Rob Dance

CEO & Founder of ROCK.

I started my business with just £1,000
 
(I bought a cheap van, a laptop, and a desk)
 
Today, we operate internationally
Today, we hire over 100 of the brightest minds
Today, we're an 8-figure business, on our way to 9
Today, we're launching several new start ups
Today, I work with some of the most gifted people in the world
 
But what you don't know is:
 
20 years ago, I failed my exams and dropped out of school
17 years ago, I really struggled to write a full sentence
15 years ago, I was too scared to make phone calls

So now I'm sharing my journey with you.
Daily insights to help anyone.
Let's goooooooo 🚀

CEO & Founder

After 15 years of growing my business, I realised something FUNDAMENTAL was missing.

Our unique culture and innovative ways of managing our people has always been a TOP priority for me.

So much so, I have never found the perfect software platform to manage our people operations and help my team remove the day-to-day noise so they can focus on our mission.

Then, one day, it struck me: I NEED to create this myself to help other businesses!!

And so, Ware was born.

Launching in 2025, Ware promises to transform how you build your culture and genuinely empower your team.

Ware. Everything is.

A reminder for anyone who needs it:

(You deserve better)

Stop giving 100% to companies that treat you like you're replaceable.

If you're always:

- Staying late
- Pushing hard
- Hitting targets

And you’re still getting ignored - it's not your fault.

You're not unmotivated.
You're undervalued.

A company that appreciates you will show it. They’ll:

1) Recognise your contributions and effort.
2) Listen to your concerns and act on them.
3) Respect your boundaries and work-life balance.
4) Promote from within instead of overlooking internal talent.
5) Support your career growth with training and opportunities.
6) Make staying feel like the right choice, not your only choice.
7) Pay you what you’re worth, not what they can get away with.

Loyalty is a two-way street.

If they can't meet you halfway, don’t feel guilty for pulling back.

Protect your energy.
Know your worth.

The right workplace won’t make you question it.

Do you agree?

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Stop giving 100% to people who give you 10%. Rob Dance

Confidence in leadership isn’t about always having the answer

It’s about this…

Trusting yourself to figure things out - even when nothing feels certain.

Too many leaders think they need to seem perfect. 
But that’s not where respect comes from.

Respect comes from how you handle pressure. 
From how you respond when things go wrong.

The best leaders?

→ They ask for help. 
→ They admit when they’re unsure. 
→ They build teams that challenge their thinking.

So if you want to lead with confidence, try this:

1. Listen more than you speak.
2. Let your actions do the talking.
3. Be willing to be wrong - honesty builds trust.
4. Surround yourself with strong, honest voices.
5. Stand by your decisions, especially the tough ones.
6. Trust yourself to figure it out - not to have it all figured out.

You don’t need to feel “ready.”

You just need to trust that you’ll handle what comes next.

Do you agree?

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Confidence is knowing you’ll figure it out, not knowing all the answers. Rob Dance

Someone else’s success is not a threat.

It’s proof.

Proof that progress is possible.

When I see someone win… 
I don’t feel smaller.

I feel reminded.

If they can do it, so can I.

As Justin says, too many people sit back:

- Judging
- Comparing
- Secretly hoping someone slips up

That mindset builds nothing.

Because clapping for others shows strength.

It shows confidence. 
It shows security.

So if you’re in a waiting season, use it.

→ Study what works 
→ Improve your skills 
→ Stay consistent

There is no weakness in celebrating someone else’s win.

Only insecurity makes it feel that way.

Do you agree?

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Until it’s my turn, I will keep clapping for others. It’s just that simple. Rob Dance and Justin Wright.

Respect should never depend on a job title.

No one needs to know someone’s:

- Salary
- Position
- Qualifications

To treat them well.

They are human. 
That alone is enough.

I see people switch their tone based on who stands in front of them.

And that shift says more than any CV ever will.

Character shows when there is nothing to gain.

So if you want to grow as a leader, practise these daily:

1. Say thank you
2. Keep eye contact
3. Show basic respect
4. Greet everyone properly
5. Listen without interrupting

These actions cost nothing.

But they build trust faster than any title ever will.

Do you agree?

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Make it a habit to respect people without knowing their qualifications, title, or position. Rob Dance

This isn’t meant to feel good…

(But it’s how we grow)

As Justin Wright pointed out - it’s discomfort that takes you far.

Yet too many leaders avoid disagreement.

They see silence and call it respect.
They chase harmony instead of honest thinking.

But great leaders do it differently:

→ They listen with calm.
→ They speak with purpose.
→ They invite challenge, not just compliance.

The simple truth?
They want people who call out what’s wrong.

So if no one’s questioning you, it’s not a sign you’ve nailed it.

It’s a sign they’ve stopped believing you’ll listen.

Do you agree?

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Insecure leaders want only yes-people around them. Strong leaders want people who challenge their thinking. Justin Wright and Rob Dance

Here’s the truth:

Playing safe won’t make you happy.

If there’s even a slight chance something could improve your life…

Explore it.

- Don’t overthink it
- Don’t talk yourself out of it before you’ve even tried

Life is short.

Time doesn’t slow down.

And real happiness?

It’s rare.

It doesn’t come from staying where you feel stuck. 
It comes from movement.

Fear will always show up.

But that doesn’t mean stop.

It means you’re stepping outside your comfort zone.

That’s where progress lives.

→ Take the call
→ Book the meeting
→ Submit the application

Risk feels scary.
Regret feels worse.

Do you agree?

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If there’s even a slight chance at getting something that will make you happy, risk it. Life’s too short and happiness is too rare. Rob Dance.

Careers do not end because of:

- One rejection
- One bad review
- One missed promotion

They stall when effort stops.

Because growth belongs to people who stay in motion.

So if you are serious about career growth, focus on three things:

→ Upgrading skills 
→ Building strong relationships 
→ Asking for honest feedback

Then apply what is learnt immediately.

Progress does not reward pride.

It rewards people who refuse to stay still.

Do you agree?

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Nothing is over for someone who tries. Rob Dance.

Want to level up fast in your career?

Here’s how:

Sit beside someone who's already ahead.

Not to compete.
To catch their momentum.

When you’re around ambitious people, your mindset shifts.

→ You think bigger.
→ You raise your standards.
→ You stop making excuses.

And as Dora Vanourek puts it, it stretches you in ways you didn’t expect.

So ask yourself:

Is your circle fuelling your growth?
Or keeping you stuck?

Choose your company with intention.

It matters more than you think.

Do you agree?

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A reality check for anyone who needs it:

(You deserve better)

Loyalty isn’t a one-way street.

Too many workplaces expect it without giving anything in return.

But a company that values you will show it. They’ll:

1) Recognise your contributions and effort.
2) Listen to your concerns and act on them.
3) Respect your boundaries and work-life balance.
4) Promote from within instead of overlooking internal talent.
5) Support your career growth with training and opportunities.
6) Make staying feel like the right choice, not your only choice.
7) Pay you what you’re worth, not what they can get away with.

If they:

- Don’t value you
- Don’t invest in your growth
- Don’t try to retain you when you consider leaving

Take the hint.

Staying in a place that doesn’t value you will drain you.

Take your skills, time, and energy where they’re appreciated.

Do you agree?

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We’ve all had that one leader.

The one who:

→ Saw our potential before we did.
→ Stood by us when we doubted ourselves.
→ Pushed us forward - not with noise, but with quiet belief.

And as Reno Perry says, that kind of faith?

It stays with you.
It shapes who you become.

So here’s the real question:

Are you doing that for someone else?

Because someone on your team is waiting.

Waiting for just one person to say: “I believe in you.”

Be that person.
Be that leader.

Do you agree?

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Remember the leader who believed in you before you believed in yourself? Be that person for someone else. - Reno Perry and Rob Dance.

One of the hardest lessons to learn:

You cannot make someone change.

Sometimes you spend months trying to raise issues that are affecting the team.

- You give feedback.
- You offer suggestions.
- You explain the impact on people around you.

And still…
Nothing changes.

Why?

Because listening starts with awareness.

And if a leader cannot see the problem in their behaviour?
They believe they have no reason to change it.

Employees can raise concerns.

They can be honest.

They can explain what is happening on the ground.

But improvement only happens when a leader chooses to:

→ Listen
→ Reflect
→ Take responsibility

So remember:

You cannot force someone to hear what they are not willing to hear. 

Do you agree?

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Biggest lesson this year?

Stop over-giving to people who wouldn’t do the same for you.

It sounds blunt. 
It’s still true.

Too many of us pour:

- Time
- Effort
- Loyalty

Into relationships that give nothing back.

That isn’t kindness. 
It’s imbalance.

So I watch actions now.

Not words.

If someone values you, they show it.

→ They check in 
→ They make time 
→ They show up

The right people step up.

The rest drift away.

Do you agree?

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Biggest lesson I learnt last year was probably not to give so much of yourself to people who wouldn’t do the same for you. Rob Dance

If it isn’t said, it gets carried…

And that weight builds fast.

Silence feels easier.

- No tension
- No awkward talk
- No risk of upsetting anyone

But you pay for it later.

Unspoken words turn into stress.

Stress turns into frustration.

And frustration leaks into your:

→ Tone 
→ Energy 
→ Behaviour

Staying quiet does not fix the problem.

It stores it.

Do you agree?

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Remember: if you’re not saying it, you’re carrying it, and that gets heavy fast. Rob Dance.

Career growth is often misunderstood.

People expect it to be loud and obvious.

→ A promotion 
→ A new job title 
→ A big announcement that everyone notices

But most real growth is quiet.

Sometimes it simply looks like clarity.

Clarity about what matters at work. 
Clarity about what does not.

Real growth often begins with three things.

1) Clarity 
↳ Knowing what work truly matters 
↳ Deciding where your effort should go 
↳ Understanding the direction your career should take

2) Boundaries
↳ Setting limits 
↳ Protecting your time 
↳ Saying no to work that pulls you away from real progress

3) Better decisions
↳ Leaving environments that keep you stuck 
↳ Choosing opportunities that build real skills 
↳ Working with people who challenge you to improve

Career growth rarely arrives with applause.

Most of the time, it starts with quiet choices no one else sees.

Do you agree?

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Growth doesn’t always look loud. Sometimes it looks like clarity, boundaries, and better decisions. Rob Dance.

I used to believe support was enough to change people.

If I encouraged them…

- Backed them
- Gave them time

They would step up.

I was wrong.

Because good intentions do not build culture.

Consistent behaviour does.

If someone talks about teamwork but gossips 
↳ Believe the gossip.

If someone promises change but repeats the same mistakes 
↳ Believe the pattern.

Culture weakens when leaders ignore red flags to avoid conflict.

It strengthens when behaviour is addressed early and clearly.

Do you agree?

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🤖Data Scientist | Turning Raw Data into Money Generating Insights | Data-led Growth for Your Business 🚀

Freelance Data Scientist | ML Engineer | SQL | Python | Power BI | MLOps

Data analytics and Machine learning models built 5X faster - no quality compromise. Worked with 14 clients in 7 different industries in the last 3 years. Helped current company scale to $5M+ ARR. Featured TOP RATED PLUS talent on Upwork in 2025 with 100% job completion and satisfaction.

My Toolkit:
- Data analytics: Python, SQL, Excel
- ML Engineering: FastAPI, AWS, Streamlit, great_expectations
- Data visualization: PowerPoint, PowerBI, Plotly, Matplotlib
- Data modeling: Scikit-learn, TensorFlow, Keras
- MLOPs: CI/CD (GitHub Actions), Docker
- Additional tools: Notion, n8n, Canva, Git/GitHub

My Stats:
- 5-stars reviews on 99% of projects delivered.
- 100% Job Success Score for on-time, budget delivery.
- Helped scale currently business to $5M ARR with ATS analysis & Reporting.
- 1.5 years TOP RATER PLUS on Upwork.
- 1800 hours billed.
- $60k+ earned.

What You’ll Get Working With Me:
- A data analyst who is a real problem solver.
- Quick responses (within the hour on working hours)
- Clean, maintainable code and detailed documentation
- A clear “NO” if something isn’t feasible; no wasted time
- A well-crafted report or dashboard as per the requirements

My clients think:
- "Anas is great and highly knowledgeable. He is passionate about solving data science challenges and has helped me significantly streamline some of my processes, enabling better scalability.
- "Anas, was prompt and thorough and I would def use him again in the future."
- "Anas is great to work with. He made the job so fluid and his knowledge of data science makes him someone worth listening to. I’m glad I chose him for my small business. I’m looking forward to what’s coming next, and I would definitely hire him again."

Popular Clients & Products:
- Uhubs: Financial company based in London
- AllAboutGroup: Law recruitment firm based in London
- Growth Dynamics: Marketing and data strategist based in London.
- Adway: Tech and recruitment company based in Gotheborg, Sweden

About me:
- Football fan since I can remember (Real Madrid fan)
- Tennis lover and practitioner (one-handed backhand style)
- Mangas fan (One Piece, Naruto, Bleach)
- Travel addict (South Asia, Europe, Africa, America (soon))
- Reading is something I enjoy as much as coffee (oat latte fan recently)

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YouTube Channel
→ GO HERE: https://www.youtube.com/anas_riad

Request services via LinkedIn or Email below:
r.riadanas@gmail.com

Hire on Upwork:
https://www.upwork.com/freelancers/~0163377bf3029c7553

Anas Riad

Not every problem needs an LLM. 
Some need 12 characters of regex.

I see people spending $𝟬.𝟬𝟯 𝗽𝗲𝗿 𝗔𝗣𝗜 𝗰𝗮𝗹𝗹 to extract emails from text.

Meanwhile: ⤵️

[𝗮-𝘇𝗔-𝗭𝟬-𝟵+_.-]+@[\𝘄-]+\.\𝘄+

Done. 𝗭𝗲𝗿𝗼 𝗰𝗼𝘀𝘁. Runs in milliseconds.

We're in 2026 and somehow we forgot that basic code still works. 

Not everything needs a prompt, a model, and a 3-second response time.

Need to validate a phone number? 
→ Regex. 

Need to clean whitespace from 10,000 rows? 
→ A one-liner. 

Need to rename 500 files? 
→ A bash script your laptop runs in 0.2 seconds.

LLMs are incredible for reasoning, writing, and ambiguity. 
But when the task has a clear rule and a predictable pattern, just write the rule.

Cheaper. Faster. More reliable.
Stop using a rocket ship to cross the street.

PS: Do you use LLMs for everything because it's convenient?

♻️ Repost if your network needs this reminder 
📌 Save it for the next time you almost open ChatGPT/Claude for string matching

Follow me for more → Anas Riad

Data Scientist & BI Consultant @Adway • ML Engineer • YouTube @ Anas Riad

Dear recruiters,

If you expect a Data Scientist to be an expert in:

→ Python for large-scale data analysis
→ Excel with complex financial-style reports
→ Pandas and NumPy on real, broken datasets
→ Heavy data cleaning and feature engineering
→ Git workflows, CI/CD, and automated testing
→ LLMs, prompt design, and applied AI use cases
→ Business KPIs, north-star metrics, and trade-offs
→ Clear storytelling with non-technical stakeholders
→ Dockerized environments and reproducible pipelines
→ Experiment tracking, model versioning, and rollbacks
→ Data pipelines touching cloud storage and warehouses
→ Regression, classification, and model selection at scale
→ Advanced SQL with messy joins and window functions
→ Exploratory analysis with Matplotlib, Plotly, and Seaborn
→ Probability theory, hypothesis testing, and A/B experiments
→ Bias–variance trade-offs and model performance monitoring

… you’re not hiring one 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁.
You’re describing an 𝗲𝗻𝘁𝗶𝗿𝗲 𝗱𝗮𝘁𝗮 𝗱𝗲𝗽𝗮𝗿𝘁𝗺𝗲𝗻𝘁.

PS: Post inspired by José Siles

📌 Follow Anas Riad for more content around data science

The ML project you wish you built sooner.
2.5hr of hands-on learning ⤵️

No excuses for skipping end-to-end ML now! 🤣

(PS: Link to the full YT video in the comments)

When I first started working on churn prediction, 
I noticed something that always bothered me.

There are tons of Regression projects out there, 
but 𝟗𝟗% 𝐨𝐟 𝐭𝐡𝐞𝐦 stop at the notebook.

It’s like drawing the blueprint for a house but never building it.

So I created a full end-to-end regression project that goes from:
raw dataset → trained model → deployment → tests → cloud.

Here’s what I cover in this project & video:
→ Split data by date (time-series aware)
→ Data Quality with Great Expectations
→ Well-structured pipelines (feature, training & inference)
→ Unit & smoke tests to keep things reliable
→ Experiment tracking with MLflow
→ Containerization with Docker
→ FastAPI serving layer
→ CI/CD with GitHub Actions
→ AWS (ECS, ECR, S3, ALB) for Deployment

It’s the same type of stack I use in freelance + personal work, 
but applied to regression.

Looking forward to your thoughts on it.

🌟 Link to the full YT video in the comments

PS: I have another end-to-end coming soon, stay tuned 🤔

YouTube is still the BEST place to learn ML.

Most people still watch the wrong videos.

Here are 𝟭𝟮 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝘃𝗶𝗱𝗲𝗼𝘀 𝘁𝗼 𝗺𝗮𝘀𝘁𝗲𝗿 ML ⤵️ 

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𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗖𝗦𝟮𝟮𝟵: 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
 
The legendary starting point for ML.
Covers core theory, intuition, and key algorithms.

Video: https://lnkd.in/e-aJRQun
 
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𝗔𝗹𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 𝗶𝗻 𝟮𝟮 𝗠𝗶𝗻𝘂𝘁𝗲𝘀

A fast, structured overview of all major ML concepts.
Great for building a mental map before going deeper.
 
Video: https://lnkd.in/exCP58F6
 
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𝗠𝗟𝗢𝗽𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 𝗳𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀
 
Shows how ML actually works in real products.
Pipelines, deployment, monitoring, and production mindset.
 
Video: https://lnkd.in/erzM3b4F

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𝗛𝗼𝘄 𝘁𝗼 𝗗𝗲𝗽𝗹𝗼𝘆 𝗠𝗟 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 (𝗙𝗮𝘀𝘁𝗔𝗣𝗜, 𝗗𝗼𝗰𝗸𝗲𝗿, 𝗔𝗪𝗦)
 
From trained model to live service.
A practical deployment walkthrough with modern tools.
 
Video: https://lnkd.in/e3JtcAZk

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𝗦𝘁𝗲𝗮𝗹 𝗠𝘆 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲

Learn how to structure ML projects properly.
Clean folders, reproducibility, and real-world practices.
 
Video: https://lnkd.in/eds2Buq8

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𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗠𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻
 
A full ML project from start to finish.
Data prep, modeling, evaluation, and delivery.
 
Video: https://lnkd.in/ebx_dvtn

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𝗠𝗟𝗢𝗽𝘀 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹 (𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗲𝘄𝗲𝗿 𝗦𝗲𝗻𝘁𝗶𝗺𝗲𝗻𝘁)
 
Build a real ML pipeline using real data.
Feature engineering, modeling, and orchestration.
 
Video: https://lnkd.in/e9_xVb-Z

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𝗬𝗼𝘂 𝗪𝗶𝗹𝗹 𝗡𝗢𝗧 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗶𝗻 𝟯 𝗠𝗼𝗻𝘁𝗵𝘀
 
A reality check on learning machine learning.
Focuses on process, depth, and consistency.
 
Video: https://lnkd.in/eRqYhJWK

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𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗠𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁
 
A practical regression project walkthrough.
Covers evaluation, assumptions, trade-offs.
 
Video: https://lnkd.in/e2GEnb4N

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𝗘𝘃𝗲𝗿𝘆 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱
 
A clear overview of common ML models.
 
Video: https://lnkd.in/eMW3YNKq

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𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗚𝗿𝗮𝗱𝗲 𝗠𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹
 
Focus on building ML systems that last.
Code quality, deployment, and maintainability.
 
Video: https://lnkd.in/eXYhdxgt

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𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀)
 
A simple introduction to ML definitions and concepts.
 
Video: https://lnkd.in/eT3qrGBt
 
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90% of analysts use only 10% of Pandas.

Master these 10 operations. Level up instantly.

When I started learning data analysis,
I thought Pandas was “too big” to master.

Turns out… you only need the core.

Here are the operations I use almost daily:

• 𝗟𝗼𝗮𝗱 𝗱𝗮𝘁𝗮 → pd.read_csv()
• 𝗜𝗻𝘀𝗽𝗲𝗰𝘁 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 → .head(), .info(), .describe()
• 𝗦𝗲𝗹𝗲𝗰𝘁 𝗰𝗼𝗹𝘂𝗺𝗻𝘀 → df[["col1","col2"]]
• 𝗙𝗶𝗹𝘁𝗲𝗿 𝗿𝗼𝘄𝘀 → df[df["col"] > value]
• 𝗚𝗿𝗼𝘂𝗽 & 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 → .groupby().agg()
• 𝗖𝗿𝗲𝗮𝘁𝗲 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 → df["new"] = expression
• 𝗠𝗲𝗿𝗴𝗲 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 → pd.merge()
• 𝗛𝗮𝗻𝗱𝗹𝗲 𝗺𝗶𝘀𝘀𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲𝘀 → .isna(), .fillna()
• 𝗦𝗼𝗿𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 → .sort_values()
• 𝗘𝘅𝗽𝗼𝗿𝘁 𝗰𝗹𝗲𝗮𝗻 𝗱𝗮𝘁𝗮 → .to_csv() or .to_excel()

That’s it.

If you can combine these properly, you can solve 80% of real-world problems.

The rest is practice and business thinking.

PS: Is Pandas your go-to library for data analytics?

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95% of data scientists who reach out to me

Have this 1 thing in common ⤵️ 

“They excel at building ML models in a notebook”
but "They struggle to build something beyond a Demo"

Look, I’m not against notebooks, but that mindset is restrictive.

If your project only:

→ runs on your machine
→ uses static data (all clean)
→ isn’t deployed and usable

Then it’s a "SIMPLE" demo, not a "REAL" data project.

In my recent journey to bridge the gap, I’ve decided to collaborate with Markus Kuehnle on a detailed carousel that includes major points of an end-to-end ML project, such as:

→ Setting project environment
→ Exploratory data analysis
→ Feature Engineering
→ Model Registry & Experiment Tracking
→ Feature, training, inference pipelines
→ Serving layer (FastAPI)
→ Docker container (reproducibility)
→ GitHub Actions (CI/CD)
→ Deployment & monitoring

Hope it gives you more clarity on what it takes to build and deploy an ML model end-to-end.

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Machine learning End-to-End pipeline

I started in data analytics in 2021. 
The job barely looks the same today.

Back then, your value was tied to tools.

You learned SQL. You learned Python. Maybe some Tableau or Power BI. And that technical gap between you and everyone else in the company? That was your edge. You could dig deeper than the person pulling numbers from a quick Excel sheet. That was enough.

Fast forward to 2025 and 2026.

AI writes code faster than most junior analysts. It reasons through data problems. It spots patterns. And yeah, I know what some of you are thinking, "but it makes mistakes." Sure. So do we. We miss things. We overlook patterns. We carry bias into every analysis without realising it. We're not the gold standard we like to think we are.

I've been working with AI daily for the past 3 years now. And what I've watched happen slowly at first, then all at once, is that the technical barrier that made analysts valuable is shrinking fast.

That directly hits entry level roles the hardest.

If your entire value is "I can write a SQL query and build a dashboard," you're competing with something that does it in seconds. Not in a hypothetical future. Right now.

But here's what I've also noticed in 3 years of doing this work:

The analysts who actually move the needle? They were never just "tool operators."

They're the ones who sit with stakeholders and ask the hard questions. They're the ones who take a messy data output and turn it into a recommendation that helps the business earn more, save more, or move faster. They understand context. They push back on bad assumptions. They connect the numbers to actual decisions.

No AI tool does that well yet.

So if you're early in your career or thinking about getting into analytics the path forward isn't about memorising SQL syntax or chasing every new tool.

It's about:

→ Going deeper than surface-level reporting  
→ Learning to frame problems before jumping into data  
→ Giving recommendations, not just dashboards  
→ Expanding into predictive models, time series, and forecasting  
→ Using AI as a multiplier, not pretending it doesn't exist

The role of data analyst isn't dying.

But the version of it that was just querying and reporting? That chapter is closing.

The analysts who evolve will be fine. Better than fine, actually, because AI gives you leverage that didn't exist 3 years ago.

The ones who don't evolve will wonder what happened.

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