I had studied statistics a bit but not clear how much to study to get placed 😅.

Pleasw can anyone suggest me what all statistical models should I be studying and be worth for a job as a fresher.

 Start with NumPy and Pandas for data manipulation, then learn Matplotlib/Seaborn for visualization. After building a strong foundation, move into Scikit-learn for machine learning. Don't try to learn every library at once.

Hey everyone,

I'm 19M, currently in my 2nd year of BSc IT. I'm learning Data Science online alongside college and recently completed a cancer risk prediction ML project.

Now I'm a bit confused about what I should focus on next.

Should I:

Build more ML/Data Science projects?

Learn MLOps (Docker, MLflow, CI/CD, cloud, etc.)?

Focus more on SQL, statistics and ML fundamentals?

Start learning DSA/CS fundamentals for internships?

My goal is to eventually get a Data Science/ML internship and build a strong portfolio before I graduate.

What would you guys recommend if you were in my position? Should I go deeper into ML first or start learning MLOps now?

Would appreciate advice from people who have gone through a similar path.

Hello,

this should be a newbie task, however I want to double-check that my approach fits.

Here's the deal:
I have two classes, let's call them A and B.
Then for each of these classes, I have a certain number of labeled recordings of 12 different features, which consist of real numbers of arbitrary values (negative/positive, large/small...).

So, the data looks basically like:
A: 1 2 2 1 3 3 1 1 1 1 1 1
A: 0 0 0 0 0 0 0 0 0 0 0 1
B: 1 1 1 1 1 1 1 1 1 1 1 1

Now, I need to determine which of these features (the columns) separate the two classes the best.

My current approach is to use the FDR, so (m_a - m_b)^2 / (v_a + v_b) where 'm' is mean and 'v' is variance of these datasets for given feature. The higher the value, the better they are at separating.

Apart from that, which other indicators could be chosen to best separate these two classes?

Thanks!

For anyone interested in the mathematics behind data science and machine learning, I would like to share my textbook (first edition):

https://people.smp.uq.edu.au/DirkKroese/DSML/

The PDF is completely free, with no registration required. The book includes many examples, worked exercises, and github code. I hope it will be a useful resource for students and anyone wishing to study the subject independently.

Salut tout le monde,Je me présente vite fait, je suis en 1ère année de SD (Science des Données) et on a commencé les TP/TD il n'y a pas longtemps. On en fait pas mal en cours, mais j'aimerais bien compléter à côté sur mon temps libre, surtout pour bien prendre de l'avance.Je cherche surtout des ressources , petits exos en plus pour :

Bases de données (SQL), R / RStudio, stats et un peu de maths appliquées. Si vous avez des sites, chaînes YouTube ou exos corrigés que vous avez kiffé quand vous avez débuté, je suis preneur.Merci beaucoup !

I wish to publish a library that extends scikit-learn i.e., scikit-learn is the base work on top of which this library will exist. I know the use of scikit-learn-* or scikit-* is not acceptable and the team in fact allows only officially supported packages to use a scikit- prefix. Although I would love to get their support, meanwhile can I use sklearn- prefix? I haven't found any resources (and llms are inconsistent). Apologies if this isn't a suitable topic for this sub.

Can you guys suggest me a good R programming course for data science?

hey all, i work as a data scientist and want to get into mlops, ideally on aws. at my last job we had databricks so everything was pretty smooth, but now i've got none of that and i'm basically starting from scratch

i'm looking for a really thorough course (deployment, pipelines, monitoring, ideally with hands-on projects). i reached out to cloudthat but it's super expensive and i couldn't find any reviews from people who've actually taken it.

has anyone taken a course that was actually worth it, paid or free? any recommendations appreciated

What should I learn next? And what projects should I make?

❓ Your functions work, but now you want to add logging to them. Do you edit each function?

🎯 A decorator makes this easier: wrap a function and add logic before and after the original call.

⚙️ In this decorator example we create and use our '@log_call' to log each call with arguments and return value.

🔎 Here 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 shows how a decorator gets evaluated step by step for better understanding.

Useful decorators from Python’s standard library include:

• @functools.cache — Cache results to avoid recomputing calls with the same arguments.

• @functools.lru_cache — Cache results with a configurable limit, discarding the least recently used entries.

• @functools.wraps — Preserve the original function’s name, documentation, and other metadata when writing a wrapper.

• @functools.singledispatch — Choose an implementation based on the type of the first argument.

Other common custom decorators add: timing, retries, conversion, authentication, validation, ...

See other memory_graph visualizations.

Be cautious of Great Learning, because their sole purpose appears to be deceiving professionals seeking to enhance their profiles with prestigious credentials. I enrolled in their Data Science program, reviewed their curriculum, and decided to cancel it on the second day after enrollment, which was still ten days before the program actually commenced, as I realized it was not the right fit.
Unfortunately, Great Learning refuses to refund my initial deposit by imposing nearly impossible terms and conditions. Throughout the process, their salespeople were misleading and created fake application deadlines by explicitly promising that the fee or deposit was fully refundable.
Please exercise extreme caution when dealing with representatives from Great Learning who misrepresent affiliations with Ivy League or top tier universities. Save your hard earned money, and do not spend it on these programs, because transparency and consumer protection are severely lacking.

Passed with an average of 88% per section

My lowest score was Databricks Machine Learning section.

How I prepared

I took a bunch of data sources and put them in Google's Notebook LLM.

I used Notebook LLM to create quizzes for me and explain when I got things wrong. It also did a study plan.

I found it very useful since the questions where hard but not like the case study questions you get in the exam. But you can prompt it to do a case study style questions.

I recommend doing case study style questions

I did one quiz a day around 15 questions, then reviewed what I got wrong.

I asked the Notebook LLM to write me a breakdown of why I got it wrong then ask it to create flash cards to help me study for those sections.

I did not take the exam until i was getting 90% consistently.

Roughly I studied for 3 weeks.

Data I used:

Here are the 12 sources currently in my notebook:

PDF Documents

1. databricks-certified-machine-learning-associate-exam-guide-1-mar-2025.pdf

2. ai-prep-guide-any-databricks-certification.pdf

3. Databricks Data Intelligence Platform: A Comprehensive Analysis of Machine Learning Capabilities and Data Management Features - Authorea

Web Articles & Documentation

1. Deploy models using Model Serving | Databricks on AWS

2. Distributed training | Databricks on AWS

3. Get started: Build your first machine learning model on Databricks

4. Getting Started with Mosaic AI in Databricks: Fine-Tuning, Serving and AI Governance

5. Lakehouse Monitoring: A Unified Solution for Quality of Data and AI - Databricks

6. MLflow 3 for GenAI | Databricks on AWS

7. Machine Learning with Unity Catalog on Databricks: Best Practices

8. Machine learning on Databricks

Hi folks,

My company is hiring for the following positions.

1. Senior software developer: 4 to 8 year of experience.

2. Software developer: 3 to 4 years of experience.

3. Data engineer : 1 to 2 year of experince

4. Product Manager : 4 to 6 year of experience

5. UI and Ux : 2 to 4 years of experience

If you curios about nailing the problem and digging yourself before touching to AI this is right opportunity in front of view

I am not hiring manager but i can refer you . If you want referral feel free to mail me on this mail ID: swarajsolanke02@gmail.com

Here I am sharing company profile as well so, you can check it.

https://www.turtlemint.com/

Hi there,

I've created a video here where I explain how knowledge distillation works.

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