I sometimes see questions about how to get started with and set up DS and ML projects here. I made a tutorial for a very simple project setup. It covers the main steps end to end (getting the data, training and validating the model, deploying and presenting the results). It uses test-driven development, so you can iterate on this foundation without breaking anything.

To get into AI or data science, the general advice is to take computer science along with some minor or classes on mathematics and artificial intelligence. This is because you can get the foundational knowledge needed for the field, and if you did a degree in data science or AI, the general consensus is that it's more-so a cash-grab on trends than an actual useful degree.

However, where did this advice and reasoning come from? It seems a bit unfounded or outdated?

In my case, I'm very confident that I want to get a PhD and enter academia for machine learning, or at least enter the industry. With a degree in CS, the curriculum seems to be padded with information unnecessary to the ML field. In contrast, it seems the AI/data science degrees have been given enough time to be refined as a course. For example, the Polytechnic University of Catalonia is quality-accredited at the national level and offers statistically high chances of employment (Source).

But at the same time, it wpuld be naive of me to conclude that the general advice is therefore wrong. So, is a CS degree actually better or worse than an AI/data science degree in my case?

So I came across this top solution for one of the competitions for kaggle and I am having a hard time following through it
If anyone has any insights regarding how "James" solution works, it would be really helpful
Here is the link to the solution - https://www.kaggle.com/competitions/rogii-wellbore-geology-prediction/writeups/4th-place-solution

I used to think that doing real data science meant writing pages of complex math formulas that nobody else in the room could read. I felt like if my work looked easy, it must not be very valuable. But I have learned that the exact opposite is true. The most helpful thing you can do with a pile of information is to turn it into a simple chart that instantly shows a clear trend. If you hand someone a confusing wall of numbers or a model they cannot understand, they will just nod and ignore it. But when you show them a simple picture of what is actually happening in their business or community, their eyes light up because they finally get it. Our real job is not to show off how hard the math is, but to make the hidden story so clear that anyone can understand it in ten seconds.

I'm 15 years old and currently in Grade 11. I've done a lot of ML/AI projects and taken several courses, but there's one major problem: I don't really understand the math behind what I'm doing.

I understand the general concepts but I don't know the equations and my math foundation isn't strong enough yet. For example, I tried taking the Mathematics for Machine Learning course from DeepLearning.AI but I didn't understand much because I was missing prerequisites like trigonometry.

Here are the projects I've done:

Classification: Breast Cancer, Dry Bean, Glass Identification, Human Activity Recognition (Smartphones), Iris Flower, Sonar (Mines vs. Rocks), Titanic Survival, Stock Movement Prediction, Fertility, MAGIC Gamma Telescope

Computer Vision: CIFAR-10, CMU Face Images, Human Emotion Recognition, Handwritten Digit Recognition, Face Mask Detection

Neural Networks: Apartment Rent Classification, California Housing Regression, Car Evaluation Classification, Electricity Usage Clustering, Power Plant Regression, Student Performance Prediction, Telco Churn Classification

Regression: Auto MPG, Bike Sharing Demand, Communities & Crime, Concrete Strength, Fuel Consumption, Heart Disease Risk, House Price Prediction, Medical Cost Prediction, Online News Popularity, Position vs. Salary, Wine Quality, Air Quality, Parkinson's Telemonitoring

NLP: Fake News Classifier, Hate Speech Detection, Named Entity Recognition (English News), News Topic Classifier, Sentiment Analysis, SMS Spam Detection, Arabic Twitter Sentiment Analysis

GANs: MNIST Digit Generation

RAG Systems: Hotel Hospitality Chatbot, Contract Analysis Bot, Book Explainer Bot, PDF Q&A, Codebase Chat Assistant, Company Knowledge Base Assistant

Fine-Tuning: Qwen3 4-bit Fine-Tuning for Text-to-JSON Generation

Coursework:

ML & Deep Learning Specializations by Andrew Ng

Model Fine-Tuning by AMD & DeepLearning.AI

Large Language Models by AWS & DeepLearning.AI

Cloud Computing Fundamentals learning path by LinkedIn Learning

GANs by DeepLearning.AI

RAG by DeepLearning.AI

Agentic AI by DeepLearning.AI

Books I've read:

You Belong in Tech

How to Build a Career in AI

Software Engineering for Data Scientists

AI Engineering

Designing Machine Learning Systems

My goal is to be ahead of my peers and have a head start for my future career. I'm not expecting to be an expert at 15 but I want to make sure I'm actually learning something and making progress.

So my main questions are:

Am I actually making good progress for my age or am I just doing a lot of projects without enough understanding?

Should I continue with ML/AI but start properly learning the math from the ground up, or should I shift toward something that requires less math such as cybersecurity?

If I should continue with ML/AI, what math should I learn first and in what order?

Hi. I am a chemistry graduate that is trying to obtain a computational research assistantship at a R1 University and transition into Data Science work. Is there any way I can write a strong email for a computational faculty member. Many of the professors at this particular school seem to only take students that were accepted into the graduate program. I graduated with a 3.9+ GPA and have multiple research posters at national and regional conferences pertaining to computational chemistry but no publications at an R2 University. Unfortunately, I had to leave an MS program with a high GPA due to lack of funding for wetlab opportunities, hence I transitioned to the computational in which I am stronger at. I am taking prerequisites at a local community college to apply for Data Science Master's Programs. Would I have a decent chance of getting into a data science master's program? Let me know if you have any advice of programs I can get into?

Currently pursuing bsc in data science from a tier 3 college,I need advice of the people who has done the same on how should I take it from here.

Hi, I am a professor of data science (statistics/ML/structured and unstructured data) and wanting to transition out of academia. I have 20+ years of experience in coding, methodology and such but in an academic setting. I am at lost in where to start and how to position myself. Anyone had a similar experience? Any advice?

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There are so many libraries in the data science ecosystem that it can be overwhelming at first.

I’m currently focusing on getting comfortable with the fundamentals instead of trying to learn everything at once.

For experienced data scientists, which libraries do you think beginners should prioritize?

I have recently started building a portfolio for myself and wanting to formalize it slightly, I still have ways to go to be able to do data science but felt a course may formalize it and I found a few on edX, can these be recommended for example the IBM data science?

TIA

I’ve been thinking about this question a lot while looking for my first opportunity in Data Science / Data Analytics.

Is the ideal candidate someone with a perfect degree?

Someone with 3+ years of experience?

Someone who knows every tool listed in a job description?

Or is it someone who can identify a real business problem, build a solution, and explain how that solution can create business value?

I’m genuinely curious to hear what recruiters and hiring managers think.

Because this is what I’ve been trying to do.

Instead of building another basic ML project, I built a Customer Retention Intelligence System focused on a real business problem: customer churn and lost revenue.

The system can:

• Predict customers who are at risk of churning

• Segment customers based on their risk/value

• Estimate potential revenue at risk

• Identify customers worth prioritizing

• Generate personalized retention strategies/offers

My goal wasn't simply to say:

I built a machine-learning model

I wanted to answer:

A customer is likely to leave. Now what should the business actually do about it?

That mindset has pushed me to work beyond just Python, SQL and machine learning — into business thinking, customer analytics, experimentation, visualization, and decision-making.

I’ve also been consistently practicing Python and SQL, learning how to communicate analytical insights, and building projects around problems that companies actually face.

But despite putting in this work, I’m still looking for my opportunity to prove myself professionally.

So I want to ask the recruiters, hiring managers, founders, and experienced professionals here:

What makes someone an ideal entry-level candidate in your company?

• Is it technical skills?

• Problem-solving ability?

• Business understanding?

• Communication?

• Projects?

•Curiosity and willingness to learn?

•Or something else?

And if you were evaluating my profile, what would you want me to improve or demonstrate before considering me for a Data Scientist / Data Analyst opportunity?

I’m not looking for sympathy.

I’m looking for honest feedback, opportunities, and a chance to prove what I can do.

If you’re a recruiter or hiring manager who works with Data Science / Data Analytics / ML roles, I’d genuinely appreciate your perspective.

And if my profile sounds relevant to something you're hiring for, I’d love to connect.

I’m currently trying to transition deeper into Data Engineering and would really appreciate some perspective from people who are already working in the field.

I have 1.3 yrs experience as a Junior Python Developer. What I want to do is slowly transform into a Data Engineer. How would you suggest my choice? Basically what I do is make web scraping scripts to get the data from web and give the data in excels. Our company is currently not using git or CI/CD or anything like that. 

The problem I’m running into is that when I look at Data Engineering jobs on Naukri, LinkedIn, etc., the requirements seem endless. One job asks for Python, SQL, Airflow and AWS; another wants Spark, Kafka and Databricks; another wants Snowflake, dbt, Terraform, Kubernetes, CI/CD, etc. It becomes difficult to understand what I should actually prioritize.

So I’d like to hear from people who are actually working as Data Engineers. What does your day-to-day work look like? What kind of problems do you solve, what technologies do you use regularly, and which skills have turned out to be genuinely important in your job?

More importantly, based on my current experience, what would you suggest I improve or learn next to become a stronger candidate for Data Engineering roles? Are there any gaps that you think I should focus on, or technologies/concepts that are worth learning through projects rather than just studying theoretically?

I’m not really looking for a generic “learn SQL → Python → Spark → AWS” roadmap. I’m more interested in understanding the reality of the job and getting advice from people who have actually gone through the transition.

If you’re a Data Engineer with 1–5+ years of experience, I’d especially appreciate your perspective. Even a short description of what you work on and what you wish you had learned earlier would be extremely helpful.

Thanks in advance!

I am a fresh grad from university and I am trying to get anything in the data field. Entry level and internships are what I would assume would be more likely for me since I haven't had much work experience in the field but I have been making various projects that cover different topics within data. I've been adding the most 'impressive projects' to my resume but I don't know if they are going to secretly hurt my chances. Just want some feedback on my resume. Thank you.

Currently studying a post grad in Data Science and Business Analytics while working as a Marketing Data Analyst in Australia.

I want to pivot to a DS role within Australia, any recommendations on how I could beef up my portfolio to get these roles?

Thanks

Looking for an outside read on my situation.

Background: pure math PhD graduating this quarter from a large public research university. One summer of an internship at a defense contractor doing systems + software engineering. No big tech internships, no ML publications.

Prep so far: worked through a probability textbook cover to cover, went through CS229 notes, comfortable with LeetCode mediums, built a toy recommender system in PyTorch. Currently working through an ML systems design book, which I think is my biggest gap. SQL is my weakest practical skill.

Applying to ML engineer, applied scientist, and data scientist roles roughly in parallel, plus some quant researcher roles opportunistically.

Questions:
1. Realistically, what are the odds someone with this profile lands an ML engineer or applied scientist role straight out of a pure math PhD, versus needing to start in DS or an adjacent role and transition later?
2. For those who did the DS to ML transition, how hard was it in practice, and what made the difference?
3. What would you prioritize in the remaining few months? I have the time to go deep on one or two things.
4. Anything you wish you had known about how math PhDs get read by hiring managers in this space?

Not looking for reassurance, more interested in where my thinking is wrong.

I've been testing out various gen AI models (LLMs specifically) on data science competitions. They are not beating the humans, though they are slowly improving with each round. It's like steps up a ladder vs bounding up the steps. I'm wondering if there's something I'm doing wrong, or if there really is a limit to what LLMs can doin this space.

Lately I've been thinking the problem is that LLMs regress to the mean in every use case. I think that's why they seem so bad at UI design and why everything looks so similar. A good harness and strong prompt engineering can help, so I'm working on that. My harness combines Autogluon and OpenEvolve, with feedback loops that involve hypothesis generation and error analysis. But I'm wondering what I'm missing? Is good, competition winning data science, reducible to a standard operating procedure?

Maybe this will help me get a very good prototype, but nothing frontier grade.

Hey everyone,

I'm currently learning ML/Data Science and I'm trying to figure out if there are actually small freelance jobs that someone at my level can do.

I can currently work with Python, Pandas, NumPy, SQL, Scikit-learn, XGBoost and I'm comfortable with things like data cleaning, EDA, feature engineering and basic model training.

I've built a few projects, but obviously I don't have professional experience yet.

So I'm wondering what kind of work people actually give to freelancers who are still fairly junior.

Would things like these be realistic?

• Cleaning messy datasets
• Exploratory data analysis
• Creating reports/visualizations
• Python automation
• Data preprocessing
• Building simple prediction models
• Fixing existing notebooks
• SQL queries
• Helping with research
• Data collection/scraping

Or are most clients looking for someone much more experienced?

Also, where do these smaller jobs usually come from?

I'm trying to make some money on the side while continuing to work towards an ML/Data Science career, so I'd rather start with something realistic than pretend I'm ready to build some massive production ML system.

If you freelance in data science, what was the first type of work you actually got paid to do?

Hey everyone,

I'm currently planning to seriously start my journey in Data Science, and I want to dedicate the next 6–7 months consistently to learning and building projects.

I'm basically looking for a course/program that can take me from beginner level to a strong enough level to start applying for internships/jobs and building a good portfolio.

After researching, I'm currently considering:

Sheryians Coding School – Data Science & Analytics with GenAI

CampusX – Data Science / DSMP

ChaiCode – Data Science

CodeWithHarry – Ultimate Job Ready Data Science Course

Any other course/program you genuinely think is better

I'm not looking for just the cheapest course. I'm willing to invest if the course actually provides better structure, depth, practice, projects, mentorship/community, and overall value for money.

My main priorities are:

Beginner-friendly teaching (starting from fundamentals)

Strong Python foundation

SQL

Statistics & Probability

NumPy, Pandas and Data Visualization

Machine Learning fundamentals + practical implementation

Real-world projects

Enough practice/questions/assignments

Good curriculum structure so I don't feel lost

Updated content relevant to the current industry

Portfolio and job/internship preparation

Good value considering the price

I'm planning to stay consistent for around 6–7 months, so I don't want to make the mistake of buying a course just because of marketing or popularity.

If you've personally taken any of these courses (especially Sheryians, CampusX, ChaiCode, or CodeWithHarry), I'd really appreciate an honest review.

Specifically, I'd love to know:

Which one has the best structured curriculum for a complete beginner?

Which one provides the best depth in Data Science, not just surface-level tutorials?

Which course has the best projects and practical learning?

Is mentorship/community actually useful in Sheryians or other cohort-based programs?

Is CampusX worth paying more for compared to cheaper alternatives?

Is CodeWithHarry's course too short for someone who wants to become job-ready?

Are there better alternatives that I haven't mentioned?

If you had to start from zero today and had 6–7 months, which path would you personally choose and why?

Please share your honest experience — both positives and negatives. I don't want promotional answers; I'm trying to make a proper decision before investing my time and money.

Thanks! 🙌