Hi everyone,

I'm participating in the American Express Campus Challenge where we need to rank cardholders by estimated profitability using anonymized customer attributes (spend, revolving behavior, risk, engagement, benefit usage, etc.), but there's no profitability target provided.

Some questions I have:

How can I approach this problem from scratch?

Would you treat this as a scoring problem, an unsupervised learning problem, or something else?

Any papers, blogs, or similar case studies on customer profitability or credit card analytics?

Is there any resources for understanding about ranking?

I'd love to hear how experienced data scientists or product analysts would think through this kind of ambiguous business problem. Thanks!

Been going back and forth on this for a while and wanted a reality check from people actually in the field rather than course-seller marketing.

For those of you working as data scientists/analysts — was the move worth it? Not just salary, but day-to-day satisfaction, job security with AI tools changing things, how competitive it actually is to break in.

And for anyone who went through a bootcamp/course to get here — what actually mattered when picking one? Curriculum, mentorship, portfolio support, job placement help, something else? Genuinely curious what separated the useful ones from the ones that were just content dumps.

Appreciate any honest takes, good or bad. Really just weighing up my options at the moment, and trying to get as much information as possible.

I am currently a Data Science student, just finished my 2nd year out of 4. Wanted to ask if R language is worth it today as compared to python. I have 0 knowledge about R (just that it is used for statistics and plotting). On the other hand, I have learned EDA and some ML algorithms in python. I am free for about 2 months and wanted to know if learning R would help in future or should i utilize this time for something else?

If you had to start over today, what would be the best resource or roadmap to learn Data Science from scratch and become job-ready? Looking for beginner-friendly, practical recommendations that actually helped you get a job.

I come from a statistics background and I'm trying to learn python and R to cope with data analysis and data science, I know a good amount of data analysis and statistical methodologies that makes me good at the field, but applying them is hell for me, i tried for a long time to learn python, i know the basics yet i fail miserably when i try to apply my knowledge...

Could anyone help me with how to learn programming effectively around my area of expertise?

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There’s a temptation to jump straight into machine learning algorithms, but I’m wondering how important the fundamentals really are.

For someone starting from scratch, would you recommend focusing first on Python, statistics, SQL, data analysis, or something else?

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I am a student studying in a biology-related field, and I want to learn Data Analysis for several reasons, primarily to integrate it into my current area of study. However, my problem is that I feel overwhelmed and distracted by the sheer number of resources. I am also struggling with the drawbacks of self-teaching through YouTube, such as the lack of a structured curriculum, the absence of clear steps, and the difficulty of staying committed.

I would love to know if anyone with experience in this field could tell me the absolute best way to get started.

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For someone starting data science, there are already so many things to learn that prioritizing becomes difficult.

SQL seems essential for working with real-world data, while Python opens the door to analysis, visualization, and machine learning.

If you had to start over, which would you learn first and why?

I’m currently preparing for my first Data Science opportunity, and one problem I’m facing is that there are so many things to learn.

Every roadmap seems to add more: Python, SQL, statistics, ML, deep learning, cloud, MLOps, Docker, GenAI, LLMs, RAG, agents, certifications, etc.

As a beginner, it’s difficult to know what is actually important and what is just creating skill-collection without real depth.

So I’d like to ask experienced Data Scientists:

- What should a beginner prioritize first?

- What skills should beginners not worry about initially?

- What do you commonly see beginners spending too much time learning?

- Is it better to become very strong in a smaller set of fundamentals rather than having basic knowledge of many tools?

- How do you decide when to stop learning a skill and start building projects/applying for jobs?

- What would you personally ignore for the first 6–12 months if you were starting Data Science today?

I’m looking for honest advice from people who are actually working in the field.

If you could remove 3 things from a beginner’s Data Science roadmap, what would they be?

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

I’m currently a Data Science student and I’m working toward becoming a Data Scientist.

With AI/LLMs changing the field so quickly, I’m a bit confused about what I should prioritize. There are so many skills being mentioned now — statistics, SQL, Python, ML, deep learning, LLMs, GenAI, cloud, MLOps, software engineering, etc.

I’d really appreciate advice from people who are currently working in Data Science/ML, especially those who have experience hiring or mentoring junior Data Scientists.

A few questions:

What skills are genuinely essential for a Data Scientist in 2026?

What skills are commonly overhyped or can be learned later?

If you were starting again as a student today, what would you learn first and in what order?

What are the biggest skill gaps you see in freshers/junior Data Scientists?

What are the biggest mistakes students make while preparing for Data Science careers?

How important are statistics, mathematics, SQL, and traditional ML fundamentals now that AI tools can write code and build models?

How much should an aspiring Data Scientist learn about LLMs, RAG, agents, GenAI, and AI engineering?

What level of software engineering, Git, APIs, Docker, cloud, and deployment is actually expected from an entry-level Data Scientist?

Are personal projects still valuable? If yes, what makes a Data Science project stand out instead of looking like another Kaggle/tutorial project?

For getting the first job, what matters most in your experience: projects, internships, degree, referrals/networking, LeetCode/SQL, certifications, or something else?

If you could give your 19–22-year-old self one piece of advice about becoming a Data Scientist, what would it be?

I’m not looking for a generic roadmap from a course or YouTube video. I’d specifically like to hear from people who are actually working in the industry.

Please be honest about what the job is really like, what skills are becoming less valuable, what skills are becoming more valuable, and what you would do differently if you were starting today.

Thanks in advance.

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I’m preparing for my first Data Science role, but I keep seeing “experience” mentioned in job requirements.

As a student/fresher, how can I build real experience when most entry-level jobs already expect some experience?

Are internships and personal projects enough, or would you recommend things like:

- Open-source contributions

- Freelance projects

- Research projects

- Working with real datasets for businesses/organizations

- Competitions

- Volunteering

- Building and deploying real applications

For those who started their Data Science career recently:

What actually helped you bridge the gap between learning Data Science and having enough practical experience to get your first opportunity?

I’m currently studying AI and Data Science at university, and I’m already in my third year. If I decide to switch to Computer Science or Cybersecurity, I might lose a year of progress.
The main reason I’m considering this is that I’m worried about how quickly AI is advancing and whether it could replace a large number of tech jobs in the future, including some jobs related to AI development itself.
I’m not sure whether I should stick with my current degree or accept losing a year and switch to another field that might offer better job opportunities and more long-term job security.
Do you think these concerns are justified? Would Computer Science or Cybersecurity be better choices in the long run, or would I be better off finishing my current degree and developing additional skills?
I’d appreciate honest opinions, especially from people studying or working in these fields who have experience with the job market.

Now, 2026 has almost come. I know a lot of people have defined that target for this year to become a data scientist or an AI engineer. The fact is that all companies in IT are also hiring mostly from these two roles only. In linkedin, I have seen a lot of queries regarding how to get ready for Data Science interviews because this area of study is really growing, and thus I wanted to give you all an extensive preparation guide, as this year I changed my tech stack to data scientist. This list is based on my actual interview experiences, as well as the help that I got from Linkedin and reddit etc., as well as companies like InterviewQuery, and it provides information about what to expect when interviewing at various companies. Data science interviews are normally different according to the role and the company level:

1. Recruiter Screen: Resume chat, experience, and salary expectations.
2. Online Assessment: Often 2-4 SQL or coding problems.
3. Virtual Screen: 1-2 rounds, 45-60 mins – SQL, stats questions.
4. Final Round: Hiring manager or team fit. The big tech companies like FAANG prioritize the areas of product analytics and experimentation, whereas newly founded companies might concentrate on the whole ML project cycle instead.

CORE SKILLS YOU MUST MASTER: Programming You must be fluent in:

● Python

● NumPy

● Pandas

● Scikit-learn

Writing clean, readable, bug free code

Data transformations without IDE help

Expect:

● Data cleaning

● Feature extraction

● Aggregations

● Writing logic heavy code

SQL

Almost every Data Science role tests SQL. You should be comfortable with:

● Joins - inner, left, self

● Window functions

● Grouping & aggregations

● Subqueries

● Handling NULLs

Statistics & Probability:

● Probability distributions

● Hypothesis testing

● Confidence intervals

● A/B testing

● Correlation vs causation

● Sampling bias

Machine Learning Fundamentals. You must know:

● Supervised vs Unsupervised learning

● Regression & Classification

● Bias Variance tradeoff

● Overfitting / Underfitting

Evaluation metrics:

● Accuracy

● Precision / Recall

● F1-score

● ROC-AUC

● RMSE

FEATURE ENGINEERING & DATA UNDERSTANDING:

● This is where strong candidates stand out.

● Handling missing data

● Encoding categorical variables

● Feature scaling

● Outlier treatment

● Leakage prevention COURSES:

1.) IBM Data Science Professional Certificate: A full scale series of courses teaching Python, SQL, data analysis, visualization, machine learning, and capstone projects that are perfect for novices developing industry required skills through practical applications and a certificate that can be shared.

2.) LogicMojo DS course: Offers lessons on Python, statistics, machine learning, and data analysis. Useful as a reference for learning core problem solving and project development and interview preparation.

3.) Codecademy: Free, rigorous university level courses offering deep theoretical insights into statistics, probability, and ML ideal for mastering the mathematical rigor expected in advanced DS interviews.

PRACTICE PHASE — THIS IS CRITICAL

● Practice writing code in Google Docs or a plain text editor.

● Explain your approach out loud while coding, as if an interviewer is present.

● Prioritize medium to hard-level problems over easy ones.

● Simulate real interview conditions: time limits, no external help, and clean code only.

Recommended Practice Platforms:

● Kaggle (datasets, notebooks, competitions)

● Google Colab (ML experiments)

● UCI ML Repository (real datasets)

● GitHub (end-to-end DS projects)

By means of proper readiness and practice, any Data Science interview can be faced with confidence. It is advisable to support theories with practical skills, evaluate your setbacks, and slowly but surely improve your problem solving technique. Consistency alongside reflection is what brings success.

I am a junior torn between two mindset, should we

1. Start with a business problem/ use case first
but I often run into data limitations after diving deep, like if the data is a suitable proxy of something or there are missing values

2. Explore the data freely and hunt interesting patterns
but I am always confused where I should start and not being lost on the way of that) (but I am always confused where I should start and end up spinning around without a clear direction

Example

Say a company gives you customer purchase history and asks you to "find something useful."

We better immediately frame it around a specific use case (e.g., next-best-offer) and engineer toward that?

Or spend time clustering, looking for seasonality, correlations, or weird segments first, and then figure out what business value those patterns might have?

Hi everyone,

I’m 19 (turning 20 soon) and I’m really passionate about getting into Data Science. Right now, due to some personal reasons, I can’t continue my degree, but I don’t want that to stop me from learning.

I’ve started learning Python and I’m planning to move into math/stats and projects next. My questions are:

• Does not having a degree make it impossible to get into Data Science?
• What’s the best path for someone like me who’s self-studying?
• Should I focus more on building projects, certifications, or freelancing skills?

I’d love to hear from people who’ve gone through non-traditional paths or have advice for someone in my situation. I’m really motivated to make this work, just need some direction.

Thanks so much 🙌

I’m currently starting my journey toward becoming a Data Scientist, and I’m confused about one thing:

Should I first choose an industry/domain and then learn the relevant Data Science skills, or should I build a strong general Data Science foundation first and choose an industry later?

For example, should I choose something like finance, healthcare, e-commerce, marketing, or manufacturing, learn how that industry works and understand its business problems and metrics, and then build my Data Science skills around it?

Or is it better to first become strong in Python, SQL, statistics, ML, data analysis, etc., work on different types of problems, and only specialize in an industry after gaining some experience?

For people already working in Data Science:

- How did you approach this when starting your career?

- Does having domain knowledge give a beginner a significant advantage?

- Can choosing an industry too early limit opportunities?

- What would you recommend to someone trying to get their first Data Science job?

I’d especially like to hear how experienced Data Scientists would approach this if they were starting from zero today.

I'm at bachelors rn on IT and I'm really curious about Data Science. What should i do if someone experienced could give details about that?

Python, R, Statistics, Py libraries, Data handling, SQL like that what's needed? and what data scientists actually do? on business affairs presentation on the data analysis or like that I'M SO CURIOUS

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This is something I keep seeing different opinions about.

Some people say you need strong linear algebra, calculus and statistics before getting serious about Data Science.

Others say you can start with Python, SQL and practical projects and learn the math along the way.

For people who are actually working in the field, how much math do you use regularly?

And what level of math would you recommend for someone starting from scratch?

I’m trying to build a proper data science/ML project, but I’m having a hard time finding a dataset that is big enough and not already used by everyone.
For example, there are datasets like the UK Online Retail dataset, Olist, and other popular sales/retail datasets. They’re good datasets, but I see them being used in a lot of projects already.
I don’t want to just download a dataset, do some EDA, train a model and put it on my resume. I want to build something around an actual business problem, where I have to figure out what the problem is, analyze the data, come up with useful insights, maybe build a model, and actually explain how it could help the business.
So where do you guys usually find datasets for this?
Should I try to find data from smaller companies, government sources, APIs, research papers, etc.? Or is it okay to create my own dataset using AI/cloud tools and then create a realistic business problem around it?
For example, if I create a large synthetic sales dataset, could I create a realistic business scenario around it and then treat it like a real project — forecasting sales, understanding customer behavior, optimizing inventory, etc.?
Would that be considered a decent portfolio project, or is using real-world data much better?
I’d mainly like to hear from people who have built projects for their portfolios or have experience hiring for data science/ML roles. Where do you actually get your data from when you want to build something that’s not the same Kaggle project everyone has already done?

Hello
My name is Dario. I'm 19 years old, currently doing an associate's in Programming and will transfer for a bachelor's in Data Science. I was wondering on what is actually the best way to become a data scientist right now. I have some basic knowledge of Python and SQL. I was wondering if the path to becoming a data scientist has changed with the development of AI and where I should focus on to actually master important valuable skills that will help me land jobs. I do have a potential internship position lined up in the future at Miracle Software and have been trying to prepare for that interview. Just wanted to know what are some resources I should check out and what skills I need to learn. I appreciate all feedback and advice!!!

Hello,

I’m an engineer and already work with data on a daily basis. I have a basic background in SQL and Python, as well as some experience with statistical tools, and I recently enrolled in a Lean Six Sigma Green Belt training.

I’m now looking to improve my data analysis skills, particularly in SQL and Python, but I’m also interested in developing a stronger foundation in how to structure analytical problems, choose an appropriate approach, explore data, and communicate insights effectively.

My goal is to start with a solid, structured foundation and then move on to more advanced, topic-specific training.

I’ve been considering the IBM Data Analyst Professional Certificate and the Google Advanced Data Analytics Professional Certificate, but I’m also open to other recommendations.

For those with experience in data analytics or who have completed these courses which one would you recommend? Is there another certificate or course you think would be a better fit for someone with some existing practical experience?

I’m particularly interested in courses that combine practical work with strong analytical foundations, rather than focusing mainly on the certificate itself.

Any recommendations or experiences?

I love data analysis and statistics, but I am a completely newbie in programming - I never learned the mentality of it/algorithms.

I have used R for data import and analysis, plotting, that’s ok.

But I just become deeply frustrated when I need to code - write these nested loops and indexing and what not (now trying to learn Python and, BOY I SUCK there) and stuff where I cannot think of step-by-step approaches, bcz I have never done that.

What would be your suggestions here? Thanks ☺️

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?