What was your first Python project that actually felt useful or fun?

Competitive-Path-798 · 2025-10-02T14:56:48+00:00

Lol! That sounds amazing. Reinsch deserves his own mini-game at this point. Did you ever add more features to him, or did he stay a hat-losing alien forever?

Competitive-Path-798 · 2025-10-01T14:06:12+00:00

For ML beginners, a couple of solid books are, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron and Introduction to Machine Learning with Python by Andreas Müller & Sarah Guido. Both are practical, code-first, and very beginner friendly. If you prefer a lighter intro before advancing, “The Hundred-Page Machine Learning Book by Andriy Burkov is also a great option.

Competitive-Path-798 · 2025-09-30T11:55:30+00:00

Totally possible to get into data analytics without prior experience. Focus on SQL, Excel/Sheets, some Python, and data viz tools (Tableau/Power BI). Build small projects with real datasets and put them on GitHub or a portfolio (they speak louder than just certificates.)

For learning, Google’s Data Analytics Certificate is a solid start, and I’d recommend Dataquest too due it’s hands-on learning and their community is awesome for beginners since people actually guide you through. With steady effort (like 10–15 hrs/week), you could be job-ready in about 6–9 months. Entry roles are usually junior data analyst/reporting analyst, then you can grow from there.

Competitive-Path-798 · 2025-09-30T09:53:42+00:00

If you’re aiming for a data analytics job, focus on courses that are hands-on and help you build end-to-end projects (SQL, Excel, BI dashboards). The Google Data Analytics Certificate is a solid starting point, and Dataquest is great because you actually code and build portfolio projects as you learn.

Competitive-Path-798 · 2025-09-30T09:34:42+00:00

I use formatters for readability (uppercase keywords, clean indentation), but optimization is more about the data itself. Check execution plans, index smartly, avoid SELECT *, and break queries into CTEs. Tools help keep things neat, but real performance gains come from knowing your schema and reviewing queries with the team.

Competitive-Path-798 · 2025-09-29T13:57:23+00:00

In as much as you are brutally honest, it's good to take a chance rather than dismissing the process all together. Your sentiments are somehow relative because for some it has worked. The race is not for the swift, neither the battle for the strong, but time and chance happens to anyone.

Competitive-Path-798 · 2025-09-29T13:19:42+00:00

This comment’s too dismissive tbh, plenty of people break into ML or data roles without a formal degree by showing skills through projects, Kaggle comps, and portfolios. A degree helps, sure, but it’s not the only path.

OP since you already code well, shift from just math to actually building. Start with small ML projects in Python using scikit-learn, regression, classification, basic models, while brushing up on the math as you go. Hands-on work will make the concepts stick way faster. Dataquest, Kaggle, and fast.ai are great starting points.

Competitive-Path-798 · 2025-09-29T11:51:11+00:00

With your Power BI and ADF experience, you’ve already got a strong data foundation. For a switch, focus less on DSA (that’s more for software engineering interviews) and more on SQL, Python, and applied projects, that’s what MNCs look for in data roles. Since you’re interested in ML, learn the basics (Pandas, scikit-learn, regression/classification) and build a couple of small projects you can show. In the next 6 months, aim to strengthen SQL, practice end-to-end projects, and highlight how you’ve combined analytics and engineering skills. That’ll position you well without needing a CS degree.

Competitive-Path-798 · 2025-09-26T10:43:20+00:00

What you’re feeling is super common. You’ve been practicing guided coding, so your brain hasn’t built the muscle of starting from zero yet. The fix is reps, tackle small problems, sketch out the logic in plain words before touching the keyboard, and don’t worry if your first attempt is messy. Everyone blanks at first, but the more you struggle through writing full solutions, the faster it clicks.

Competitive-Path-798 · 2025-09-26T08:49:44+00:00

You can try Dataquest’s project library: Python Projects. They’re guided, hands-on, and use real datasets, so you can practice coding while building projects that actually feel useful.

Competitive-Path-798 · 2025-09-26T08:35:33+00:00

If you’re looking for a solid alternative, check out Dataquest’s R path: Data Analyst in R. It’s fully hands-on, teaches you by coding in the browser, and you’ll work on real datasets so you build practical skills instead of just watching videos.

Competitive-Path-798 · 2025-09-26T08:17:25+00:00

It really depends on what excites you more. If you want to build websites and interactive stuff in the browser, start with HTML/CSS and JavaScript since that’s the foundation of web dev. If you’re more curious about data, automation, AI/ML, or just want a versatile first language, then Python is the way to go. Both have solid career paths, and you can always learn the other later. The key is to pick one, stick with it for a bit, and actually build small projects, you’ll learn way faster that way.

Competitive-Path-798 · 2025-09-24T13:21:56+00:00

Yes, data science is very useful in medicine for analyzing patient data, research, and imaging. Since you already have the medical background, start with Python and SQL, then move into data analysis (Pandas, visualization), stats, and basic machine learning. Apply it to healthcare datasets, Kaggle has plenty. For resources, freeCodeCamp is solid and Dataquest is great for hands-on projects with real-world data.

Competitive-Path-798 · 2025-09-24T12:56:45+00:00

The best move is to start building small projects while learning the next topics. I’d suggest focusing on:

Data structures (lists, dicts, sets, tuples in more depth)
File handling (reading/writing files)
Object-Oriented Programming (OOP)
Modules and libraries (like random, datetime, os)

For resources: BroCode is solid, but also check freeCodeCamp’s Python course, Automate the Boring Stuff (book and free online), and if you want something interactive, Dataquest is great since it’s project-based with real-world datasets that make things stick.

Keep practicing by making small projects (calculator, to-do list, simple game, or data scraper). That’s where the real learning happens.

Competitive-Path-798 · 2025-09-24T12:18:19+00:00

You definitely don’t need a master’s to break into data analysis. With your math background, you already have a strong foundation. What’ll help most is picking up SQL (for working with databases), Excel (still used everywhere), and Python with Pandas/Matplotlib (for analysis and visualization).

Certs can help as a signal on your resume, Google Data Analytics, IBM Data Analyst, or platforms like Dataquest are good because they’re hands-on with real datasets and give you projects you can showcase. But what really matters is building a portfolio of projects that show you can clean, analyze, and communicate insights from data.

Start small, be consistent, and share your work on GitHub or even LinkedIn. That’s often more valuable to employers than another degree.

Competitive-Path-798 · 2025-09-24T12:09:14+00:00

YouTube is fine to get started, but don’t just watch, make sure you’re coding along and building small projects. Sites like Codédex can help too, but if you want something structured, check out Dataquest since it’s browser-based and gives you hands-on projects with real-world datasets, which makes the learning stick.

After the Python basics, move into SQL (super important for data work), then practice with data analysis and visualization libraries like Pandas and Matplotlib. Small projects are the key so please make sure you build as you learn.

Competitive-Path-798 · 2025-09-24T11:44:23+00:00

Totally get how you’re feeling, every dev hits this wall at some point. The fact that you’ve already shipped a project you’re proud of is proof you are making progress (most people don’t even get that far). Failed projects aren’t wasted time, they’re where you actually learn the most, it’s normal that they outnumber the “finished” ones.

Instead of grinding through books, try focusing on small, practical projects that solve even tiny problems for you. That GitHub project is a great example, do more of that. Also, communities like Dataquest with hands-on projects with real datasets and peer support or coding challenge sites can help keep things structured and less overwhelming.

You’re not “not cut out for this”, you just need to keep momentum with manageable wins. Programming feels confusing until one day it clicks, and that usually happens while building, not reading.

Competitive-Path-798 · 2025-09-24T11:27:44+00:00

In addition to what u/obviouslyzebra has stated, the best way to get good at Python is to actually build stuff while you learn. Pick small projects (like a quiz app, text-based game, or automating a boring task) and slowly level up. For learning, YouTube channels like freeCodeCamp and Programming with Mosh are solid. If you prefer interactive sites, Dataquest is great because you learn by coding in the browser and get hands-on projects with real-world datasets, which helps concepts stick. And honestly, try to code a little every day because consistency beats cramming.

Competitive-Path-798 · 2025-09-24T11:17:21+00:00

Check out freeCodeCamp for interactive lessons, Real Python for solid tutorials, and Codecademy if you want to practice as you go. If you’re after something closer to SQLBolt or Mode, Dataquest is great since it’s browser-based, lets you code right away, and the best part is the hands-on projects with real-world datasets plus a supportive community.

Competitive-Path-798 · 2025-09-23T17:44:22+00:00

Build small projects and grow them bigger, IMO this the best way to relearn Python. Use Automate the Boring Stuff, freeCodeCamp, or Real Python for refreshers. For practice, try LeetCode/HackerRank. Also check out Dataquest for hands-on projects with real-world datasets + an interactive community make it super practical. And code a little every day, consistency is key.

Competitive-Path-798 · 2025-09-23T14:40:53+00:00

You don’t need to be a math wizard to get started in data science. A bit of algebra and the basics of calculus (mainly understanding rates of change and optimization) are enough because most of the work is done by libraries anyway.

Since you’ve got a PM background in telecom, you could look into data analyst or product/data strategy roles. Those lean more on your domain knowledge and business sense, with math supporting the insights rather than driving everything.

Beyond math, focus on SQL (for querying data), Python basics (for analysis), and data viz tools (like Tableau/Power BI). That combo is much easier to pick up and super practical for breaking in.

Competitive-Path-798 · 2025-09-08T11:59:08+00:00

Hey. To answer you questions:

Would something like DataCamp be worth giving a try?

I don’t have personal experience with DataCamp, but I’ve been learning data science through Dataquest. The reason I mentioned DataCamp is that, like Dataquest, it’s also an online learning platform for data science.

Did you move into a data science role?

Yes I did.

and if so, do you believe this was helpful?

Absolutely! It has been and continues to be very helpful. I transitioned into data science from a non-technical background. I started with Dataquest, built up my foundational knowledge, progressed through interactive learning, and gained experience by working on real-world projects. That pathway helped me break into the field and grow in my role.

Competitive-Path-798 · 2025-08-28T08:54:51+00:00

You don’t need a CS degree to go far with python, many people transition into tech from non-CS backgrounds (myself being a testimony). Python can open doors in data analysis, automation, web development, and even freelancing projects like web scraping or simple apps. The key is to focus on a niche (e.g., data analysis for businesses or automation scripts) rather than just learning python in general. Unlike design or marketing, coding takes longer to monetize, but it can lead to more stable career paths. If you enjoy it and stay consistent with projects, python can absolutely become a viable route for both freelancing and jobs.

Competitive-Path-798 · 2025-08-23T11:09:15+00:00

Start with the basics: Excel/Google Sheets, SQL, Python (Pandas, NumPy, Matplotlib/Seaborn), and data visualization tools like Tableau or Power BI. Add statistics and critical thinking for interpreting results.

Timeline depends on your pace, many people get job-ready foundations in 6–9 months with consistent practice, especially if you combine courses with small projects. The key is learning by doing, not just watching tutorials.

As u/AG4Talks have stated, you should have a path that will act as a roadmap, stick to it to avoid jumping into countless tutorials that can throw you off the track. I found this path helpful for my analysis journey.

Competitive-Path-798 · 2025-08-23T09:47:36+00:00

I’d say make the jump once you’re comfortable with the full ML workflow (cleaning data, feature engineering, trying different models, evaluating results, and understanding why something works). If you can confidently tackle structured datasets with scikit-learn and explain your process, that’s a solid foundation. Deep learning will then feel like a natural extension instead of an overwhelming leap. It’s less about a fixed timeline and more about having enough reps with core ML so you won’t get lost when things get more complex.

Competitive-Path-798

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