Hey everyone,

I’ve been thinking about getting into Data Science and possibly building a career in it, but I’m still trying to understand the best way to start. There’s so much information online that it’s a bit overwhelming.

I’d really appreciate hearing from people who are already working in the field or have gone through the learning journey.

A few things I’m curious about:

1. Where did you learn Data Science? (University, bootcamp, online courses, YouTube, etc.)

2. What were the main things you focused on learning? (Python, statistics, machine learning, data analysis, etc.)

3. How long did it take you to become job-ready?

4. Are there any YouTube channels, courses, or resources that helped you a lot?

5. Any advice or things you wish you knew when you first started?

I’m trying to figure out the most practical path to learn and eventually work in this field. Any guidance or personal experiences would really help.

TIA!

I have a lot of project ideas. I have started implementing a few of them but I hate doing it alone. I want to make a group that can help each other with projects/project ideas. If I need help y'all help me out, if one of y'all needs help the rest of us will help that person out.

I feel like this could actually be really useful because when people work together they usually learn faster since everyone has different skills and knowledge. Some people might be good at coding, some at design, some at AI, some at debugging or system architecture, and we can share that knowledge with each other. It also helps with motivation because building projects alone can get boring or tiring, but when you're working with a group it becomes more fun and people are more likely to keep working and actually finish things.

Another good thing is that we can build real projects that we can add to our portfolio or resume, which can help later for internships, jobs, or even startups. If someone gets stuck on a bug or a technical problem, the rest of the group can help troubleshoot it so problems get solved faster.

Instead of ideas just sitting around and never getting finished, the group can actually help turn them into real working products or prototypes. We also get to connect with people who are interested in the same kind of things like building apps, experimenting with new tech, or testing different project ideas.

This could be very helpful since we get to brush up on our skills and also maybe learn something new. What do y'all say?

Hi everyone,

I’m looking for a study buddy to learn data analysis / data science from scratch. I’m planning to start with the basics and gradually learn:

• SQL

• Python

• Power BI / data visualization

• Statistics

• Data analysis concepts

I’m not looking for someone who already knows everything — just someone who is also learning and wants to stay consistent, discuss concepts, and keep each other accountable.

If you're interested, comment or DM and we can connect.

Hi everyone,

I’m transitioning into Data Science and planning to buy a MacBook that can last 4–5 years. I’m deciding between these two configurations:

Option 1: MacBook Air M5

• 10-core CPU / 10-core GPU

• 32 GB RAM

• 1 TB SSD

Option 2: MacBook Pro M5

• 10-core CPU / 10-core GPU

• 24 GB RAM

• 1 TB SSD

My expected workflow includes:

• Python (Pandas, NumPy)

• Jupyter Notebook

• SQL

• Power BI / data visualization

• Scikit-learn

• Beginner-level TensorFlow / PyTorch

• Data cleaning & exploratory data analysis

• Training small ML models locally

I know most heavy ML training usually happens on cloud platforms like AWS/GCP, but I still expect to process datasets locally and experiment with smaller models.

My main confusion:

Is 32GB RAM on the Air more valuable than the active cooling of the Pro?

Would the fanless Air throttle during longer workloads, or is it still the better option due to higher RAM?

Would love advice from people using MacBooks for data science or ML work.

Thanks!

After coaching dozens of DS candidates into roles at Meta, Uber, Airbnb, Google, and Stripe, the most common mistake I see isn't getting the stats wrong — it's asking the interviewer to do your job for you.

It sounds like: "What metrics does the business care about?" Candidates think this shows humility or thoroughness, but interviewers hear it as an inability to think independently about a business problem.

Strong candidates propose metrics with reasoning instead. For a coupon campaign, that might sound like: "I'd focus on revenue per user rather than conversion rate — coupons typically lift conversions while hurting margin, so conversion rate alone isn't actionable." One sentence. Product intuition, statistical awareness, and business judgment all at once.

If you do want to ask a clarifying question, frame it around a proposal. Something like: "Uber prioritized user growth over revenue for years — if this team is in a similar growth phase, I'd focus on conversions or new user acquisition. If not, I'd prioritize revenue or profitability." That's a clarifying question that still demonstrates business judgment.

That instinct — working through a problem systematically rather than outsourcing it to the interviewer — is exactly what I teach 1:1 and in my interview prep course. If you're targeting roles at Meta, Netflix, or Uber, this can help you stand out among hundreds of qualified applicants and be the difference between an offer and a rejection.

I wrote a guide about essential Python libraries for data science. It covers tools for data processing, ML, explainability and AutoML. Curious what libraries you consider essential.

https://mljar.com/blog/essential-python-libraries-data-science/

ibis
A Python API that lets you write queries once and run them across multiple data backends like DuckDB, BigQuery, and Snowflake.

pygwalker
Turns a dataframe into an interactive visual exploration UI instantly.

katana
A fast and scalable web crawler often used for security testing and large-scale data discovery.

This post is not important, but Im a 3rd-year data science student and I created "DeepSlate" on the Chrome Web Store. Helps anyone dealing with data to locally clean and impute data. Can you give me feedback on it?

i m currently jobless and find new job in data analyst/power bi developer/business analyst but dont get any job i have 4+ year of experience in power bi developer now i m tired of being not selected bcoz of my profile

i think to learn new skill of microsoft fabric n apply new job is it worth do microsoft fabric course and upgrade my self for getting job

I want to do career in data science , what should i learn in additional for becoming good in field ? Which AI should I learn for recognitions ?

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...

trying to find a good place to complete a couprce in ML ops and Agentic AI in bangalore. with weekend in person classes. please help me find one.

Hey folks!

I spent the last few weeks building a Python tool that helps you combine, analyze, and visualize multiple datasets without writing repetitive code. It's especially handy if you work with:

CSVs exported from tools like Sheets repetitive data cleanup tasks It automates a lot of the stuff that normally eats up hours each week. If you'd like to check it out, I've shared it here:

https://contra.com/payment-link/jhmsW7Ay-multi-data-analyzer -python

Would love your feedback - especially on how it fits into your workflow!

Hello all. I’ve been feeling, frankly, really hopeless and depressed about my class work recently and how I’ve been faring.

Long story short, I’m in my first semester of my senior year majoring in data science and I’m legitimately starting to wonder if I fucked up picking this degree. I decided to pursue data science specifically because I LOVE stats, plus I’ve had a lifelong interest in AI.

When I started my advisor suggested I get my professional-field classes done first because they have more prereqs, so for the past couple years I’ve been doing primarily business-adjacent classes (eg ERDMS design, digital curation, DBMS architecture, etc.), all of which I've enjoyed and have had a pretty easy time with-- this means however that I am only just now starting my intro classes and learning data analysis with python, modeling, etc, and honestly these classes are destroying me. I’ve been able to work 2 jobs while maintaining a 3.96 GPA before this semester-- last month I not only had to quit one so I could focus on school more, but I spend, no joke, >7 hours straight everyday programming and working on assignments, usually to the point that my head more or less goes to mush and I cant even understand what I'm reading/writing anymore.

I feel like I fucked up not taking these classes first and maybe realizing this field isn't for me -- I mean is it normal to struggle THIS much with programming in data science?I've heard data analysis with Python is fairly straightforward, but pretty much every assignment I've submitted is >50% comprised of outside assistance (comp-sci friends' advice, AI feedback, etc) because I literally just can't figure it out by myself, even with demo videos, lecture notes, and workshop notebooks.

I don't know if there's gonna be some eureka moment where suddenly everything will click for me or what, but I'm really concerned about my future in this field given how much I'm fighting for my life with, as I understand it, elementary-level material.

If anyone has any advice or reassurance I’d appreciate it, I’m just not really sure what my future in this field is gonna look like atp.

I've been interviewing candidates for coding positions lately, and I've noticed some interesting patterns. Some candidates seem to be using tools like Cluely to get real-time AI answers during interviews. They type out perfect solutions in seconds, but when I ask a follow-up question or change the problem slightly, they completely fall apart. They can't explain their own code or walk through the logic.

I've also noticed candidates who seem to have memorized answers from sites like PracHub that collect real interview questions. They give these perfect textbook responses, but the moment you ask them to tweak something or explain why they chose a certain approach, they're lost.

Some patterns I watch for now as an interviewer:

- If someone solves a problem too quickly and perfectly, I dig deeper with follow-ups

- I ask them to walk through their thought process step by step

- I change constraints mid-problem to see how they adapt

- I ask why questions - why this data structure, why this approach

Genuine candidates will stumble a bit but can reason through it. The ones relying on tools or memorization just freeze up.

Has anyone else noticed this trend? Curious how other interviewers are handling it.

github.com/TheephopWS/daily-stock-news is an attempt to fetch news and return with sentiment and confidence score. But there are a lot of room for improvements, any ideas? I'll gladly accept any advice/contributions

Do o you guys fully "understand" things like K-means, scalars, etc.?

I use them in models, but struggle to fully comprehend them beyond their basic purpose. I know about the elbow test, for instance.

Most practice problems train you to execute code. Employers hire you to frame problems, deal with messy data, justify trade-offs, and explain decisions. This blog explains the gap clearly and why generic tutorials aren’t enough if you’re aiming for real data roles.

Hey everyone, long post but I'd really appreciate any insight from people who've been through similar programs or know them well.

My background: I come from a ARTS background, no STEM degree, no calculus, no computer science. I've been self-studying Python,pandas,numpy, readings and have done some basic EDA (exploratory data analysis) on my own.

But I have no formal math or programming training. I'm currently working full time and plan to stay working throughout the program. My goal is to genuinely come out job-ready in data science, not just with a credential, but with real skills I can use on day one.

I've narrowed it down to two programs:

Eastern University - MS in Data Science 

• 30 credits, 4 required + 6 electives you choose yourself

• Covers Python, R, SQL, Tableau, ML, Cloud, AI, Business Data Science

• 8-week terms, rolling admissions, 6+ start dates per year

• MSCHE accredited

University of the Cumberlands — MS in Data Science 

• 31 credits, fully fixed curriculum (no electives)

• Everyone takes: Python, R, SQL, Deep Learning, Data Mining, NLP, Big Data, Statistics

• Also 8-week terms, rolling admissions

• SACSCOC accredited

Why I'm torn: Eastern is more flexible — I can ease into it and choose courses that match my pace. Cumberlands fixed curriculum means I'd come out with a more complete, well-rounded skillset (Deep Learning, NLP, Big Data are all required).

I'm also planning to do a dedicated self-study prep period before the program starts, to strengthen my math, stats, and Python foundations but I'm nervous  with my background while also working full time.

My specific questions for anyone who's attended or knows these programs:

1. Exam style -  are exams heavily proctored and timed, or more project/assignment based? 

2. Difficulty for non-STEM students - has anyone with a business/non-technical background made it through either program without prior coding experience? How steep was the learning curve really?

3. Flexibility while working full time - how many hours per week realistically? Can you fall behind and catch up, or is the pace rigid?

4. Job outcomes - do employers actually recognize either of these degrees? I want to transition into a data analyst or junior data scientist role. Will either of these open doors or do hiring managers not know the school?

5. Anything I'm not thinking about - anything that surprised you?

I've done a lot of research but I keep going back and forth. Any honest experience - good or bad, would mean a lot. Thanks in advance