She could build an end-to-end scikit-learn Pipeline in her sleep.

Six months into her first job, she’d used it exactly once.

“I feel like I forgot everything.”

She hadn’t forgotten anything. The stack just changed the moment she stopped interviewing.

The Python libraries you learn for interviews are not the libraries you use at work.

Not because interviews are outdated. Because they’re testing for something different.

Interviews test one thing:

Can you build the ideal version?

Work rewards a different one:

Can you make the messy version work with what’s already there?

The interview stack is clean. scikit-learn Pipelines. PyTorch model classes. SQLAlchemy. dbt. MLflow. Every library used the way its documentation intended.

The work stack is whatever the team had when you joined. pandas doing eighty percent of the load. A utils.py file three engineers ago wrote. One SQL client. A script called run.py that nobody has time to refactor.

The library the team already uses will always beat the one you’re better at.

Three things nobody tells juniors about this gap:

1. Ninety percent of your real value lives in pandas, SQL, and knowing where to look. The rest is optional most weeks.

2. Your fancy stack is what you build to, not what you build with. You earn the room to change how the team works before you use it.

3. Feeling behind because you’re “only using pandas” means you’ve misread what work rewards. You’re not behind. You’re doing the job.

Interview prep isn’t wasted, it buys you the interview.

But once you’re inside, the person who ships in the ugly stack the team already trusts will always be more valuable than the one who knows a prettier one.

Interviews reward the ideal. Work rewards the workable.

Learn both. Use each in the room it belongs in.

— Josep

Are you still here? 🧐

👉🏻 I want this newsletter to be useful, so please let me know your feedback!

Before you go, tap the 💚 and the restack buttons at the bottom of this email to show your support, it really helps and means a lot!

Which library did you spend the most time learning for interviews and use the least at work? Please let me know👇🏻

I have a bunch of projects on GitHub. Is that my portfolio?

Short answer: no.

A GitHub is a repository. A portfolio is a story.

Two different objects. Two different audiences. Two different jobs.

GitHub is for the person who wants to read your code. That’s a tiny audience. Usually another engineer. Usually already inside your team.

A portfolio is for the person who doesn’t want to read your code.

And that’s the audience that actually decides whether you get hired, promoted, or brought into a room. Hiring managers. Product leads. Recruiters. Clients.

They don’t open notebooks. They don’t scroll commits.

They look for three things, in this order:

1. What problem did you solve

2. What decision did it change

3. What would you do differently now

None of that lives on GitHub.

Your GitHub has the raw material. A portfolio has the argument.

The gap between “I built things” and “I can show you what they mean” is the gap between a career that stalls and one that compounds.

Most data people are stuck on the wrong side of it.

Not because they haven’t done the work. Because they never translated it.

Next week I’ll break down why most data portfolios still fail the moment they land in front of a hiring manager. And what the ones that actually convert do differently.

For today, open your GitHub.

Pick one project.

Write two sentences about what decision it would have changed if the results had gone the other way.

That’s the seed of a portfolio.

Code proves you did the work. A portfolio proves you understood it.

— Josep

Are you still here? 🧐

👉🏻 I want this newsletter to be useful, so please let me know your feedback!

Before you go, tap the 💚 and the restack buttons at the bottom of this email to show your support, it really helps and means a lot!

What's one project sitting on your GitHub right now that deserves a portfolio page? Reply and tell me the one sentence you'd use to open it. 👇🏻

Not writer’s block. Something worse.

The feeling that whatever I wrote wouldn’t be worth anyone’s time.

I published anyway. You’re reading it now.

That’s what seven weeks of showing up actually looks like from the inside. Not a streak. Not momentum. A decision you make again every week, usually under so…

Read more

My instinct was to separate everything cleanly. One client on Monday and Tuesday. Another on Wednesday and Thursday. Fridays for admin, proposals, the business side of things.

Three clients. Five days. Neat boxes.

It worked for about a month.

Then a client needed something on a Thursday. An…

Read more

I had never taught at a university before.

No formal teaching experience. No academic track record.

Just the diagrams.

They’d seen the work, decided I understood something worth teaching, and reached out.

I said yes. Went all in. And it became one of the…

Read more

Nobody asked about the model.

They asked: “What does this mean for us next quarter?”

I didn’t have that answer ready.

That’s not a modeling problem.

That’s a communication problem.

Most data education teaches you to build correctly.

Almost none of it teaches you to explain what you bui…

Read more

Watched the videos. Did the math. Understood the intuition behind every update step.

Then I sat in front of a real dataset. Messy, incomplete, and half-documented.

I had no idea what to do first.

That’s the gap nobody talks about.

Learning data science is something you can measure.

Courses completed. Concep…

Read more

A new research project.

A new side concept I never shipped.

A new approach to content I mapped out in a notebook and never opened again.

Each one felt like momentum. Each one was actually a reset.

This is what happens without a system: you don’t stop working.

You just keep star…

Read more

Not a planned break.

Not a strategic pause.

I just stopped.

I had just moved from Barcelona to Rotterdam. Took on a full-time European research project. And somewhere between the relocation boxes and the new job, databites.tech became one more thing on a list that was already too long.

So it fell off the list.

Here’s what I …

Read more

Josep here, back with your weekly bite of career insights and encouragement ✨

A quick gut-check:

When you picture ChatGPT, what’s the first image that pops up?
Someone cranking out SQL? Debugging Python? Auto-drafting emails?

That was my picture too, until I dug into a new OpenAI study covering hundreds of millions of users and billions of messages.

The data flipped my mental model.

🧭 In today’s issue:

• What 700M+ people actually do with ChatGPT (fast facts you can use)

• 3 implications for your data career (beyond “code faster”)

• Copy-paste prompt formulas + a context checklist to make your outputs land

Stick with me for ~5 minutes; this one can upgrade how you work starting today. ✨

Let’s dive in! 👇🏻

The surprising reality

From May 2024 → June 2025, usage exploded.

Now there are more than 2.5B messages/day (!!!).

But here’s the twist:

• 3 buckets dominate (~80%):
  Practical Guidance, Seeking Information, Writing.

• Coding is small. ~4.2% of all messages (Data Analysis ~0.4%).

• At work, writing wins. ~40% of work messages are writing/editing/clarifying text. Two-thirds of that is editing your text, not generating from scratch.

• Intent matters: ~49% Asking, 40% Doing, 11% Expressing.
  “Asking” is growing faster and is rated higher quality than “Doing.”

• Most usage isn’t work. Non-work messages grew from 53% to ~73%.

  

• Who’s using it? Nearly half of adult messages come from <26 years old;
  usage is surging in lower-income countries; the early gender gap has largely closed.

Translation: People aren’t just “getting AI to do tasks.”
They’re using it to think, decide, and write more clearly.

Why this matters for you (data folks)

It’s tempting to think of AI purely as a “doer”: something that can code faster, write boilerplate reports, or automate workflows.

But the reality is that people use it most for thinking and communication support.

And that should make us pause.

Because the biggest opportunities for us as data professionals aren’t just in making ChatGPT write SQL faster. They’re in using it as a partner to sharpen how we think, ask questions, and explain context.

• Leverage = writing + context
  Dashboards rarely fail because of SQL. They fail because they don’t say what it means for a decision. LLMs excel at

  • (a) sharpening your thinking

  • (b) editing your words so stakeholders “get it.”

• “Soft” use ≠ soft impact
  The next generation of workers arrives expecting AI at every step, scoping, brainstorming, drafting, revising, and explaining.

  If you only use AI for code, you’re leaving most of the value on the table.

• Edge beats execution
  If only ~4% of usage is coding, “I code with AI” won’t differentiate you.

  Owning the problem framing, asking better questions, and adding context will.

How you can use this insight today

Use these as is. They’ll lift the quality of outputs immediately.

1) Decision Framing (for analysis, memos, or dashboards)

You are my writing co-pilot. 
- Context: [business model], [audience], [decision & timeframe], [constraints], [data caveats].
- Goal: Draft a concise brief that answers: 
    1) What changed? 
    2) Why? 
    3) So what? 
    4) Now what? (next actions + owner + when)
- Tone: Clear, non-jargon. 
Max 250 words. Add a 1-line TL;DR.

2) Stakeholder Translation (tech → business)

You are a translator for non-technical execs. 
- Input: [technical finding/SQL/Python output].
- Audience: [role, e.g., VP Sales]. Decision needed: [X by Y].
- Rewrite: 
    1) 3 bullets: impact on revenue/cost/risk 
    2) Confidence & caveats 
    3) A single recommended action with owner & deadline

3) Editing Your Draft (LLM’s #1 work use)

You are my editor. 
- Audience: [e.g., CX leadership]. 
- Objective: [approve plan].

Revise this draft for clarity, brevity, and flow. 
Replace jargon, keep numbers, keep nuance. 
Return: 
    1) improved text 
    2) 3 notes on what you changed and why

4) Analysis Planning (prevent dead dashboards)

You are a senior analytics lead. 
- Problem: [business question]. 
- Metric(s): [define]. 
- Decisions: [which levers may change].
Design an analysis plan: hypotheses, required data, cuts/segments, pitfalls, success criteria, and the “decision table” we’ll hand to stakeholders.

5) “Asking” beats “Doing” (quality boost)

Before doing anything, ask me 5 clarifying questions that would change the output, then propose 2 alternative approaches and their trade-offs. 
Goal: best possible answer for [audience/decision].

The Context Checklist (paste next to your IDE)

Before you ask an LLM to write, code, or summarize, check these:

• Audience: who is this for (role, context, reading level)?

• Decision: what choice will this enable, and by when?

• Metric(s): which KPI matters, what’s “good vs bad”?

• Constraints: data limitations, guardrails, must-haves, off-limits.

• Caveats: sampling bias, freshness issues, “unknowns.”

• Format: bullets vs memo, 1-pager vs slide, TL;DR required?

If you can’t answer these, your output will look polished but won’t move a decision.

Common traps to avoid

• Prompting with verbs, not context. (“Write SQL for churn” → meh.)

• Pretty output, no decision. If the reader can’t act, you didn’t finish.

• Forgetting the audience. Exec ≠ PM ≠ engineer. Rewrite per role.

• Over-trusting summaries. Always add caveats + confidence.

TL;DR you can share

Most people use ChatGPT to think and write, not just to code.
For data pros, the win is context → better decisions → impact.
Lead with Asking (clarify), then Doing (draft), finish with Editing (for audience).

Your turn

How do you mostly use ChatGPT right now — Asking, Doing, or Editing?
Hit reply with one example and the audience you wrote for. I’ll feature a few (anonymously) in next week’s Note.

Stay curious,

— Josep

P.S. If this helped, forward it to a teammate who’s still using LLMs only for code. They’re missing 80% of the upside.

Are you still here? 🧐

👉🏻 I want this newsletter to be useful, so please let me know your feedback!

Before you go, tap the 💚 and the restack buttons at the bottom of this email to show your support—it really helps and means a lot!

Any doubt? Let’s start a conversation! 👇🏻

Josep here, back with your weekly bite of career insights and encouragement ✨

Last week, we unpacked how to position ourselves for luck.

This week, let me ask you this:
Have you ever built a chart that technically made sense… but no one seemed to get it?
You showed the data, but it didn’t land.

It didn’t inspire action.

It didn’t spark con…

Read more

Josep here, back with your weekly bite of career insights and encouragement ✨

Last week, we unpacked why mindset beats raw talent — and how the smallest shift in belief can unlock big transformation.

This week, we go even deeper.

🌀 What do you do when the world is changing faster than your plans can keep up? You improve your position — …

Read more

Josep here, back with your weekly bite of career insights and encouragement ✨

Last week, we explored how mindset, not talent, shapes your future, and how a small shift in belief can unlock big growth.

This week, we go even deeper.

🌀 What happens when the world keeps changing faster than your plans can keep up?

🧭 In today’s issue:

• Why resilience is the most underrated “hard” skill

• How I navigated uncertainty from junior positions to EU freelancing

• 3 practical ways to become more adaptable starting today

Stick with me for 5 minutes — it will be worth it, I promise! ✨

Let’s dive in! 👇🏻

‼️ The fifth module, Data Science Basics, of the introductory course to Data Science is now live on LearnDs.ai. Just go check it out now!

𝗪𝗵𝘆 𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗜𝘀 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗛𝗮𝗿𝗱 𝗦𝗸𝗶𝗹𝗹

According to the Future of Jobs Report 2025, the skills employers value most are:

1. Analytical thinking

2. Resilience, flexibility, and agility

3. Leadership and social influence

Yes — resilience ranks just under analytical thinking.

That’s not just soft.

That’s strategic.

𝗧𝗵𝗲 𝗦𝘁𝗿𝗲𝘀𝘀 𝗧𝗲𝘀𝘁 𝗼𝗳 𝗖𝗵𝗮𝗻𝗴𝗲

I’ve had a few identity shifts over the years:

🚀 From working as a junior data scientist at a startup...

💻 To becoming an analytics engineer for the government...

📄 To picking up technical writing as a side hustle...

🎓 To teaching as a university professor...

👨🏻‍💻 To launching a solo data consulting career...

🇪🇺 To navigating unpredictable timelines in EU projects...

Each move came with a fog of uncertainty: no roadmaps, no guarantees.

But looking back, the only thing that carried me through was the ability to bend, not break.

That’s what resilience is:

mental elasticity.

So What Is Resilience, Really?

Resilience isn’t about being tough.

It’s about being adaptable:

• Staying calm when the plan falls apart.

• Pivoting quickly when new tools or expectations arise.

• Bouncing back when feedback stings or a project fails.

It’s the skill that lets you stay in the game long enough to grow into your next opportunity.

How To Train Resilience (Yes, Like a Muscle)

1. Reframe setbacks as feedback
   Got rejected? Didn’t get the role?

   Then you gotta ask: What can I learn from this?

2. Build in flexibility
   Set goals, but leave room for plan B (and C).

   Careers aren’t linear.

3. Zoom out regularly
   When you're in the weeds, everything feels bigger than it is.

   Weekly reflection = long-term clarity.

This Week’s Challenge

Think of a situation that recently shook your confidence.
Now ask yourself:

What would a resilient version of me do here?

Write down three small actions you could take to move forward anyway.

One baby step is all it takes to rebuild momentum.

‼️ The fifth module, Data Science Basics, of the introductory course to Data Science is now live on LearnDs.ai. Just go check it out now!

Dive in and explore it today!

PS: You’re allowed to…

• Be unsure — and still show up.

• Feel overwhelmed — and still take action.

• Fall short — and still be proud of your effort.

• Pivot — and still stay true to your mission.

• Rest — and still call it progress.

Growth doesn’t mean constant hustle.
It means adapting with intention. That’s resilience. 💪🏻

Subscribe now

Are you still here? 🧐

👉🏻 I want this newsletter to be useful, so please let me know your feedback!

Before you go, tap the 💚 and the restack buttons at the bottom of this email to show your support—it really helps and means a lot!

Any doubt? Let’s start a conversation! 👇🏻

Want to get more of my content? 🙋🏻‍♂️

Reach me on:

• LinkedIn, X (Twitter), or Threads to get daily posts about Data Science.

• My Medium Blog to learn more about Data Science, Machine Learning, and AI.

• Just email me at rfeers@gmail.com for any inquiries or to ask for help! 🤓

Remember now that DataBites has an official X (Twitter) account and LinkedIn page. Follow us there to stay updated and help spread the word! 🙌🏻

See you next Tuesday!

Best,

-Josep

Josep here, back with your weekly bite of career insights and encouragement ✨

Last week, we explored how soft skills like emotional intelligence are becoming the new superpowers in an automated world.

This week, we go deeper.
🌀 Beneath every skill—technical or human—lies the mindset that fuels it.

Read more

Josep here, back with your weekly bite of career insights and encouragement ✨

Last week, we explored how showing up—even when uninspired—can unlock your best creative work.

This week, we’re shifting focus to a different kind of edge: the human one.
🌀 our most valuable skills might not be technical—they’re human.

🧭 In today’s issue:

• Why soft skills are now the real differentiator in your career

• The emotional intelligence gap—and how to close it

• 5 practical ways to level up your people skills starting today

Stick with me for 5 minutes — it will be worth it, I promise! ✨

Let’s dive in! 👇🏻

‼️ The third module, Python Basics of the introductory course to Data Science is now live on LearnDs.ai. Just go check it out now!

The Human Edge in an Automated World

Hard Skills Get You In. Soft Skills Keep You There.

For years, we’ve been told that mastering technical skills—like coding, data analysis, or engineering—is the key to success.

And yes, they matter.

But here’s the truth:

In today’s workplace, soft skills are the new hard skills.

That’s not just a catchy phrase, it’s a reality echoed by business leaders worldwide.

LinkedIn’s 2024 survey shows that 9 out of 10 global executives agree soft skills—also known as “human” or “durable” skills—are more important than ever.

This highlights the growing value placed on uniquely human abilities like communication, adaptability, and emotional intelligence as key differentiators in today’s workforce.

Why?

Because in a world where AI and automation are handling more technical tasks, the uniquely human abilities—like communication, empathy, and adaptability—are what set you apart.

Why Soft Skills Matter More Than Ever

1. The Emotional Intelligence Gap

Emotional Intelligence (EQ) is the ability to understand and manage your own emotions, and to recognize and influence the feelings of others.

It's becoming increasingly vital in leadership and team dynamics.

Yet, many professionals struggle with EQ. They might excel technically but falter when it comes to building relationships, managing stress, or navigating conflicts.

The good news?

EQ can be developed. It starts with self-awareness and a commitment to growth.

Subscribe now

2. Adaptability and Resilience: Your Career's Secret Weapon

The modern workplace never stands still—new technologies, shifting markets, reorganizations. In this constant flux, success belongs to those who adapt swiftly and stay grounded under pressure.

The challenge? Resistance to change breeds stress, stagnation, and missed opportunities.
The mindset shift? See change not as a threat, but as a catalyst for growth.

Learn fast, stay flexible, and recover stronger.

Adaptability and resilience aren’t just nice-to-have—they’re survival skills.

To build them:

• Embrace new challenges as growth opportunities

• Stay open to feedback and continuous learning

• Strengthen your ability to bounce back from setbacks

Those who adapt don’t just survive—they lead.

Subscribe now

3. The Rise of Automation and AI

As automation and AI continue to accelerate, many tasks that once required technical expertise are now being handled by machines.

This doesn’t make hard skills obsolete—but it does shift the spotlight to qualities technology can’t replicate.

Human traits like creativity, collaboration, and problem-solving are becoming your most valuable edge.

The challenge?

Professionals who rely solely on technical know-how risk being left behind, facing stagnation, or struggling to adapt in an evolving job market.

The New Balance: Hard Skills Get You Hired, Soft Skills Get You Promoted

Hard skills are essential, especially in technical fields.

But in today’s evolving workplace, soft skills—like communication, empathy, and adaptability—are what separate those who thrive from those who stall.

65% of roles require abilities machines can’t replicate, and most employers now rank soft skills as equally or more important than technical ones.

Better Teams Start with Better People Skills
Collaboration is more than just showing up to meetings. It’s about active listening, empathy, and openness. Without these, even the best teams crumble.

👉🏻 Want to lead stronger teams? Start by becoming a better listener.

Leadership Is a Human Skill
Great leaders inspire, communicate clearly, and resolve conflict with emotional intelligence—not just technical know-how.

👉🏻 You don’t need to be perfect—just human and aware.

Soft Skills Fuel Career Growth
As you rise, your impact depends less on code or charts and more on influence, mindset, and trust. Neglect these, and your career can quietly plateau.

👉🏻 Want to move up? Start growing sideways—relationally and emotionally.

5 Practical Ways to Enhance Your Soft Skills

1. Prioritize Self-Awareness: Regularly reflect on your experiences.

   • Understand your strengths, weaknesses, and emotional triggers.

2. Practice Active Listening: In conversations, focus fully on the speaker.

   • Avoid interrupting and show empathy.

3. Develop Emotional Regulation: Learn techniques like deep breathing or mindfulness to manage stress and respond thoughtfully.

   • Include them in your daily routine to enhance your emotional regulation.

4. Enhance Conflict Resolution Skills: Approach conflicts with a problem-solving mindset.

   • Seek win-win solutions.

5. Seek Feedback and Mentorship: Engage with mentors or coaches who can provide insights and guidance on your interpersonal skills.

   • Learn from people who are ahead so you can get there faster.

Technical skills may open doors, but soft skills are what keep them open.

They enable you to lead, collaborate, and adapt in ways that machines can't replicate.

Investing in your soft skills isn't just good for your career—it's essential for long-term success.

Subscribe now

The third module, Python Basics of the introductory course to Data Science is now live on LearnDs.ai. Just go check it out now!

Dive in and explore it today!

PS: You’re allowed to…

• Lead with empathy — even if you’re still learning how.

• Show up as yourself — even when it feels a little vulnerable.

• Ask questions — and not have all the answers.

• Struggle with change — and still be resilient.

• Build people skills — one awkward conversation at a time.

• Be human — not perfect.

• Ask for help (including from me! 🙋🏻‍♂️)

Soft skills take time.

Emotional intelligence is a practice.

Progress comes from leaning in, not opting out.

Subscribe now

Are you still here? 🧐

👉🏻 I want this newsletter to be useful, so please let me know your feedback!

Before you go, tap the 💚 and the restack buttons at the bottom of this email to show your support—it really helps and means a lot!

Any doubt? Let’s start a conversation! 👇🏻

Leave a comment

Don’t forget to check this week’s cheatsheet 💥

Want to get more of my content? 🙋🏻‍♂️

Reach me on:

• LinkedIn, X (Twitter), or Threads to get daily posts about Data Science.

• My Medium Blog to learn more about Data Science, Machine Learning, and AI.

• Just email me at rfeers@gmail.com for any inquiries or to ask for help! 🤓

Remember now that DataBites has an official X (Twitter) account and LinkedIn page. Follow us there to stay updated and help spread the word! 🙌🏻

See you next Tuesday!

Best,

-Josep

Josep here, back with your weekly bite of career insights and encouragement ✨

Last week, we dove into how GenAI is not the real threat, but how you adapt to it might be.

This week, we’re taking a different kind of plunge — one that’s a little more personal, a little more uncomfortable, and incredibly important:
🌀 Your work doesn’t have t…

Read more

Josep here, back with your weekly bite of career insights and encouragement ✨

Last week, we explored The Power of Saying No and how it can help you focus on what matters.

This week, we’re taking a more powerful turn, because today’s topic isn’t just important — it’s transformational:
🌀 GenAI won't replace you—someone who knows how to us…

Read more

Josep here, back with your weekly bite of career insights and encouragement ✨

Last week, we explored How to Stay Relevant in a Rapidly Shifting Job Market, with practical tips to help you stay ahead of the curve 👇🏻

This week, we’re taking a more personal turn.

Today’s note is about something deceptively simple… but deeply transformative:

Read more

Josep here, back with your weekly bite of career insights and encouragement ✨

Last week, we explored 8 ways to increase your luck surface area—practical ways to stop waiting for luck and start attracting it.
If you missed it, catch up here 👇🏻

This week, we're switching gears completely.

Today’s note is about something way bigger:
🌀 How t…

Read more

Josep here, back with your weekly bite of career insights and encouragement ✨

Last week, we explored the idea of Luck Surface Area—the concept that you can increase your exposure to good opportunities through action, visibility, and mindset.

If you missed it, check it out now 👇🏻

This week, we’re taking things one step further.

Read more