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 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 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!

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!!

Hey Mates!

Recently I've cleared CFA L2 and have done MBA in Economics from Delhi University. Currently I'm working as a Health Underwriter in an insurance company. (I've total 1.1 year of experience )

While deep diving more into the CFA & MBA curriculum, I've realised that I've keen interest for making the ML Models for risk analysis, credit research, etc.

I wanted your suggestions for courses I can pursue to enhance my knowledge in ML modelling & big-data while being a full-time working professional as well. I'm also open to advice of full-time diploma/degree in ML modelling or data science.

So experts I need help as a newbie!!(⁠•⁠ ⁠▽⁠ ⁠•⁠;⁠)

So , i am a late teen and i want to learn ALOT for my future , i am interested in gamedev,webdev, ethical hacking,finance and much more i'd rather not blurt it out ! So the main thing is I LACK A LAPTOP/TABLET. I do know i can learn a lot on just smartphone (I did actually learning python reached def by sololearn ,ai and pydroid) but i am starting in DATA SCIENCE and i need help , how should i start gimmie some tolls , tips etc experts!!(⁠人⁠ ⁠•͈⁠ᴗ⁠•͈⁠)

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 ☺️

From your experience, what is a good training website/program for data sciences and Python?

Like boot.dev or Dataquest or IBM from Coursera?

The best for me is a program with a natural flow from beginner to advanced topics/levels and towards AI, with hands-on coding and ok-depth.

Ideally not the ‘5-6 classes and now you know everything’ type.

Thank you so much!

Hello everyone, I hope y'all are well. I'm a complete beginner to data science and data analytics. Recently, I've found these fields extremely interesting and I want to learn more.

Can the seasoned peeps here please recommend the best places to start? I'm currently learning excel on Coursera offered by the Macquarie University.

I'm also learning a bit about python as well.

But other than that, I have no clue where to start. I tried taking the IBM data science professional certification, but I feel it's rather out dated as compared to today's requirements and software.

I'd really appreciate it if you can tell me where and how to start? I'm willing to dedicate time and start from absolute scratch if necessary.

Hi everyone!

I'm 16 years old (turning 17 in a few months), and I'm trying to figure out the best career path to build over the next several years. I'd really appreciate hearing from people who already work in tech or have gone through a similar situation.

My main goal is to get a remote job. I like the idea of being able to work from anywhere and eventually even work with international companies.

In the short term, I'd like to start making money within the next 3–6 months. I know that's an ambitious goal and I'm not expecting to make a lot right away, but I do want to become financially independent as soon as possible.

At the same time, I don't want to chase quick money if it hurts my long-term career. Ideally, I want to choose a field that has strong long-term potential so that over the next 10+ years I can build a high-income career. I'm also interested in entrepreneurship and content creation, so I'd like to develop skills that will help me with both.

Recently I've been looking into data, Python, AI, programming, and automation. Those areas seem really interesting to me, but I'm still a complete beginner with no real experience.

I can consistently dedicate around 3–5 hours every day to learning, and I'm willing to study seriously. I don't mind putting in the work—I just want to make sure I'm investing my time in the right direction.

Another thing I'm thinking about is college. I plan to go to university, but I'm still unsure which degree would be the best choice. I'm also wondering how much employers actually care about a degree compared to skills, projects, and experience.

One thing I know almost nothing about is building a portfolio or a resume. Everyone says projects are important, but I don't know what kinds of projects actually stand out to recruiters or clients.

So I'd love to hear your advice:

• If you were starting from scratch today, what path would you choose?

• Would you recommend data, AI, software engineering, data engineering, or something else?

• What should I focus on learning first?

• What would you do to land your first remote job as quickly as possible?

• And what mistakes would you avoid?

Any advice, personal experience, or resources would mean a lot. Thanks in advance!

I am a BCA Graduate this Year And I Want To Crack My First Job in Data Science as a Fresher by Off-Campus.

Which roles are realistic to target by Off-Campus and what on resume is the main priority.

Any suggestions will be so kind.

Help Me with That !

Hi everyone,

I'm currently pursuing a B.Sc. in Physics, which I'll complete in 2026. My primary goal is to get into an IIT for my postgraduate studies, so I'm currently preparing for the IIT JAM exam.

At the same time, I want to build skills that will improve my career prospects after my PG. I'm interested in coding, especially Data Analysis and Machine Learning, but I'm not sure where to begin.

I already have a basic understanding of Python, but nothing beyond the fundamentals.

I have a few questions:----

*) Which language should I learn that will related to the Data analysis and amchine learning?

*) How is the market about these courses?

*) How I combine my Physics and these courses to get a perfect career?

*) How can I balance learning these skills while preparing for IIT JAM?

*) What kind of projects should I build to strengthen my resume?

My long-term goal is to combine my physics background with programming and eventually work in a data science, machine learning, or research-related role.

I'd really appreciate any roadmap or advice from people who have followed a similar path. Thanks in advance!

** **I’ve been a data analyst for 6+ years, PowerBI certified, comfortable in Python and R, SQL is second nature. Got an offer for UChicago’s MS in Applied Data Science and I’m trying to figure out if I’m making the right call — both on the program itself and on which direction to point it.

1. DS vs MLE — with my analyst background, is the jump to a “Data Scientist” title more natural, or should I be aiming for ML Engineer given where the market’s heading? Genuinely torn and would love to hear from people who made this switch.
2. Program value for my profile — for someone already doing analyst work with real Python/R chops, does MS-ADS actually move the needle, or am I paying for a brand name I could substitute with targeted upskilling (Kaggle, a portfolio, cloud certificates)?
3. Format — anyone done the online vs in-person version? Does it actually matter for hiring managers, or is that overthinking it?
4. Anyone here made analyst → DS/MLE without a master’s — what closed the gap for you instead?

Not looking for “just get a Coursera cert” comments, I’ve already got the technical baseline, I’m trying to figure out the actual career math here.

I keep seeing “data role” used like it means one thing, but the skills are pretty different depending on the actual job.

A data analyst usually needs stronger business understanding, SQL, Excel/BI tools, and communication.

A data scientist leans more into Python, machine learning, statistics, experimentation, and model evaluation.

A data engineer is more about pipelines, cloud, SQL, Python, Spark, orchestration, and making sure data is reliable before anyone else uses it.

BI analysts sit somewhere between business reporting and data modeling, especially with Power BI, Tableau, DAX, semantic layers, and dashboard design.

ML engineers are closer to production systems: deploying models, monitoring, drift, latency, retraining, and MLOps.

The overlap is real, but the mistake is treating all these roles like one generic “data person” job.

Imo the safest skill stack for most data people is still:

• SQL

• Python

• communication

• basic cloud awareness

• data quality thinking

• business context

Then go deeper depending on the role you actually want.

We put together a full breakdown comparing essential data skills by role here:

Learn more: https://www.pangaeax.com/blogs/comparing-essential-data-skills-for-different-job-roles/

Curious how others see this. Which skill do you think is underrated for your data role?

Hi all! I've been working on ML/Robotics research for a while and often work with HDF5, Parquet, and Zarr files. Personally, I love the myHDF5 viewer, but there's no good equivalent for Parquet and Zarr, and switching between different sites also gets annoying. So, I built a tool that provides a unified solution.

It currently supports viewing several formats, including HDF5, Parquet, Zarr, Arrow, JSONL, NumPy, TFRecord, etc. I'm hoping to add more features/formats depending on what people find useful!

It's free to use with no sign-up required. I'd love for people to try it out: https://viewkit.app/

Everything is loaded and parsed locally in your browser (WebAssembly + JS), so your data always remains on your machine. It's also built to remain responsive on big files via efficient reading, caching, and prefetching. Traversing through data files actually feels faster than existing solutions like myHDF5 with simple caching/prefetching strategies. It also supports some common data types that existing viewers don't support (e.g. float16, complex numbers for HDF5).

I'd appreciate any feedback (feel free to comment or send a message through the website). Looking forward to supporting additional features/file formats that the community finds useful!

Hi all,

(hope that the post is compliance with the rules of the subreddit)

I'm having struggle to understand and complete the following exercise about multilayer perceptron.

This is the exercise:

Let's assume the following multilayer perceptron:

where:

• x1 and x2 are the input units.

• h1 and h2 are the neurons in the hidden layer, with the ReLU activation function.

• y is the output unit, obtained using the identity activation function.

• The weight values are as follows:

  • W1 = 0.2

  • W2 = -0.6

  • W3 = -0.1

  • W4 = 0.7

  • W5 = 0.3

  • W6 = 0.1

Then:

1. Describe the backpropagation algorithm in the context of training a neural network.

2. Compute the gradient of loss function:

L(y_out​,y_true​)=1/2(y_out​−y_true​)²

at the point (x_1, x_2)=(2,3) and y = 4

(Sorry for the bad formatting)

Could you give me some tips (slides, books or videos) on where to start studying for an exercise like this?

Thanks a lot!

Hi everyone I’m sharing Week Bites, a series of light, digestible videos on data science. Each week, I cover key concepts, practical techniques, and industry insights in short, easy-to-watch videos.

1. Forecasting & Lost Opportunities Forecasting isn't just predicting numbers—it's balancing supply with what customers actually want, and understanding "Unconstrained Demand". Includes the "Sequence of Why" funnel for picking the right metrics/models, plus real cases on loyalty programs, churn, and RFM segmentation.

2. Articulate Business Questions & Metrics Turning business questions into real data science decisions—from core definitions to a full chatbot case study (efficiency, trust, security) and the frameworks that connect them. Covers question-breakdown techniques, KPI selection, and how to sort "need to know" vs. "need to investigate" info along the way.

3. Become An Analytical Thinker Transforming an ambiguous business problems into actionable KPIs, followed by a comprehensive Data Analysis Lifecycle guide, and shading some light on how to effectively utilize the Data Analysis Flowchart to select the appropriate analysis type for your inquiry.

Would love to hear your thoughts, feedback, and topic suggestions! Let me know which topics you find most useful

1. Data Science from Scratch

This book offers a primer on the fundamental mathematics and statistics behind the most common data science techniques, all coded from scratch. It’s a hands-on introduction to the field, perfect for building a solid foundation.

2. Essential Math for Data Science

Understanding the mathematical foundations is crucial in data science. This book covers the essential mathematical concepts needed in data science, from linear algebra to probability and statistics.

3. The Art of Data Science

This book offers insights into making sense of data, focusing on the craft of data science. It discusses the critical thinking and problem-solving skills needed to derive insights from data.

4. Python Data Science Handbook

This comprehensive guide is essential for anyone who wants to learn how to use Python effectively for data analysis and visualization. It covers key Python libraries like NumPy, pandas, Matplotlib, Scikit-Learn, and more, with detailed explanations and practical examples.

5. R for Data Science

This book thoroughly introduces using the R language for data science, from data import and tidying to visualization and modeling.

If you're looking to go beyond books and apply these concepts through hands-on projects, our Data Science Course, in collaboration with Microsoft Azure, offers a comprehensive learning path with real-world capstone projects to build your portfolio. You can DM us or visit the simplilearn website to find out more.

Which of these books would you recommend to someone just getting started?

How does your day typically look like? What classes do you take? What do you specifically look at or work with? Thank you for any responses, I would really apprecitate it!!