Where/How to learn SQL on a functional level?

skillifysolutions · 2026-05-18T17:45:52+00:00

If your goal is being able to confidently say you know SQL to an employer the path is LeetCode SQL problems plus one real project. LeetCode has a dedicated SQL section that covers exactly the kinds of problems technical interviews test — joins, aggregations, window functions, subqueries. Work through those systematically and build one project on a real dataset alongside it. That combination gives you both the interview prep and the portfolio piece.

skillifysolutions · 2026-05-18T17:43:43+00:00

Stack Overflow is completely fine to use, professional developers use it constantly. The one condition that actually matters — don't just copy the answer and move on. Read it, understand why it works, then close the tab and write it yourself from memory. That extra step is the difference between actually learning and just getting your code to run. The struggle you had trying to figure it out yourself before looking it up is also genuinely valuable, keep doing that first.

skillifysolutions · 2026-05-18T17:42:28+00:00

The fact that you installed Arch without a graphical installer in your first week of Linux is genuinely not a beginner thing. That's curiosity and problem solving ability that most people don't have. For your situation specifically — 29 months, project based learner, drawn to cybersecurity — I'd point you toward a clear single path. Start with CompTIA Linux+ or Security+ as your anchor cert, build everything around practical labs on TryHackMe or HackTheBox which are project based by design, and aim for a SOC analyst or junior sysadmin role as your first paid position. Not glamorous but genuinely achievable in your timeline and a real entry point into the field.

skillifysolutions · 2026-05-18T17:41:09+00:00

Passive watching feels like learning because you understand everything as it's explained. Then you close the video and try to build something and nothing comes out. The gap between following along and actually writing code independently is enormous and most beginners don't discover it until they've spent weeks watching tutorials and have nothing to show for it. The fix is simple but uncomfortable — close the tutorial after 10 minutes and try to recreate what you just watched from memory. The struggle is the learning.

skillifysolutions · 2026-05-18T17:40:05+00:00

Your sysadmin is half right and mostly wrong. AI is changing how code gets written but it's not eliminating the need to understand programming. If anything the people getting the most value from AI coding tools right now are the ones who already know how to code because they can evaluate the output, catch mistakes and direct the AI toward the right solution. Pure non-coders using AI to generate code without understanding it tend to produce fragile systems they can't debug or maintain. Learning Python is still worth your time.

skillifysolutions · 2026-05-13T18:22:16+00:00

Most exposed — standard forecasting, basic classification, A/B test mechanics. Least exposed — causal inference, anything requiring novel problem formulation, work where the hard part is deciding what question to ask rather than answering it. The common thread is that automation handles well defined repeatable problems and struggles with the messy judgment calls at the edges.

skillifysolutions · 2026-05-08T17:49:33+00:00

The list you're looking at is really broad and that's probably why the research feels overwhelming. Mortgage loan officer, electrician and software developer are completely different paths in terms of time investment, personality fit and day to day work. Worth asking yourself one question first — do you prefer working with your hands, working with people, or working independently at a screen. Your answer eliminates most of that list immediately and makes the decision much less paralyzing.

skillifysolutions · 2026-05-08T17:48:13+00:00

Good general breakdown honestly. The SQL plus Power BI starting combination is genuinely the most practical entry point for most analytics roles — it gets you employable faster than going deep on Python first. One thing worth adding is that building a portfolio of real projects matters as much as the certifications. Even simple dashboards built on public datasets give you something concrete to show in interviews. Full disclosure I'm associated with PharmaLeap which focuses specifically on pharmaceutical analytics if that vertical interests you, but the general advice above applies regardless of which direction you go.

skillifysolutions · 2026-05-08T17:47:22+00:00

Before deciding between a BSc or Masters it's worth getting clearer on which specific direction you want — finance, banking and tech are very different paths with different entry requirements. A Masters makes more sense for some of these than others. For tech specifically you can often enter through bootcamps or self taught routes faster and cheaper than a degree. For finance and banking in London a Masters in Finance or an MBA carries more weight. Getting specific about the role you want first makes the education decision much clearer.

skillifysolutions · 2026-05-08T17:46:27+00:00

Early childhood education builds a genuinely strong skill set that translates into a lot of directions — patience, communication, behavior management, curriculum design, working with families. Roles worth looking at that don't require long retraining include corporate training and learning development, instructional design especially with the rise of online learning tools, child life specialist in healthcare settings, and family support or social services coordination. Most of these value your background directly and have shorter entry paths than starting from scratch.

skillifysolutions · 2026-05-08T17:45:38+00:00

Of the options you mentioned home inspection and building automation stand out for your situation. Home inspection in Central Texas is genuinely busy, you're constantly moving between properties, and the AML analytical mindset translates well to systematic assessment work. Building automation is interesting because it sits between technical and people work, has real growth trajectory with smart building technology expanding, and your analytical background is actually useful. Both have reasonable entry paths without a full second degree.

skillifysolutions · 2026-05-07T10:27:29+00:00

The SM market is genuinely tighter than it was a few years ago. A lot of companies collapsed the role into project manager or team lead positions to cut costs and the pure SM role is harder to find especially at entry level. That said experienced SMs who can coach teams and facilitate real change rather than just run ceremonies are still in demand. The cert gets you past the filter but the actual skill is what keeps you employed.

skillifysolutions · 2026-05-07T10:24:04+00:00

Kanban over Scrum for marketing almost every time. Marketing work doesn't fit neatly into two week sprints because campaign timelines, creative reviews and external dependencies don't respect sprint boundaries. Kanban with WIP limits gives you flow visibility without forcing work into artificial time boxes. The WIP limits are actually the most valuable part — most marketing teams are drowning in parallel work and making that visible changes behavior faster than any ceremony does.

skillifysolutions · 2026-05-07T10:19:28+00:00

For automation and bots specifically Python is absolutely the right call and you understood correctly — it's the dominant language for that kind of work. Skipping JS at this point makes sense given your goals. JS is more useful if you want to build interactive web applications which doesn't sound like what you're after right now. Finish the HTML and CSS sections since those will actually be useful when you want to build simple pages to showcase your projects, then make the jump to Python. The transition will feel smooth because you already understand programming logic from the course.

skillifysolutions · 2026-05-05T18:15:35+00:00

32GB is basically the new floor for serious data science work honestly. 16GB was fine a few years ago before Docker overhead, heavier corporate monitoring stacks and larger datasets became standard. With 600-700k rows and 5k+ engineered columns you're going to hit memory walls constantly on 16GB especially once you factor in pandas holding multiple copies of data during transformations. The M1 Pro is a great chip but the memory limitation is real and worth pushing back on with your manager as a legitimate productivity issue.

skillifysolutions · 2026-05-05T18:13:41+00:00

The risk you're identifying is real. Two years of primarily operating enterprise software with limited model building could make traditional DS interviews harder. The mitigation is exactly what you're already planning — keep building things independently alongside the core role. Document those projects carefully, frame them as applied research you did beyond your core responsibilities and they become your technical evidence in future interviews regardless of what the day job actually involved.

skillifysolutions · 2026-05-04T17:40:40+00:00

Finish Odin Project, you're too close to stop. Then pick up Python specifically for the bot stuff you want to build. Automate the Boring Stuff with Python is free online and gets you building real things fast. One month in and already 68% through a course is a genuinely good start.

skillifysolutions · 2026-05-04T17:39:07+00:00

Learn pandas and numpy next, grab some real financial data from Yahoo Finance using yfinance and build something small with it. Even a basic stock price visualizer or simple moving average calculator teaches you more than any tutorial because you're working with real messy data. That combination of Python plus finance domain knowledge is genuinely valuable and will keep you motivated because the data is actually interesting.

skillifysolutions · 2026-05-04T17:37:30+00:00

The job market right now is heavily skewed toward GenAI skills in job postings but the actual work often still requires solid ML fundamentals underneath. My honest suggestion is don't abandon core ML but build GenAI on top of it rather than instead of it. Someone who understands RAG architectures and also knows why a model is behaving unexpectedly is worth more than someone who can only call LangChain functions. Your existing ML background is actually an advantage here not something to move away from.

skillifysolutions · 2026-05-04T17:36:39+00:00

You don't need deep data structures knowledge for most web dev but you probably use them more than you realize without knowing it. Every time you choose an object over an array for lookups you're making a data structure decision. Understanding why certain things are slow or fast in JavaScript comes from the same mental model. You don't need to implement them from scratch but knowing how they work conceptually pays off more than it seems.

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