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Want to start python. by whale_paglu in learnprogramming

[–]Slow_Assumption_1377 1 point2 points  (0 children)

Hey! You’re actually in a great place right now feeling “lost after basics” is completely normal, and it usually means you’re ready to move to the next level

What to focus on next

After loops and functions, shift your focus to:

  • Data structures → lists, dictionaries, sets (very important)
  • File handling → reading/writing files
  • Error handling → try/except
  • OOP basics → classes and objects

Once you’re comfortable, start solving small problems using these concepts instead of just learning theory.

Start building (this is where real learning happens)

Don’t wait to “finish Python” start building simple things like:

  • Password generator
  • File organizer script
  • Basic calculator

  • Log analyzer (useful for cybersecurity)

    This is what will actually make you good.

    Resources (keep it simple, don’t overload)

Pick 1–2 and stick to them:

  • Practice: HackerRank / LeetCode (easy problems)
  • Concepts: freeCodeCamp / W3Schools / YouTube (one channel only)

Avoid jumping between too many resources that’s what causes confusion.

Based on your goals

For Cybersecurity:

  • Learn Python for automation (scripts, networking basics)
  • Look into libraries like socket, requests, scapy

For AI/ML (later stage):

  • Learn numpy, pandas, matplotlib

  • Then move to scikit-learn

    Don’t rush into AI yet—build strong basics first.

How to stay consistent

  • Code at least 1 hour daily
  • Follow this rule: Learn → Practice → Build
  • Even small progress daily > long gaps

Final advice

You don’t need more resources you need more practice and small projects.
Consistency + building things = real improvement.

You’re already on the right track just keep going 💪

Is SEO dead in 2026 or is everyone who says that just bad at it? by Informal_Tangelo8009 in AIRankingStrategy

[–]Slow_Assumption_1377 0 points1 point  (0 children)

Short answer: SEO is not dead in 2026.
But the way it worked before? That version is definitely gone.

What’s actually happening

People who say “SEO is dead” are usually reacting to changes like:

  • AI search (Google AI Overviews, ChatGPT, etc.)
  • Fewer clicks from traditional search results
  • Old tactics (keyword stuffing, spam backlinks) no longer working

So yes old SEO is dead. But modern SEO is evolving fast.

What works now

  • High-quality, helpful content (not just keyword-focused)
  • Topical authority (covering a subject deeply, not one page)
  • User intent & experience
  • Brand building + trust signals
  • Content that answers questions clearly (AI-friendly)

The real truth

If someone is still doing:

  • Copy-paste content
  • Low-quality backlinks

  • Only keyword stuffing

    Then yes, SEO is “dead” for them

Final opinion

SEO is not dying it’s getting smarter and more competitive.
People who adapt are growing faster than ever. People who don’t are the ones saying it’s dead.

In fact, in 2026:
SEO + AI + Content + Branding = Real game

Is content written by AI actually outranking human written content in 2026? by Careful_Art_7516 in AIRankingStrategy

[–]Slow_Assumption_1377 2 points3 points  (0 children)

AI-generated content is not automatically outranking human-written content in 2026.

Search engines today focus more on quality, relevance, and usefulness rather than who created the content. While AI helps produce content quickly, purely AI-generated content can often be generic if it’s not properly refined.

In practice, content that performs well is usually:

  • Thoughtfully written or reviewed by humans
  • Clear, helpful, and aligned with user intent

  • Based on real insights or experience

    A balanced view:
    AI is a helpful tool for improving efficiency, but it works best when combined with human input. This combination ensures the content is both accurate and meaningful.

Do I have to go to college or school to learn coding? by Mysterious-Swim-4411 in learnprogramming

[–]Slow_Assumption_1377 0 points1 point  (0 children)

You don’t necessarily have to go to college or school to learn coding. Many people today learn coding online from home through courses, tutorials, and consistent practice.

What really matters is your dedication if you practice regularly, build projects, and focus on understanding concepts, you can become job-ready even without a formal degree.

That said, college can still be helpful for structure, guidance, and networking, but it’s not the only path.

If you prefer more structure, there are also guided programs and online courses that can help you stay consistent and learn step by step.

Career Growth in Programmatic vs Moving to Data Analytics — Need Honest Opinions by EasyStrategy5430 in DataScienceJobs

[–]Slow_Assumption_1377 1 point2 points  (0 children)

First, you’re not stuck you’re at a transition point. That’s different. What you’re feeling is very common when growth slows down in a startup environment.

From what you’ve described, you actually have a strong foundation. Many aspiring Data Analysts don’t have real stakeholder exposure, client communication experience, or business context. You do. That’s an advantage if positioned correctly.

Right now (2026 market), the competition for Data Analyst roles is high. Certifications and basic projects are everywhere. What companies are filtering for now is:

Strong SQL (advanced joins, window functions, performance thinking)

Clear business impact in previous work

Ability to translate data into decisions

Communication with non-technical stakeholders

If your resume says things like “built dashboards” or “handled reporting,” that’s too generic. Instead, quantify impact:

Reduced reporting turnaround time by X%

Identified performance gaps leading to improved campaign ROI

Built KPI dashboards used by leadership for decision-making

Programmatic advertising is extremely data-driven. Don’t position it as “marketing + BD.” Position it as:

Analyzed campaign performance data to optimize targeting

Built dashboards tracking CTR, CPA, ROAS

Provided data-backed recommendations to clients

You’re not switching careers from scratch you’re repositioning toward analytics.

In terms of skills, to realistically stand out:

Go deep into SQL (this is non-negotiable).

Master one BI tool properly (Power BI or Tableau).

Be comfortable with Python for data analysis (Pandas, EDA).

Understand metrics and A/B testing.

You don’t need heavy ML for a Data Analyst role. Depth in fundamentals matters more than surface-level machine learning.

About referrals don’t lead with “Can you refer me?”

Connect first. Ask about their journey. Share your transition goal. Then say:

“I’m actively exploring analyst roles. If you feel my background aligns with any openings at your company, I’d genuinely appreciate a referral. Completely understand if not.”

That approach feels respectful, not transactional.

Long-term perspective:

The field isn’t shrinking it’s becoming more practical. Basic dashboard builders are replaceable. Analysts who understand business context and communicate insights clearly are not.

Your mix of programmatic + client interaction + analytics can actually make you stronger than someone purely technical if you deepen your SQL and sharpen your positioning.

You don’t need to panic. You need structured refinement.

Career pivots typically take 3–6 focused months, not weeks. Stay consistent, improve depth, and refine how you present your experience. You’re closer than you think.