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?

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

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

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

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! 🙏

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!

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! :)

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.

I will be starting university in September and I choose data science as my course. Where can I learn about data science as a beginner is their any youtube channel or website.

[ Removed by Reddit on account of violating the content policy. ]

Someone guide me for the same domain i’m in my second year and i need a proper roadmaps and provide me some sources to learn this domain and tell me about the job openings for this is it worth studying in this big 2026. or should i want to shift to some other domain

Hi everyone,

I'm a Management & Data Science student founder at TUM (Technical University of Munich). Over the past three years of studying and working with data, I've come to one conclusion that changed how I learn: how you learn matters more than how much information you consume. I remember what it felt like as a beginner — the more I "learned," the more overwhelmed I got, with no clear sense of how to actually turn scattered knowledge into real skills or a real portfolio. I want to help a small number of people avoid that trap and build a genuinely solid foundation instead.(I'm also currently building my own startup on the side, and I have to prepare bit more budgets for marketing part by doing more job).

Who this is for:

Complete beginners or early learners in data analytics who are ready to commit long-term — not people looking for a weekend crash course or another disconnected "masterclass." If you're the type who wants structure, depth, and consistency over quick hacks, this is for you.

What we'll cover

The curriculum is built around learning methodology and how to actually think about data — not just tool tutorials — combined with a full practical skillset:

Learning methodology & how to think about data (the foundation of everything else)

SQL: PostgreSQL & MySQL

Statistics

Excel for analysis

Power BI

Python & all packages needed

Git & GitHub

Data pipelines

Snowflake & Databricks

Cloud fundamentals: AWS & Azure

Applied AI for data analytics

Structure — two phases

Phase 1 (starting mid-August): Foundations

All the topics above, taught step by step, each section ending in a small hands-on project so you actually apply what you learn, not just watch it.

Phase 2: Portfolio & real-world experience

Larger, portfolio-worthy projects, real collaboration, and the business sense and soft skills that actually matter on the job. At this stage, I'll also bring in small internal projects from my own startup so you earn from real projects.

Logistics

Format: 100% online, from anywhere

Language: English

Schedule: 3 sessions/week, 1 hour each (55 min core content + 5 min learning-strategy discussion)

Price (Phase 1): €23/hour

Want to split the cost? You can pair up with one other person at a similar level and share a session

Billing: weekly or monthly, pause or cancel anytime, full refund for any unused sessions

Spots: limited to 10, first come first served

Not sure if it's a fit?

DM me to book a free 20-minute intro call — we'll figure out together whether this is the right fit for where you are and what you want to build.

Thanks for reading, and looking forward to meeting some of you.

Hello everyone,

I've been learning Data Analytics for some time now, but one of my biggest challenges has been staying consistent.

So far, I've completed Python and Statistics, and my goal is to become industry-ready within the next 4 to 4.5 months.

I still need to learn SQL, Power BI, and Excel, while also building a strong portfolio of projects.

I'd appreciate advice on how to structure my learning over the next 4.5 months. Specifically:

How would you categorize or prioritize these topics?

What sequence would you recommend for learning them?

How would you design a monthly and weekly study plan to stay consistent and make steady progress?

What are your practical tips for revision?

How do you balance revising previously learned concepts while continuing to learn new topics and building projects?

I would really appreciate any insights, study strategies, or roadmaps that have worked for you. Thanks in advance!

I'm a physics engineeering student, and I spend a lot of my time writing numerical simulations and analyzing data.

Programming in C++ is enjoyable, but most of numerical computing libs in are just unpleasant to use. So I started building my own solution in my free time.

GitHub: https://github.com/mslotwinski-dev/NumC

Some of the things I built into it: - You can write mathematical expressions naturally, like sin(x) * exp(-x), and differentiate or integrate them in a single line thanks to lazy expression trees. - It has a built-in plotting engine, so you can display graphs in a native Win32 window or export them as clean SVGs ready to drop into a LaTeX report.

Of course, the project won't surpass the quality of professional libraries. Its goal is to be convenient and accessible for users whose passions lie more in math, rather than programming.

If you're using C++ for simulations, numerical methods, physics, or data analysis, I'd really appreciate any feedback.

What was written by AI?

Most of the project, its entire idea, design, aesthetics and UX was programmed manually by me. I often used the textbook "Numerical methods in engineering with python" by Kiusaalas. Which doesn't mean that I didn't manage to do everything myself.

AI was used to write all the documentation (I wish I were fluent enough in English that it would take a finite amount of time). It was also used to write parts of simple algorithms that I knew but would be extremely tedious to implement by hand, or to improve the performance of algorithms that could be written better.

I'm aware of the ethical aspects of using AI, so I wanted to be honest and describe which things I did on my own and which I did with the help of LLM. At the same time, bearing in mind that this is a project that can help many people in their scientific work and studies, I hope that the benefits outweigh all the evil that LLMs cause.

Hi everyone.

I'm trying to decide between pursuing Data Science and Cybersecurity as a long term career, and I'd really appreciate advice from people who are already working in either field.

From your experience, which field offers better:

• Job security
• Salary growth
• Number of job opportunities
• Long term career prospects
• Demand across different countries

I'd especially like to hear from people who have worked in either industry or have seen how the job market has changed over time.

Thank you!

Hello all! I’ve been meaning to break into data and data science in specific. Just to provide background, I’m a senior cs major, and over the past 2 years I’ve been glowingly interested in working with data. Last summer I spent time learning data analysis. I have a solid foundation with Python and worked with pandas. In a prior project for data analysis, I analyzed loan data and used SQL and Tableau to create a dashboard. I understand data science is different in that, rather than focusing on uncovering information from data and making recommendations, data science is more focused on using data to train models to predict things using ML. I’ve been meaning to start a project but just lack the guidance to actually begin. I’m not sure where to start with actually creating a project, not just learning the info for it. I’m not exactly sure what a data science project should even look like and was hoping someone could point me in a direction and share some advice about how they first started out. Thank you all!!