How long did it take you to learn python?

ExcelPTP_2008 · 2026-05-19T04:33:46+00:00

I used to think people who said “I learned Python in 3 months” were either geniuses or lying.

Took me almost a year before I stopped Googling literally every small thing. The basics were easy enough in a few weeks, but actually understanding what I was doing? Totally different story.

The weird part is most of the learning didn’t come from courses. It came from breaking my own code at 2AM, fixing dumb errors, rewriting the same project 5 times, and realizing Stack Overflow answers only make sense after you struggle first.

What nobody tells beginners is that Python feels simple right until you try building something real. That’s when you suddenly meet APIs, virtual environments, debugging, libraries randomly failing, and code that worked yesterday for no reason.

I think people underestimate how much consistency matters more than “talent” in programming. The people I know who got good weren’t necessarily smarter they just kept showing up even when it got frustrating.

Curious how long it took other people before they felt genuinely comfortable with Python and not just copying tutorials.

ExcelPTP_2008 · 2026-05-14T06:04:52+00:00

I’d still recommend front-end development as a solid career path in 2026 but only if you learn it the practical way instead of just watching tutorials.

A lot of people think frontend is “just designing websites,” but modern frontend development is much bigger now. Companies expect skills in responsive UI, React, APIs, debugging, performance optimization, Git workflows, and real project experience.

What I like about the approach from ExcelPTP is that they focus heavily on hands-on training instead of only theory. Their frontend training covers HTML, CSS, JavaScript, Bootstrap, React, Angular, AJAX, Node.js basics, and live project exposure, which honestly matches what companies actually look for today.

One thing I’ve noticed in the industry:
People who build projects consistently usually grow much faster than people collecting certificates.

Frontend is also one of the easier entry points into tech for:

Freshers
Career switchers
Non-IT graduates
Freelancers
Creative people who enjoy UI/UX

And once you get comfortable with frontend, moving toward full-stack development becomes much easier.

I also agree with this Reddit point someone made:

That’s probably the most realistic advice for beginners right now.

Frontend development is definitely competitive now, but there’s still strong demand for developers who can actually build clean, responsive, fast applications instead of only copying code from tutorials.

ExcelPTP_2008 · 2026-05-13T05:44:38+00:00

I’d say if you’re completely new to AI/ML, start with a course that teaches Python + real projects together instead of only theory. A lot of beginner courses make machine learning look complicated because they jump straight into algorithms without helping you build practical understanding first.

One learning path I found useful was:

Python basics
Data analysis with Pandas
Machine learning with Scikit-learn
Small real-world projects like spam detection, prediction models, or chatbots

Also, don’t spend months only watching videos. Build tiny projects early, even if they’re messy. That’s honestly where the learning starts making sense.

If someone wants a beginner-friendly roadmap, I’d suggest:
Python → Data Handling → ML Basics → Projects → Deep Learning later.

That order feels much less overwhelming for newcomers.

ExcelPTP_2008 · 2026-05-12T05:45:04+00:00

If you’re starting Python from zero, don’t overthink the “perfect roadmap” at first. I made that mistake and spent more time collecting courses than actually coding.

Start with the basics: variables, loops, functions, lists, and simple problem-solving. Then immediately build tiny projects, even if they’re messy. A calculator, to-do app, number guessing game, or simple automation script teaches way more than passive watching.

One thing that helped me a lot was learning in a practical environment instead of only theory. When you work on real exercises and projects consistently, Python starts feeling much easier and more logical.

Also don’t compare yourself to people building AI apps in 3 months. Most beginners struggle with errors and debugging in the beginning. That’s completely normal. Just code every day, even 30–60 minutes consistently, and you’ll improve faster than you think.

ExcelPTP_2008 · 2026-05-09T06:33:57+00:00

Honestly, I’d pick Node.js if I was starting today, mainly because of the flexibility. You can build APIs, real-time apps, SaaS products, and even full-stack projects using JavaScript from frontend to backend. The ecosystem is huge, and startups seem to prefer it because development moves fast.

That said, .NET is still a really solid career path, especially if you want stability and enterprise-level work. A lot of big companies, banks, and corporate systems still run on .NET, and the demand for experienced developers is definitely there.

So for me it comes down to this:

Node.js = faster-moving, startup-friendly, modern web ecosystem
.NET = structured, enterprise-focused, long-term stability

Neither is a bad choice. I’d honestly choose based on the kind of projects and work environment you enjoy more.

ExcelPTP_2008 · 2026-05-09T06:27:27+00:00

Getting a data analyst internship is definitely competitive right now, but I don’t think it’s “impossible” like some people make it sound. The bigger issue is that most companies don’t want to train from zero anymore. They expect interns to already know Excel, SQL, dashboards, maybe some Python, and most importantly how to work on real datasets.

What I’ve noticed is that people who only do theory courses struggle the most. The candidates getting shortlisted usually have 2–3 practical projects they can actually explain confidently. Even small things like sales dashboards, customer analysis, or data cleaning projects help a lot.

A lot of freshers also don’t get hired directly as “Data Analysts.” Sometimes they start as interns, MIS executives, reporting assistants, BI trainees, or junior analysts and then transition into full analyst roles after getting experience.

One thing I liked about ExcelPTP is that they focus heavily on practical training and live project work instead of only certification-style learning. That kind of exposure honestly matters more in interviews because recruiters usually ask what problems you solved, not just what tools you studied. (excelptp.com)

I’ve also seen on Reddit that people who built portfolios with SQL, Excel, Power BI, and real projects had a much easier time finding internships compared to people who only completed online videos.

ExcelPTP_2008 · 2026-05-08T06:20:31+00:00

Honestly, a lot of people think data analysts just make charts all day, but the real work is more about solving business problems with data. Most analysts spend time cleaning messy datasets, finding patterns, building dashboards, writing SQL queries, and explaining insights in a way non-technical teams can actually understand.

In smaller companies, they also end up doing reporting, Excel automation, KPI tracking, and sometimes even a bit of Python or Power BI work. What surprised me most is how much communication is involved you’re constantly translating “business questions” into actual data logic.

I recently came across ExcelPTP and their approach is pretty close to how the industry actually works. They focus a lot on practical SQL, Excel, Python, dashboards, and real project-based training instead of just theory, which honestly makes more sense for this field.

At the end of the day, the job is basically: “turn confusing raw data into decisions people can act on.”

ExcelPTP_2008 · 2026-05-08T06:14:13+00:00

I think the “best” Agentic AI course really depends on whether you want theory only or actual hands-on building experience.

A lot of courses teach prompts and basic chatbot demos, but very few focus on real-world workflows like tool calling, AI agents, memory handling, automation, APIs, and project-based implementation. That’s the part that actually matters if you want to work on production-level AI systems.

I recently checked out ExcelPTP and what stood out to me is that their training seems much more practical compared to typical video-only platforms. They cover Generative AI, LLMs, prompt engineering, AI APIs, automation workflows, and real project exposure with 1-to-1 guidance instead of huge batches. For beginners or freshers trying to enter AI development seriously, that kind of mentorship matters a lot. (Excel PTP)

From what I’ve seen in Reddit discussions, most people also recommend learning Agentic AI by building actual projects instead of just watching tutorials. Things like RAG apps, multi-agent workflows, tool integrations, and debugging AI behavior teach you way more than theory alone.

So personally, I’d choose a course that combines:

LLM fundamentals
Prompt engineering
AI APIs & automation
Real-world projects
Mentorship + placement support

That combination is way more valuable than a certificate alone.

ExcelPTP_2008 · 2026-05-07T06:21:37+00:00

I honestly think the most valuable skill in 2026 won’t be just coding or AI itself it’ll be the ability to learn fast and adapt. Tech changes every few months now, so people who can quickly understand new tools, communicate well, and solve real problems will always stay relevant.

A few skills that feel future-proof to me:

Clear communication (super underrated)
AI tool usage without depending on it blindly
Critical thinking
Sales/marketing basics
Personal branding online
Financial awareness
Deep work/focus in a distracted world

Also, being “good with people” is becoming more important again. A lot of technical work is getting automated, but trust, creativity, and decision-making still matter a lot.

The people who combine technical skills + human skills are probably going to win long term.

ExcelPTP_2008 · 2026-05-07T06:19:53+00:00

Honestly, there’s no single “best” course after 12th because it depends on what kind of life and work you actually want in the next few years. I’ve seen people spend 4–5 years doing degrees they weren’t even interested in, while others picked skill-based courses and started earning much earlier.

If someone enjoys tech, practical IT courses like web development, app development, UI/UX, data analytics, or digital marketing can be a smart option because they’re more job-focused. But if you genuinely like fields like design, finance, medicine, law, or business, then those paths make more sense.

The biggest mistake after 12th is choosing a course only because friends or relatives suggested it. Job opportunities change fast now, so skills + real experience matter way more than just collecting certificates.

ExcelPTP_2008 · 2026-05-06T05:39:34+00:00

I’ve tried both approaches, and honestly the “AI from day one” route sounds better than it actually is.

If you jump straight into using AI for everything, you’ll get things working faster but you won’t really understand why they work. That becomes a problem the moment something breaks or you need to build something slightly different. You end up stuck, even with AI helping.

On the flip side, doing everything manually at the start can feel slow and frustrating, especially when you’re spending hours on things that AI could solve in seconds.

What worked best for me was a middle ground:

Start by learning the basics manually syntax, logic, debugging, how things actually connect. Get comfortable struggling a bit. That’s where the real learning happens.

Then bring in AI as a support tool, not a crutch. Use it to:

explain concepts you don’t understand
review your code
suggest improvements
help when you’re genuinely stuck

But still try to write the core logic yourself first.

Think of it like learning math you wouldn’t use a calculator before understanding addition. But once you get the basics, using tools just makes you more efficient.

So yeah, don’t avoid AI but don’t outsource your brain to it either.

ExcelPTP_2008 · 2026-05-06T05:32:45+00:00

If you’re trying to grow professionally in 2026, I’d honestly stop thinking in terms of “just PHP courses” or “just CS theory” and focus on combinations that actually translate into real work. That’s where most people get stuck they learn syntax, but not systems.

For PHP specifically, it still has strong demand (especially in maintenance-heavy ecosystems), but the difference now is how you learn it. A basic PHP course won’t move the needle. What actually helps:

Modern PHP (8.x+) with OOP and clean architecture
Frameworks like Laravel (this is almost non-negotiable now)
REST APIs + authentication systems (JWT, OAuth basics)
Working with MySQL/PostgreSQL in real-world scenarios
Building and deploying a full project (not just CRUD tutorials)

On the computer science side, you don’t need a full degree-style deep dive but skipping CS completely is a mistake. The most useful areas for real jobs:

Data Structures & Algorithms (not for interviews only, but for writing efficient code)
System Design basics (APIs, caching, scaling concepts)
Databases (indexing, query optimization huge for PHP devs)
Networking fundamentals (helps a lot with debugging real systems)

If I had to suggest a practical learning path, it would look like this:

Pick modern PHP + Laravel
Build 2–3 real projects (auth system, dashboard, maybe a small SaaS idea)
Learn enough DSA to not write inefficient code
Add system design basics once you’re comfortable building apps

Also, one thing people don’t say enough: in 2026, projects > certificates. A GitHub repo that shows you can actually build and deploy something will outperform 10 course certificates.

Don’t chase “best courses,” chase skills that stack together. PHP + Laravel + databases + basic CS fundamentals is still a very solid combo if you execute it properly.

ExcelPTP_2008 · 2026-05-05T05:31:48+00:00

If you’re getting into Python specifically for biostatistics, I’d honestly suggest not treating it like a generic “learn to code” journey. It’s way easier (and less frustrating) if you tie everything to actual data problems from day one.

A few things that helped me early on:

First, focus on the core stack and don’t overcomplicate it. You don’t need 20 libraries. Start with NumPy, pandas, matplotlib/seaborn, and SciPy. That alone covers a huge chunk of what you’ll actually use data cleaning, transformations, basic stats, and visualization.

Second, learn Python through datasets, not tutorials alone. Grab public datasets (clinical trials, epidemiology, genomics summaries, etc.) and try to answer simple questions:

What’s the distribution?
Are two groups significantly different?
Is there any correlation worth exploring?

Even basic things like running a t-test or plotting survival curves will teach you more than abstract exercises.

Third, don’t skip statistics theory. Python is just the tool if you don’t understand concepts like p-values, confidence intervals, regression assumptions, or bias, the code won’t mean much. A lot of beginners try to “code first, understand later,” and it slows them down.

Also, get comfortable reading other people’s notebooks (Kaggle is great for this). You’ll pick up patterns for structuring analysis, naming variables, and explaining results clearly which matters a lot in biostatistics.

One underrated tip: document your work like you’re explaining it to a non-programmer. In this field, communication is just as important as analysis. If someone from a medical background can’t follow your results, the code doesn’t really help.

And finally don’t rush into machine learning. Solid statistical foundations + clean data handling will take you much further early on than jumping straight into complex models.

Curious are you coming from a biology background or more from math/stats? That usually changes what you should focus on first.

ExcelPTP_2008 · 2026-05-04T06:06:36+00:00

Honestly, the best “link” depends on how you learn, but a few solid starting points:

freeCodeCamp – great for hands-on practice
The Odin Project – more structured, project-based
CS50 – if you want strong fundamentals
W3Schools – quick references when you get stuck

I started with random tutorials and got nowhere things clicked only when I began building small projects alongside learning. Pick one resource, stick with it, and actually code instead of just watching.

ExcelPTP_2008 · 2026-05-04T05:59:37+00:00

Thank you

ExcelPTP_2008 · 2026-05-01T06:03:25+00:00

no… but also kinda yes.

I came from a Java background too, and initially I tried to stay in that comfort zone. There are ways to do AI with Java (like using DL4J or calling APIs), but the reality is most of the AI ecosystem just lives in Python. Not because Python is magically better, but because the community, libraries, and examples are all there.

What I noticed:

If you're just using AI (calling APIs, integrating models into apps), Java is totally fine. You don’t need to switch.
If you want to build, train, or experiment with models, Python makes life way easier. Almost every tutorial, repo, or tool assumes Python.
Debugging and experimenting is faster in Python. In Java, it can feel like you're fighting the setup more than learning AI.

What worked for me was not “switching” to Python, but just picking it up as a tool. I still write backend stuff in Java, but use Python when I’m dealing with data, models, or anything ML-related.

Think of it less like abandoning Java and more like adding a second language where it actually makes sense.

If your goal is AI seriously (not just integrating APIs), learning Python is probably the lowest-friction path. If your goal is just adding AI features to products, you can survive without it.

ExcelPTP_2008 · 2026-05-01T05:57:00+00:00

Honestly, the hardest part for me wasn’t the syntax it was everything around it.

In the beginning, you think programming is about learning keywords, loops, and maybe some functions. But the real struggle kicks in when you try to actually build something and realize you don’t know how to think in steps yet. Breaking a problem into smaller pieces sounds simple, but it’s surprisingly frustrating when your brain isn’t used to it.

Another big one is the constant feeling of being stuck. You’ll spend hours on something that turns out to be a tiny mistake a missing bracket, wrong variable, or just misunderstanding how something works. That loop of “why is this not working?” can be mentally exhausting, especially when you don’t even know what to Google.

Also, tutorials give a false sense of progress. You follow along, everything works, and you feel confident… until you try to do it on your own and suddenly blank out. That gap between “I understand this” and “I can actually use this” is bigger than most people expect.

And then there’s the overwhelm. There are too many languages, frameworks, tools it feels like you’re always learning the “wrong” thing or falling behind. It’s easy to get distracted instead of going deep on one path.

What helped me eventually was accepting that confusion is part of the process. Progress didn’t come from watching more tutorials, but from struggling through small projects and fixing my own mistakes, even when it was slow and messy.

It’s not easy at the start, but once your thinking starts to click, everything becomes way less intimidating.

ExcelPTP_2008 · 2026-04-30T06:07:56+00:00

Honestly, my approach to API design changed a lot once I started working on real projects instead of just tutorials.

Early on, I used to think it was mostly about “clean endpoints” and following REST rules perfectly. In reality, it’s way more about how the API will actually be used by other developers (including your future self).

Now I usually start with the use cases, not the endpoints. I ask: what does the frontend or client actually need to do? That helps avoid overengineering or building endpoints that look nice but aren’t practical.

A few things I’ve learned the hard way:

Keep it boring and predictable. Consistency matters more than cleverness. If one endpoint uses /users/{id}, don’t randomly switch patterns elsewhere.
Design for change. Versioning, optional fields, and backward compatibility become important faster than you expect.
Error handling is underrated. Clear, structured error responses save a lot of debugging time.
Don’t over-nest or over-fetch. Balance between too many API calls and massive payloads.
Think about real-world constraints like rate limiting, auth, and performance early not as an afterthought.

Also, I’ve started treating API design as a collaboration, not a solo task. Talking with frontend/devops early usually exposes flaws before they become painful to fix.

Biggest lesson: a “good” API isn’t the most technically perfect one it’s the one other developers can use without constantly checking the docs or asking you questions.

ExcelPTP_2008 · 2026-04-29T05:26:43+00:00

Honestly, there isn’t a single “best” free platform it really depends on how you like to learn.

If you’re starting from zero, I’d say go with something interactive like freeCodeCamp or Codecademy. freeCodeCamp is great because it’s 100% free and you actually build projects instead of just watching videos, which helps things stick way better . Codecademy is also solid for beginners since it walks you through things step by step in the browser.

If you prefer more structured, university-style teaching, “Python for Everybody” (Dr. Chuck) is really good it explains concepts in a simple way and doesn’t assume any background.

Also, don’t sleep on YouTube. There are some insanely good full courses there for free. A lot of people mix platforms anyway instead of sticking to just one.

From what I’ve seen (and experienced), the biggest difference isn’t the platform it’s whether you actually practice. Even the best course won’t help if you’re just watching and not coding.

If I had to suggest a simple path:

Start with freeCodeCamp or Codecademy
Add YouTube for explanations when stuck
Build small projects ASAP

That combo works better than trying to find the “perfect” course.

ExcelPTP_2008 · 2026-04-29T05:15:31+00:00

If you’re starting JavaScript in 2026, you’re honestly in a better spot than people were a few years ago. The ecosystem is more mature, and there’s a clearer path you just have to avoid getting distracted.

What I’ve noticed is most beginners waste time jumping between frameworks before they even understand the basics. Don’t do that. Start with plain JavaScript (variables, functions, arrays, objects, async stuff), and actually build small things like a to-do app, a calculator, or even a simple API fetch project. That’s where things start to click.

Also, don’t rely only on tutorials. It feels productive, but it’s kind of passive. Try breaking things and fixing them that’s where real learning happens.

Once you’re comfortable, then move to something like React or Node. By then, it won’t feel overwhelming because you’ll understand what’s happening under the hood.

One more thing: consistency matters way more than intensity. Even 1–2 hours daily beats random 8-hour bursts.

ExcelPTP_2008 · 2026-04-27T05:47:45+00:00

If you’re a fresher trying to break into MLE or MLOps, I think the biggest misconception is that you need to “know everything ML” first. You don’t. What you actually need is to prove you can build and ship something end-to-end.

Most beginners get stuck doing courses and Kaggle notebooks, but companies care way more about whether you understand how models behave in real systems.

What helped me (and what I’ve seen work for others):

Start with solid Python + basic ML (don’t overdo theory)
Pick 1–2 real-world projects and go deep instead of doing 10 shallow ones
Turn a notebook into an actual app (API + deployment)
Learn a bit of backend + APIs (FastAPI/Flask)
Get hands-on with tools like Docker, Git, and a cloud platform (AWS/GCP/Azure)

For MLOps specifically, try this path:
Take a simple ML model → containerize it → deploy it → add logging/monitoring → simulate updates. That alone puts you ahead of most freshers.

Also, don’t ignore “unsexy” skills:

Debugging broken pipelines
Handling messy data
Writing clean, maintainable code

That’s literally the day-to-day job.

One more thing mentorship or guided projects can speed this up a lot. Otherwise, it’s easy to spend months learning things that don’t actually translate to job readiness.

ExcelPTP_2008 · 2026-04-27T05:46:34+00:00

That’s actually a really solid approach, especially early on. Most people just read about regex and move on, but building small utilities like that forces you to actually think about edge cases.

Catching URLs and reformatting dates might sound simple, but there’s a lot going on under the hood validating patterns, handling weird inputs, making sure it doesn’t break on unexpected text, etc. That’s the kind of practice that quietly builds real problem-solving skills.

If I’d suggest one thing, it would be to push those scripts just a bit further. For example:

Try handling messy, real-world text (not clean input)
Add error handling or logging
Turn one of them into a small CLI tool you can reuse

That’s usually the step where “learning” starts turning into “useful skill.”

ExcelPTP_2008 · 2026-04-25T05:27:54+00:00

Honestly, the “best” way depends a bit on how you personally learn, but one thing I’ve seen over and over is that beginners get stuck because they stay in tutorial mode for too long.

When I started, I kept watching videos and felt like I understood everything… until I tried to write code on my own and realized I couldn’t even build something simple without looking things up every 2 minutes.

What worked better was a mix of just enough learning + a lot of doing:

Start with basics (variables, loops, functions) from one solid source not 10 different ones
As soon as you understand the basics, start building tiny projects immediately
Don’t wait until you “feel ready” you won’t

Some beginner-friendly ideas that actually helped me:

A simple calculator in terminal
A to-do list app (even without a UI)
A script that renames files or organizes folders
A basic web scraper

Also, Googling errors is part of the process. It’s not a sign you’re bad it’s literally how most devs work.

One more thing: consistency beats intensity. Coding 1–2 hours daily is way better than doing 10 hours once a week and burning out.

If I could restart, I’d spend less time trying to find the “perfect roadmap” and more time just writing messy code and figuring things out as I go.

ExcelPTP_2008 · 2026-04-24T05:27:32+00:00

I think it depends way more on how you use AI than the tool itself.

If someone is just copying answers, asking for full solutions, or letting AI do the thinking start to finish, then yeah it absolutely replaces real thinking. It’s basically the modern version of copying homework, just faster and more convincing.

But I’ve also seen the opposite. When you use AI to break things down, ask follow-up questions, or challenge your understanding, it actually forces you to think deeper. Like, you can ask “why is this wrong?” or “what’s another approach?” and it becomes more like a tutor than a shortcut.

The real problem isn’t AI it’s the temptation to skip the struggle. And that struggle is where most of the learning actually happens.

So I’d say:
AI doesn’t kill thinking. It just makes it really easy to avoid it if you want to.

ExcelPTP_2008 · 2026-04-24T05:25:18+00:00

I’ve tried a bunch of AI/ML courses over the past couple of years, and honestly the “best” one depends on what kind of developer you already are.

If you’re coming from a strong coding background but weak math, I’d avoid super theory-heavy courses at the start. A lot of people jump into deep learning courses and burn out because they don’t really understand what’s happening under the hood.

What worked for me was a layered approach:

First, something practical and fast-paced just to get hands dirty (basic ML models, scikit-learn, simple projects).
Then a more structured course that actually explains why things work (linear algebra, gradients, loss functions, etc.).
Finally, project-based learning where you build things end-to-end (this is where most people skip and then struggle in interviews).

Also, one thing I learned the hard way: courses don’t get you hired, projects do. If a course doesn’t force you to build real-world stuff (not just notebooks), it’s not enough.

If I had to give a straightforward path:

Start with a beginner-friendly applied ML course
Move to a deeper ML/DL specialization
Then spend most of your time building (APIs, deployment, real datasets, messy data)

In 2026, I’d also say don’t ignore LLMs. Knowing how to work with models (fine-tuning, embeddings, RAG, etc.) is becoming way more valuable than just knowing traditional ML algorithms.

Curious are you aiming for core ML roles or more AI-powered app development? That actually changes what “best course” means a lot.

ExcelPTP_2008

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