I help entry-level, non-technical professionals get their first data analyst role | Data Analyst @RBC | Data Career Coach

I help immigrants and career-switchers land analyst roles—without burning out on certificates.

By day I build decision-grade analytics (currently at Autodesk). By night I run Data Career Compass, where we treat your job search like a product launch:
Market → Positioning → Portfolio → Distribution → Interviews.

My POV:
Most candidates try to add more tools. Winners learn to package their value. Your portfolio isn’t a gallery; it’s a sales page for your judgment.

Receipts:

→ Professionals land interviews by aligning projects to business outcomes (not toy datasets).

→ My frameworks have been used across finance, tech, and education to translate experience into results.

→ Built data products and controls in regulated environments (RBC Capital Markets) that leaders rely on daily.


What you’ll get here:

→ Weekly breakdowns of real projects (SQL, Python, dbt, DuckDB, Looker/Tableau/Power BI).

→ Storytelling patterns that turn insights into action.

→ Templates that remove friction: portfolio briefs, stakeholder one-pagers, interview scorecards.

Coming April:

I’m launching a coaching program designed for mid-level and senior professionals ready to transition into analytics.

This isn’t an entry-level bootcamp—it’s a career accelerator that helps you reposition your existing experience, communicate business value, and step confidently into data-driven roles.

Work with me:

↳ Cohorts + 1:1 coaching → DM “HIRED”

↳ January program for mid/senior professionals → DM “APRIL” for early access

↳ Guest workshops for teams and communities → DM “WORKSHOP”


I build analysts who think like product managers and communicate like consultants.

Peter Ndiforchu

Every data analyst has lived through this meeting.

Dashboard says: $1.2M
Finance says: $1.1M

Meeting topic for the next 45 minutes:

“Where did the $100K go?”

First theory:
Maybe a filtering issue.

Second theory:
Currency conversion.

Third theory:
Time zone mismatch.

Fourth theory:
Someone forgot to refresh the data.

Meanwhile the analyst is quietly thinking:

“Please don’t be a duplicated join. Please don’t be a duplicated join. Please don’t be a duplicated join.”

45 minutes later…

Root cause:

Someone exported the numbers to Excel
…and manually deleted a row “that looked wrong.”

New meeting scheduled:

“Aligning reporting methodology across teams.”

Welcome to data analytics.

Where 90% of the job is not finding insights…

It’s explaining why two numbers that should match… don’t.

If you’ve ever spent an hour chasing a mysterious variance in a meeting, you’re officially part of the club. 📊😅

Helping Non-Technical Professionals Build Data-Driven Careers That Travel Globally | Data Career Strategist | Senior Data Analyst @ Autodesk

↳ For sponsorship, www.amneymedia.com 
↳ For 1:1 Coaching, DM me

Amney Mounir

SQL is a muscle that you need to practice regularly.
(Even in 2026 with the rise of AI)

Here a few topics that you MUST know:
↳ Basic Queries (SELECT, WHERE)
↳ Aggregations (COUNT, AVG, MIN, MAX)
↳ Grouping (GROUP BY, HAVING)
↳ Subqueries & CTEs
↳ Joins (LEFT, SELF, CROSS)
↳ Ranking Functions (ROW_NUMBER, RANK, DENSE_RANK)
↳ String Functions (CONCATENATE, LENGTH, SUBSTRING)
↳ Date Functions (CURRENT_DATE, INTERVAL)

Get your hands dirty with these resources:
📗  SQL Winter Challenge: https://lnkd.in/g6KWXCyv
📘 SQL Module: https://lnkd.in/gijvU59S

———
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Every Data Analyst interview has a SQL round.
Every Data Scientist interview has a SQL round.
Every Data Engineer interview has a SQL round.

SQL is the one language that you need to know.

So if you’re serious about breaking into data 
SQL is non-negotiable.
(𝘌𝘷𝘦𝘯 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘳𝘪𝘴𝘦 𝘰𝘧 𝘈𝘐)

Many will struggle to validate AI outputs.
That's why knowing SQL is a must.

I recommend practicing on Dataford, my favorite resource for upskilling in SQL and preparing for your next Data interview. 

Here are their most valuable features: 
📕 Modules: https://lnkd.in/gz9Cr8R3
📗 Interview Guides: https://lnkd.in/gXhBTqXs
📘 Interview Questions: https://lnkd.in/g62bfHF6

———

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Most executives talking about AI have never used it.

But the ones using it every day know the truth.

AI is powerful yes,
𝗕𝘂𝘁 𝗶𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗺𝗮𝗴𝗶𝗰.

That’s the difference between:

Talking about AI vs. Working with AI.

The best Data Analysts I know are NOT replacing themselves with AI.

They are using AI to move faster.

YET, execs are pushing SO much for it. Asking to use AI as much as possible. And you get rewarded by spending more tokens than doing actual work and impact. 

This is the problem. 

Because if everyone is using AI for work,
then what will differentiate you from someone else?

The answer is not the AI.
It’s YOU!

———

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Data Analytics @ Meta | Become a 10x Analyst with AI | 60k+ Community on LinkedIn ❤️

Data Analytics is officially dead in 2026.

This is what I am hearing a lot lately, but the reality is:

The Data Analyst role is NOT dying.
It is just evolving.

AI has become a massive part of how we work.

And if you’re not learning how to use it, you’re putting yourself at a disadvantage.

In my own role at Meta, AI has helped me:
- Explore more analysis
- Communicate insights better
- Provide more value to stakeholders

I still strongly believe this:

𝗔𝗜 𝘄𝗼𝗻’𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀.
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀 𝘄𝗵𝗼 𝘂𝘀𝗲 𝗔𝗜 𝘄𝗶𝗹𝗹.

So instead of fearing it…
Start learning how to work with it.

———

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8 habits that changed my career as a Data Analyst:

𝘚𝘢𝘷𝘦 𝘵𝘩𝘪𝘴, 𝘪𝘵 𝘤𝘰𝘶𝘭𝘥 𝘣𝘦 𝘢 𝘨𝘢𝘮𝘦 𝘤𝘩𝘢𝘯𝘨𝘦𝘳 𝘧𝘰𝘳 𝘺𝘰𝘶

1) Always question what you're being asked
2) Always validate the data before sharing it
3) Always communicate with extreme clarity
4) Build systems to automate repetitive tasks
5) Be proactive and don't wait for things to happen
6) Always prioritize impact over more work
7) Never stop learning and accept to unlearn
8) Don't be afraid to say “I don’t know”

With these habits I started getting:
→ More visibility
→ Bigger projects
→ More ownership
→ And Faster growth

Now it's your time to apply them.

———

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With this one trick, you won’t have to use CTEs or Subqueries anymore.

Most analysts solve “latest value per user” with:
window function → subquery or CTE → adding a filter

You don’t need any of that.

Just use 𝗠𝗔𝗫_𝗕𝗬 / 𝗠𝗜𝗡_𝗕𝗬

It will make your queries cleaner.
And less headache to be honest.

Here are few resources to get you started:
📕 SQL Module: https://lnkd.in/gijvU59S
📘 Playground: https://lnkd.in/g6KWXCyv

I'm Amney, I shared Data tips and resources
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Being a Data Analyst is more than SQL and Analysis.

A big portion of your time is spent on:

- Aligning and convincing shareholders
- Troubleshooting metric drops
- Meetings with the team
- 𝘗𝘳𝘰𝘮𝘱𝘵𝘪𝘯𝘨 𝘵𝘩𝘦 𝘓𝘓𝘔 (𝘪𝘯 2026)

That's why technical skills are equally important as soft skills to become a Great Data Analyst. 

———

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Soft skills > Hard skills for Data Analysts in 2026.

Let me explain why:

Most aspiring Data Analysts focus on hard skills:
- SQL
- Python
- Excel
- Tableau

While these skills are super important, everyone is becoming good at them (𝘢𝘯𝘥 𝘯𝘰𝘸 𝘦𝘷𝘦𝘯 𝘮𝘰𝘳𝘦 𝘸𝘪𝘵𝘩 𝘈𝘐)

𝗧𝗼 𝘀𝘁𝗮𝗻𝗱𝗼𝘂𝘁 𝗶𝗻 𝟮𝟬𝟮𝟲, 𝘁𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱:
1) Strong business sense
2) Product thinking & ideation
3) Clear storytelling using data
4) Ability to collaborate with teams

Here are few resources to guide you:
📕 Problem Solving: https://lnkd.in/g6iFEf7B
📘 Behavioral: https://lnkd.in/gzCzvQGs
📗 Product Ideation: https://lnkd.in/gbHRs4_H

Shift your focus from technical to soft skills,
And you will def standout in this new era.

———

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Most people know 𝗼𝗻𝗲 𝘄𝗮𝘆 to remove duplicates in SQL.

During interviews, they’ll usually ask for more than one approach.

Here are 4 ways to do it:

1) Using HAVING
2) Using Ranking Functions
3) Using SELF JOIN (𝘭𝘦𝘢𝘴𝘵 𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘵)
4) Using DELETE (𝘺𝘰𝘶 𝘯𝘦𝘦𝘥 𝘵𝘰 𝘣𝘦 100% 𝘴𝘶𝘳𝘦)

If you wanna take it to the next level, I highly recommend going to this free workshop hosted by Mark, Lead Data Scientist at Meta, on how to master your Data Scientist Interview at Big tech.

📕 FREE Workshop: https://luma.com/vsndcrlk

———

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The best Data Analysts don’t just pull numbers.

They tell great stories.

Too often I see analysis that are hard to read.
And the story doesn’t make sense.

So here are a few things to make it better:

𝟭) 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗮 𝗧𝗟𝗗𝗥
What are the 3 things they should remember?

𝟮) 𝗔𝗱𝗱 𝗮 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝘀𝗲𝗰𝘁𝗶𝗼𝗻
What problem(s) are you trying to solve?
What is the objective of your analysis?

𝟯) 𝗦𝗵𝗮𝗿𝗲 𝘁𝗵𝗲 𝘁𝗼𝗽 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀
Keep them crisp and easy to understand.

𝟰) 𝗡𝗲𝘅𝘁 𝘀𝘁𝗲𝗽𝘀
What are we going to do with it?

This way, your audience gets it.

That’s how you drive decisions.
That’s how you create impact.
That’s how you stand out.

———
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8 things you can start automating with AI 👇

𝟭) 𝗦𝗤𝗟 𝗤𝘂𝗲𝗿𝘆 𝗪𝗿𝗶𝘁𝗶𝗻𝗴: I love SQL but writing a lot of SQL from scratch is painful. Using AI helped me move faster and tweak the query from AI if needed.

𝟮) 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴
As Data Analyst, we deal with a lot of messy data, AI can help find missing values, remove duplicate and make your life easier.

𝟯) 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗼𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗘𝗗𝗔)
AI can help you detect correlations, anomalies, and trends faster and accelerate your work.

𝟰) 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻
Generating dashboards is def something I don't enjoy doing, having AI help is a big reliever.

𝟱) 𝗪𝗿𝗶𝘁𝗶𝗻𝗴 𝗥𝗲𝗽𝗼𝗿𝘁𝘀 (𝘮𝘺 𝘮𝘰𝘴𝘵 𝘤𝘰𝘮𝘮𝘰𝘯 𝘶𝘴𝘦 𝘤𝘢𝘴𝘦)
Writing takes me so much time and AI definitely helped me to make my reports clearer.

𝟲) 𝗠𝗲𝗲𝘁𝗶𝗻𝗴 𝗡𝗼𝘁𝗲𝘀
Taking notes and dealing with action items was a pain and AI can now take notes for you and send you a report so you don't have to do it.

𝟳) 𝗗𝗮𝘁𝗮 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻
Documenting the structure of the db is definitely something AI can take out of your plate (takes so much manual work and effort)

𝟴) 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀
My favorite workflow is:
𝘐 𝘰𝘯𝘭𝘺 𝘶𝘴𝘦 𝘪𝘵 𝘧𝘰𝘳 𝘴𝘪𝘮𝘱𝘭𝘦 𝘢𝘥-𝘩𝘰𝘤 𝘢𝘯𝘢𝘭𝘺𝘴𝘪𝘴.
- List action items from meeting notes
- Ask AI to start the work/analysis
- Review the work from AI

Imagine taking all of these from your plate.
You will save hours working on things that can be more impactful and help the business grow.

AI is accelerating us as Data Analysts.
Not replacing us.

———

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People will only show up when you get the job. 

Not when you’re stuck job hunting.
Not when you’re getting rejected.
Not when you're getting ghosted.

Job searching is lonely (𝘐'𝘷𝘦 𝘣𝘦𝘦𝘯 𝘵𝘩𝘦𝘳𝘦)

You’re trying to stay motivated.
Trying not to take it personally.
Trying so hard not to give up.

Here’s what I wish someone told me:
1) Rejection ≠ you’re not good enough
2) Ghosting ≠ you’re not capable
3) One “yes” changes everything

You don’t need 100 YES.
You only need 1 YES!

Stay consistent.
Keep improving.
Keep showing up.

𝗔𝗡𝗗 𝗬𝗢𝗨 𝗪𝗜𝗟𝗟 𝗚𝗘𝗧 𝗧𝗛𝗘𝗥𝗘.
I'm rooting for you!

———

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As you grow as a Data Analyst, your impact scales.

Here’s what that journey usually looks like:

𝗟𝘃𝗹 𝟭: 𝗝𝘂𝗻𝗶𝗼𝗿 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
You are told what to do and how to do it.

𝗟𝘃𝗹 𝟮: 𝗠𝗶𝗱-𝗟𝗲𝘃𝗲𝗹 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
You are told what to do
but you figure out how to do it.

𝗟𝘃𝗹 𝟯: 𝗦𝗲𝗻𝗶𝗼𝗿 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
You are given a specific problem, 
and you figure it out.

𝗟𝘃𝗹 𝟰: 𝗦𝘁𝗮𝗳𝗳 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
You identify the problems and solve them.

𝗟𝘃𝗹 𝟱: 𝗚𝗢𝗔𝗧 𝗔𝗻𝗮𝗹𝘆𝘀𝘁
You define the vision and multiply your impact.

As you move up:

Your scope grows.
Your influence grows.
Your impact compounds.

That’s how you become irreplaceable.

Where do you stand today?
𝘓𝘦𝘵 𝘮𝘦 𝘬𝘯𝘰𝘸 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴

———

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The future of Data Analysts is Human-AI Hybrid.

And it’s happening faster than people think.

I used to spend 70% of my time:
→ Writing SQL
→ Updating dashboards
→ Writing documentations
→ Fixing broken pipeline/queries

I am not doing that anymore. 

That’s why I've been using Count.co

Their AI agent:
→ Makes comprehensive dashboards
→ Writes transparent SQL (you see everything)
→ Lets humans + multiple agents work together
→ Keeps governance and trust

That’s the future.

Not AI replacing analysts.
AI augmenting them.

You can test Count.co for FREE
 ↳ https://lnkd.in/gHjButQH

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Prompting is a core skill for Data Analysts in 2026.

And most people are not ready for it.

Not because they don’t use AI.
But because they don’t know how to prompt it.

My prediction is that difference between an average analyst and a great one will increasingly be:

𝗛𝗼𝘄 𝘄𝗲𝗹𝗹 𝘁𝗵𝗲𝘆 𝗰𝗮𝗻 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗔𝗜.

I already spend most of my time using AI to accelerate my work and prompting has helped me:

- Identify new opportunities
- Generate analysis faster
- Fix SQL/Python code 

And accelerate my work and increase my impact.

This is why, the earlier you learn how to use it well,
the bigger your edge will be in this market.

———

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Damissah Debrah

Many people still think using AI for Analysis is cheating.

But it’s actually the biggest game-changer of our time.

I used to spend hours pulling one insight.
With AI, I do it 5x faster.

Instead of doing 3 analyses, I can now do 30.
I can be more efficient and deliver more impact.

We call engineers who outperform “10x Engineers.”
It’s time we start talking about 10x Analysts.

Because Analysts who embrace AI will outpace everyone
Not because they’re smarter,
But because they’ve learned to multiply their output.

So please — don’t fight it.

———

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The 5 AI tools I would use as a Data Analyst in 2026:

𝘚𝘢𝘷𝘦 𝘵𝘩𝘪𝘴, 𝘪𝘵 𝘤𝘰𝘶𝘭𝘥 𝘪𝘮𝘱𝘳𝘰𝘷𝘦 𝘺𝘰𝘶𝘳 𝘦𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘺 𝘣𝘺 𝘢 𝘭𝘰𝘵!

→ 𝗚𝗮𝗺𝗺𝗮 𝗳𝗼𝗿 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀

You will use it to turn your slides into clean and better looking presentations. It does matter especially when you have good stuff to share.

→ 𝗠𝗮𝗻𝘂𝘀, 𝗳𝗼𝗿 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵

You will use it to research industries, competitors, and trends to help you identify new opportunities.

→ 𝗖𝗹𝗮𝘂𝗱𝗲 𝗳𝗼𝗿 𝘄𝗿𝗶𝘁𝗶𝗻𝗴 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗺𝘆 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗳𝗮𝘃𝗼𝗿𝗶𝘁𝗲)

You will use it to rewrite your analysis to be clearer and more insightful (𝘵𝘩𝘪𝘴 𝘪𝘴 𝘸𝘩𝘢𝘵 𝘐 𝘶𝘴𝘦𝘥 𝘵𝘩𝘦 𝘮𝘰𝘴𝘵)

→ 𝗡𝗼𝘁𝗶𝗼𝗻 𝗔𝗜 𝗳𝗼𝗿 𝗯𝗿𝗮𝗶𝗻𝘀𝘁𝗼𝗿𝗺𝗶𝗻𝗴 𝗻𝗲𝘄 𝗶𝗱𝗲𝗮𝘀

You will use it to brainstorm analysis directions when it's still rough and you need a figure out what to do.

→ 𝗻𝟴𝗻 𝗳𝗼𝗿 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀

You will use it to automate repetitive like connecting tools, triggering reports, or moving data so you can spend more time on actual analysis.

———

I recommend using these tools to work faster, think better, and become way more efficient.

Because expectations for Data Analysts are just getting higher and higher and you need to build this edge as soon as possible (𝘣𝘦𝘧𝘰𝘳𝘦 𝘪𝘵'𝘴 𝘵𝘰𝘰 𝘭𝘢𝘵𝘦)

———

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Your job search doesn’t have to look like this in 2026.
(𝘚𝘢𝘷𝘦 𝘵𝘩𝘪𝘴! 𝘪𝘵 𝘤𝘰𝘶𝘭𝘥 𝘤𝘶𝘵 𝘮𝘰𝘯𝘵𝘩𝘴 𝘰𝘧𝘧 𝘺𝘰𝘶𝘳 𝘫𝘰𝘣 𝘴𝘦𝘢𝘳𝘤𝘩)

I’ve been testing a tool lately that almost feels like cheating..

𝗝𝗼𝗯𝗖𝗼𝗽𝗶𝗹𝗼𝘁 is a job-search partner working for u 24/7

𝗪𝗵𝘆 𝗶𝘁 𝘀𝘁𝗮𝗻𝗱𝘀 𝗼𝘂𝘁:
↳ Scans 500,000+ company career pages
↳ Match daily positions to your profile
↳ Handles the form-filling for you
↳ Apply to 100s of jobs for you

Not only that it:
↳ Sends a daily recap of everything it did 
↳ Access hiring manager contact details 

📕 Start using it today: https://lnkd.in/giPmXeF2

If you're stuck in being consistent at applying for jobs and tired of getting too many rejections, you should definitely give it a try as JobCopilot takes care of the heavy lifting for you.

———

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If you follow ONE Python roadmap, make it this one.

Most people don’t fail at Python because it’s hard.

They fail because they don't follow a clear path.

They jump between:
→ random Medium posts
→ random YouTube videos
→ random “advanced” tricks on LinkedIn

And hope it works.

𝗕𝘂𝘁 𝗜 𝗰𝗮𝗻 𝘁𝗲𝗹𝗹 𝘆𝗼𝘂, 𝗶𝘁 𝗱𝗼𝗲𝘀𝗻’𝘁.

What actually works is structured learning. 

And after reviewing many resources, the one that actually stand out to me is the Python Fundamentals Track: https://lnkd.in/gzPTdy_9

It teaches you all skills listed in this roadmap in one place, with hands-on learning, taking you from zero (very basics) to advanced Python skills, while working on cool projects.

If it worked for me it can certainly work for you. 
📕 Check it out: https://lnkd.in/gzPTdy_9

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Technical skills alone won’t get you a job in 2026.

Let me explain why: Because in these roles, the competition is too high, which means everyone applying are already great in technical skills: 

SQL, Python, Power BI etc…

The only way to stand out is by having strong product and business skills. Trust me I've been through it.

This includes:
𝗦𝘁𝗿𝗼𝗻𝗴 𝗦𝘁𝗼𝗿𝘆𝘁𝗲𝗹𝗹𝗶𝗻𝗴 → turning data into decisions
𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 & 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗦𝗲𝗻𝘀𝗲 → knowing what matters
𝗖𝗹𝗲𝗮𝗿 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 → influencing people to act

And that will come with a lot of practice. 

Leland is hosting a FREE Product Management Week (Mar 30 - April 2), 8+ virtual sessions to help you build this muscle and I highly recommend you to join. 

Sessions will be hosted by former hiring managers and interviewers at firms like Google, Meta, Microsoft, and more.

So definitely don’t miss out!

📕 RSVP: https://lnkd.in/gQPeNhrG 

Time to take your business skills to the next level!
(𝘌𝘴𝘱𝘦𝘤𝘪𝘢𝘭𝘭𝘺 𝘪𝘯 𝘵𝘩𝘦 𝘦𝘳𝘢 𝘸𝘩𝘦𝘳𝘦 𝘈𝘐 𝘤𝘢𝘯 𝘥𝘰 𝘢𝘭𝘭 𝘵𝘩𝘦 𝘵𝘦𝘤𝘩𝘯𝘪𝘤𝘢𝘭 𝘱𝘢𝘳𝘵)

———

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