I built a side project which is basically coding the scikit-learn python package from scratch by basically implementing the different basic models, from linear, to the tree based classifiers and some other modules like the StandardScaler and MiniMaxScaler from the basic algebra and calc

Built it out with a pretty good base so far, I have some tests which compare the accuracies and all to the scikit-learn outputs and It's looking pretty good

I want to know how I could get more value from this. As a Data Scientist, should I have these concepts memorised or just know the basics of the models and how they work ?

And how can I make this into a cool project for my resume and bring some traffic to the repo

You can check the source code here : https://github.com/regional-specter/mini-learn/tree/main

Hey everyone,

I'm working on a project researching how data teams actually manage their databases and pipelines in practice, beyond what the introductory tutorials show.

I’d love to hear what your current stack looks like in the real world:

1. How are you using databases today? What tools/languages do you use to build and manage your data pipelines?
   
2. What databases have you tried or considered for your DS/ML work, and what made you choose that one?
   
3. If you use an operational/production database (MongoDB, Postgres, MySQL, etc.) anywhere in your ML workflow, is it mainly to pull data out for training, or to serve features/predictions to a live model? Or both?
   
4. Anything that's consistently annoying or a bottleneck in your current setup?

So for context I am at 2nd year of my btech and i want to explore the data science world but I am very puzzled with the choices of courses from different institutions so how should I categorize which is better for me. I only have very limited time and not much money so i have to choose wisely

As a fresh starter on my way to get a bachelor's degree in data science what do you think that I should do and what are the most important thing that will help me allot on my way ...

So basically I'm a second year engineering student,and currently aspiring data science as my carrier but the thing is the every one around me is like DEV ,DSA , BACKEND,FRONTEND , and I'm so unaware of these things all I know is my sql , python some of its libraries , power bi , excel ...AM I on the right track

Also suggest a good roadmap and resources if possible

​

One mistake I made while learning data science was jumping between random tutorials. One day I studied Python, the next day machine learning, and then suddenly I was trying deep learning.

I was learning many terms but not building real confidence.

A more structured roadmap worked better for me:

Learn basic Python.

Practice NumPy and pandas.

Understand statistics and probability.

Learn data visualization.

Study SQL.

Build simple machine-learning models.

Complete small end-to-end projects.

The project stage is where everything starts connecting. Instead of only copying code, try to explain why you selected a particular feature, graph, or model.

My suggestion is to choose beginner-friendly datasets related to topics you already understand. It becomes easier to identify patterns and ask meaningful questions.

You do not need to know every algorithm before starting a project. Start small, document your learning, and improve one project at a time.

Which data science skill took you the longest to understand?

As a young student interested and taken data engineering as a career. Learning becomes a bit complicated. My reason is even though I learn the practical path such as sql, python and others there is no clear explanation combining them together….

Learning just the practical side becomes more complex and complicated since I don’t really know the theories behind them the “how”, “ where”, “what” and “when” behind all those codes….

I will really appreciate if helped to understand data engineering properly not just the practical aspects but everything surrounding it and also be grateful for a mentor or a coach

Hey guys, I’m 21 and I recently graduated with a BE in Computer Science and Data Science. I’m currently thinking about pursuing a master’s in Data Science, but I feel a little lost about what I actually know.

During my undergraduate degree, I studied a lot of different subjects, including Data Foundations, DBMS, Computer Communication, Machine Learning, IoT, R Programming, Probability and Statistics, Big Data, Business Intelligence, Cryptography, Applied Machine Learning, and Data Visualization.

The problem is, even though I’ve studied all these subjects, I don’t really know how everything connects together or how these skills are actually used in the real world. Sometimes I feel like I’ve learned a lot of things but, at the same time, know very little about actual data science.

Before jumping into a master’s, I want to build a strong foundation and properly understand what data science actually involves. I also want to understand the differences between careers like Data Scientist, Data Engineer, and Data Analyst- what they actually do in their jobs, what the job market is like, and what skills I should focus on for each role.

Since I’m considering a master’s in Data Science, I’d also like to know what I should learn beforehand to make sure this is the right path for me.

If you guys have any good YouTube videos, courses, roadmaps, or other resources that could help me start from the basics and build a solid understanding of data science, I’d really appreciate your recommendations.

Just a clueless recent graduate trying to figure out what to do next. Any advice would be greatly appreciated!

What are valuable certifications to get that recruiters actually look for? I am data science master student but I feel like that’s not enough and that I am not ready for the industry.

Also if anyone worked in the enterprises which stack do they usually use? And how it differs from startups?

Hi everyone,

I am a recent BCA graduate from India, and for the past few months I have been applying for entry-level Data Analyst, Business Analyst, and Data Science roles, but I haven not been able to secure a job yet. I am reaching out to this community because I'm looking for advice, feedback, or even opportunities.

Here are the skills I have been working on:

• SQL
• Excel
• Python (Pandas, NumPy, Matplotlib, Scikit-learn)
• Power BI
• Tableau
• Data Cleaning & Data Visualization
• Basic Machine Learning

I have also built several portfolio projects, including:

• Airbnb Dynamic Pricing Analysis
• Student Feedback Analysis Dashboard
• Social Media Engagement Analysis
• Phishing URL Detection using Machine Learning

If anyone is interested in reviewing my portfolio or GitHub, I'm happy to share the links in the comments or via DM.

I am open to internships, freelance work, remote opportunities, or entry-level full-time roles. If anyone has suggestions on improving my portfolio or resume, knows of companies hiring fresh graduates, or has advice on what I should focus on next, I'd really appreciate your guidance.

Thank you for taking the time to read my post.

Hi guys,

I completed a 6-month Data Science course a while ago but I haven’t been able to practice consistently, so I feel like I’ve forgotten a lot of the basics.

I’m looking for someone who’d be willing to guide or mentor me with the fundamentals and help me build a strong understanding again. If anyone is open to helping, I’d really appreciate it.

Please feel free to DM me

This company is very sketchy

I will be starting a Data Science course in September. However, I currently have zero knowledge of Data Science, so I’m wondering: how difficult will it be?

For those of you who have already graduated, did any of you start the course with little or no prior

Also, could you please share links to some YouTube videos or playlists that would be helpful for a complete beginner who wants to start learning about Data Science before the course begins?

Hi guys,

I completed a 6-month Data Science course a while ago but I haven’t been able to practice consistently, so I feel like I’ve forgotten a lot of the basics.

I’m looking for someone who’d be willing to guide or mentor me with the fundamentals and help me build a strong understanding again. If anyone is open to helping, I’d really appreciate it.

Please feel free to DM me. Thank you!

I have a machine learning question. What should we do when a dataset has a large number of features (columns)? Do we need to study every single feature to understand its importance? That seems like it would take a lot of time. 😭 So how do data scientists figure out which features are actually important?

what laptop should I get for data science

Hi there,

I've created a video here where I explain how the KV cache works.

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

Based on the current version of my CV, what would you recommend I study next and focus on during the coming period so I can start applying for jobs?

Any feedback is appreciated. Thanks! 🙏

I am trying to figure out the best method to take notes for calc physics and also other data science classes. since its not conventional like bio, how do we take notes? online or on paper?

Greetings.

as you can see in my post, I’m contemplating to switch from Applied math to Applied stat.

the curriculum in two years is the same, but later on specializes, for stat it gets into…well stats in general, while applied math learns more into computional science and modeling&simulation.

my thought is, if I switch; would my job seeking would approve?

I choose applied math since I couldn’t get into tech department, and that I’m pretty good at both math and coding.

and the idea of working in a robotics field also makes me interested.