↳ For sponsorship, email amneymedia1@gmail.com
↳ Webinars: https://dataford.io/webinars/upcoming
↳ 1:1 coaching: DM me

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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Amney Mounir

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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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The most stable relationship in life is SQL ❤️

- Every interview still has a SQL round
- SQL is still the main tool to pull data
- SQL is still the most reliable language
- SQL isn’t a “nice-to-have", it's a must
- SQL is even more valuable with AI

Happy ValentineSQL Day!

Btw I’m hosting a free workshop this Thursday and you should def join https://lnkd.in/gEhWpp2J

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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 ❤️

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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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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The 5 skills I'd master to be a Data Analyst in 2026:

𝟭/ 𝗦𝗤𝗟 (𝘆𝗲𝘀 𝘁𝗵𝗲 𝗞𝗶𝗻𝗴)

- Ranking Functions (ROW_NUMBER, RANK)
- Window Functions (OVER, PARTITION BY)
- Basic Functions (SUM, AVG, MIN, MAX)
- Joins (LEFT, RIGHT, INNER, SELF)
- Text Analysis (REGEX)
- Percentile (NTILE)

𝟮/ 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗦𝗲𝗻𝘀𝗲

- Measuring Success
- Improving a feature
- Troubleshooting
- Sizing

𝟯/ 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻

- Using the STAR framework to present your projects
- Storytelling (Executive summary/Presentations)

𝟰/ 𝗦𝘁𝗮𝘁𝘀

- Probability (Bayes, Conditional, Central Limit)
- A/B Testing (p-value, sample size, power)

𝟱/ 𝗣𝘆𝘁𝗵𝗼𝗻
- Using Dataframe with Pandas

I'm actually gonna add to this list 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 as this is becoming a larger part of my work as a Data Analyst and I will soon share all the cool tricks I'm using to leverage AI at work.

What would you add?

———
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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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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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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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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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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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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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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 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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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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Junior Analysts with great prompts will outperform all Senior Analysts not using AI in 2026. 

This is my prediction and here is why:

AI removed the bottleneck of execution.
The bottleneck is now thinking.

Before:
Senior Analyst → Great at SQL → faster output

Now:
Anyone can generate SQL
But only some can generate the right analysis.

So the advantage shifted from writing queries and executing faster to framing problems instead. 

Most analysts still write prompts like:
- “find retention”
- “explain the drop”
- “optimize my query”

Top analysts do something different:

They give AI a role + context + decision.

The gap between analysts in 2026 won’t be technical.

It’ll be:
Those who can ask better questions to AI.

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