I lived in the United States for over a decade.

I came here first on an F-1 visa. Later, I moved to H-1B.

Like most professionals, I assumed the only realistic path to a green card was EB-2 or EB-3. Long backlogs. Endless waiting. Uncertainty for years, sometimes decades.

At some point, I realized something wasn’t adding up.

Highly skilled professionals were building companies, leading teams, shipping products, and driving real impact. Yet the immigration system treated them as replaceable.

That’s when I stumbled upon the EB-1A pathway.

At first, it sounded like a myth. Something meant only for PhDs, researchers, or Nobel Prize winners.

But instead of dismissing it, I decided to test it.

I spent over 1,800 hours across 18 months studying regulations, reading USCIS decisions, analyzing approvals and denials, and reverse-engineering what actually works.

I built my own EB-1A case from scratch.

And it worked.

I got my EB-1A approval.

That experience completely changed my perspective.

I realized the biggest problem wasn’t eligibility. It was misinformation.

So I started building EB1A Experts.

Not as a content site. Not as a marketing company. But as an execution-first, systems-driven platform to help exceptional professionals navigate EB-1A the right way.

Today, EB1A Experts is a 250+ person company working with professionals from 1,000+ companies across tech, product, engineering, AI, data, and leadership roles.

What we’ve done so far:

→ Helped 260+ individuals secure EB-1A green cards
→ Achieved 200+ EB-1A approvals in 2025 alone
→ Built one of the largest EB-1A-focused teams globally
→ Worked with founders, ICs, managers, and executives, not just academics
→ Scaled with a strong focus on data, process, and long-term credibility

One thing I care deeply about.

EB-1A is not just for PhDs and researchers.

That belief is one of the biggest myths in immigration.

I am living proof. And so are hundreds of our clients.

EB-1A is about high achievements, not job titles. Not degrees. Not publications alone.

My work today focuses on:

• Busting EB-1A myths with real data
• Building scalable legal-tech and immigration systems
• Helping high-impact professionals take control of their future
• Sharing lessons on execution, team building, and long-term thinking

If you’re a founder, operator, or high-skilled professional navigating U.S. immigration. Or if you’ve been told EB-1A is not for you.

You might want to follow along.

We’re building clarity in a space full of noise.

Raghu Reddy

Tampa showed up.

We just wrapped our in-person EB-1A session in Tampa with a room full of founders, researchers, and high-performing professionals focused on one thing: strategy.

No surface-level advice. We broke down what actually strengthens a petition - how to position your work against USCIS criteria, how to document impact properly, and why timing is part of the equation.

If you missed Tampa, we’re heading to Atlanta next!

Today in Atlanta (3–6 PM), we’ll walk through:

• How EB-1A eligibility is evaluated
• Common weaknesses we see in filings
• How to strategically position your profile before submission
• Live Q&A with our team

It’s structured, practical, and designed for professionals who want clarity before they file.

Register here before spots fill up: https://luma.com/1z9fno4g

#EB1A #ImmigrationLaw #ExtraordinaryAbility #Entrepreneurs #TampaEvents #AtlantaEvents

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Setup time

Set up a free evaluation here: https://eb1aexperts.com/eb1a-green-card

At EB1A Experts, we specialize in providing comprehensive support and insights to EB1A aspirants, ensuring they navigate the complex immigration landscape with confidence. Our commitment is to empower individuals with the knowledge and tools they need to successfully pursue their EB1A aspirations. Join us on the path to success with EB1A Experts – your trusted partner in achieving immigration excellence.
Ready to turn your success into U.S. residency? Learn more at https://eb1aexperts.com/ 

Legal Disclaimer

The information provided by EB1A Experts is for educational and informational purposes only. While we strive to offer valuable insights, as we are not a law firm we do not provide legal advice or representation before U.S. immigration authorities.

CEO & Head of AI

As a Product Manager for at Salesforce, I spearheaded the development of several key features that significantly improved the platform's capabilities:

Advanced Natural Language Processing: We integrated the latest NLP techniques into the platform, resulting in a 20% increase in accuracy for text-based insights.
Predictive Modeling: We implemented a new machine learning algorithm that resulted in a 15% increase in the accuracy of predictive models, providing users with more reliable forecasts and recommendations.
Integrated AI Across Core Products: We seamlessly integrated Generative AI into several of our core products in, Service Cloud. This enabled users to leverage AI capabilities in their day-to-day operations, resulting in a 30% increase in the adoption of AI-powered features.

Partnering with the marketing team to create messaging and positioning for the updated Generative AI capabilities
Working closely with sales teams to understand customer needs and pain points and use that information to drive feature prioritization

Senior Manager, Product Management

I built a conversation builder product in less than 2 years that has 500M revenue potential. Conversation builder helps fortune 100 clients build Digital & Voice Conversational AI bots to serve their users. To create this best-in-industry product, I partnered with various stakeholders from C-Suite Executive teams, UX Research, Design, Data scientists, Engineering, Marketing & Sales teams.  The tool decreased the time to market by over tenfold and reduced the cost by over half. 

User research and Design: Collaborate with the user research and the design team to identify the correct user problems and build best user experiences by adopting the design thinking methodologies. 

Roadmap: Prioritize the user problems to define the Roadmap by evaluating the users, market, and competition. Work closely with the CTO, VP, and Senior Director of Product to define the strategy of the business line.

Data science: Define the problem statement and work closely with the Data scientists to solve the user problems with the best ML/AI, Natural Language Processing, and Unsupervised Machine learning models. Democratize these AI solutions across the company.

Engineering: Communicate the Roadmap to the engineering teams and set the Milestones. Work closely with them to make sure we deliver on the Roadmap. 

Product Marketing: Collaborative with the marketing teams for the go-to market strategy of the products.

Strategy: Work with the VP of Strategy to develop a Roadmap for the product.

Senior Product Manager - Conversational AI

Solving the job seeker pain points by improving the search capabilities with ML and NLP. Creating a strategy for the overall growth in the core value of the products. Working with Data science and other engineering teams to build & deploy the Machine learning models which has led to an increase in user engagement by 25% and increasing the revenues by 10%.

• Develop a Product vision, strategy, and sound business plans to build the next generation search. 
• Working with the Product owners across the company to understand how we could incorporate the Machine learning solutions into their products. 
• Working with the data science teams to incorporate the new scalable machine learning technologies to incorporate into the products. 
• A/B testing to increase the user engagement/ satisfaction and LTV of the product.
•  Working with the user research teams to understand the user problems and find out innovative solutions to solve the problems.

Product Manager

Analyzed and streamlined the data from the databases using the ETL tools and BI tools such as Tableau and Microstrategy to build enterprise data visualization and reporting software applications. These applications have resulted in decreasing the costs for the Client's by 10% and increasing the revenues by 5%.

•	Creating use cases and detailed product requirements by working with sales and customer success managers. 

•	Manage Business Development through client engagement, working with cross-functional teams, such as brand marketing, User Interface (UI/UX), Engineering, and QA testing teams. 
• Coordinate Product launches by working with the Marketing team. 

•	Develop process flow diagrams, functional specifications, system design documents, and requirement documentation by working with the respective stakeholders. 

• Work closely with the data science teams to solve user problems using natural language search and machine learning technologies.

Project Manager

• Paint a clear vision of the company and communicate across all the members of the company. 

• Working in establishing the relations with the partners across the country, which has eased in selling the products, by-products produced, and increased the revenues of the company by over 20% in a span of a year.

•Finding different channels to procure the raw material in an efficient way has led to a decrease in the costs by over 10%. 

• Designing and testing the improvements in the manufacturing plant by working with Engineers to increase the efficiency of production. 

• Working with the government agencies to obtain the necessary permissions required for ease of the operations. 

• Making the process flow diagrams to work with the financial institutions and government agencies to facilitate the ease of loans and subsidies.

CEO and Co Founder

Working with the product team to prioritize projects and to maintain a healthy pipeline. Identifying potential improvements to our platforms and communicating them to the appropriate business owners.    
•	Assuming the role of Scrum Master by leading daily stand-ups, Sprint planning and ensuring agile methodology is followed. 
•	Gathering Project requirements, Project Financials and task breakdowns. Software development life cycle and software release process is followed.
•	Utilize project management tools such as Microsoft Project to effectively manage timeline and resources.
•	Overseeing the release process and generating release notes to send to clients detailing the changes made.

Project Manager Intern

Spearheaded the team to make the software’s to increase the life of the polymer recycling machinery. The techniques increased the life of machinery by 10%, which resulted in an increase in sales by 20%.
•	Use the analysis of the data produced from the sensors of Manufacturing Equipment, Polymer Recycling Equipment, and improving the product features and helps Business Intelligence and supply chain management.
•	Making visualization dashboards to represent the modeled data from Excel and SAS for Marketing teams.
•	Analyze Strategy, success Metrics, KPIs  and product performance. 
•	Improving the features and content in the products to be used in the Search Engine Marketing.

Senior Product Manager

Immigrant → EB-1A Green Card | Helping high-impact professionals control their future

We wrapped our in-person EB-1A session in Tampa and Atlanta with a focused, intimate discussion that allowed us to go deeper into real strategy.

We covered:
• How USCIS evaluates EB-1A evidence
• Where strong candidates unintentionally weaken their case
• How to position your work before filing
• Extended, practical Q&A around real scenarios

When the setting allows for real dialogue, the strategy gets sharper and that’s exactly what happened in Atlanta.

Next and final stop: Charlotte at 4:30 PM EST. If you want clarity before you file, join us in Charlotte.

Register here: https://luma.com/8w799rg0

#EB1A #ImmigrationLaw #ExtraordinaryAbility #Entrepreneurs #AtlantaEvents #CharlotteEvents

Charlotte closed out the series strong.

Our Senior Paralegal Manager, AINOY RINTHALUKAY, joined us for the final in-person EB-1A session in Charlotte — and it was our biggest turnout yet.

The room was engaged, sharp, and ready to get into real strategy. We spent the afternoon breaking down how USCIS evaluates evidence, where strong candidates unintentionally weaken their cases, and how to position a profile before filing.

The caliber of questions across this entire series reinforced something we keep seeing: professionals aren't looking for hype. They're looking for clarity.
Grateful to everyone who joined us in Tampa, Atlanta, and Charlotte and thank you, Alicia Zajaczkowski, for moderating all three events and keeping everything running smoothly.

#EB1A #ImmigrationLaw #ExtraordinaryAbility #Entrepreneurs #CharlotteEvents

Seasoned Business Immigration Professional

Marketing and Graphic Design

EB1A Experts has grown to 250+ employees, and we're just getting started. We need our next key leader.

We're Hiring: Vice President of Operations: Hyderabad, India

We're looking for a VP of Operations to lead our India office. You'll oversee Sales, Marketing, Customer Success, and Operations — reporting directly to me.

We need someone with good leadership experience, a track record of scaling operations, and the ability to partner with our US team across time zones.

If this sounds like you — or someone you know — reach out or apply here: https://lnkd.in/gYcwVyS7

You would not believe this.

A good friend of mine and his brother both got EB-1A approved.

Same family. Same timeline. Two green cards.

Here's the thing most people miss:

They weren't Nobel laureates.
They weren't CEOs of Fortune 500 companies.
They weren't famous.

But they were each the best at what they did in their niche.


Both 

→ Documented every contribution
→ Collected recommendation letters from industry leaders
→ Built a narrative that showed sustained impact

The result? Two approvals. Same household celebrating together.

EB-1A isn't about being famous. It's about being exceptional in YOUR lane.

If two brothers from the same family can both qualify, what's stopping you?

Disclaimer: This content is for informational purposes only and does not constitute legal advice.

The March 2026 Visa Bulletin just dropped.
And it's one of the most significant months we've seen in years.

Here's what happened 👇

🔥 EB-2 India Dates for Filing: +11 MONTHS
From Dec 2013 → Nov 2014.
That's the largest single-month jump in recent memory.
If your priority date falls in this window, file your I-485 NOW.

🎉 EB-2 Rest of World: NOW CURRENT
Was Oct 15, 2024 last month.
Now it's completely current.
If you're EB-2 from anywhere outside China — you can file TODAY.

☝️ EB-1 India & China Dates for Filing: +4 MONTHS
Jumped from Aug 2023 → Dec 2023.
EB-1 continues to move faster than any other category.
This is exactly why EB-1A is the smartest path for most professionals.

📊 EB-2 ROW Final Action: +6.5 MONTHS
Moved from Apr 2024 → Oct 2024.
Significant forward movement across the board.

But here's the part most people miss:

Look at the GAP between Dates for Filing and Final Action.

EB-2 India: 14-month gap
→ You CAN file I-485 and get EAD + travel permit
→ But your green card itself may take over a year more

EB-1 India: 9-month gap
→ Much smaller gap = faster to actual green card

EB-1 ROW: BOTH current
→ File and get your green card. Period.

This is why EB-1A changes the game.

While EB-2 India applicants celebrate an 11-month jump
to reach November 2014...

EB-1 India is already at December 2023.

That's a 9-YEAR difference.

And with EB-1A:
→ No employer sponsorship needed
→ No PERM labor certification
→ No waiting for your company to "start the process"
→ Premium processing in 15 business days

⚠️ One important warning:
The State Department noted that these large movements are partly because consular visa processing has slowed due to recent administration actions. If processing picks back up, retrogression could happen later this fiscal year.

Translation: Don't wait. File now if you're eligible.

The window may not stay open. 

Want to know if you qualify for EB-1A? We've helped 250+ professionals get their green cards through EB-1A.

Book a free consultation → https://lnkd.in/gwH47EiG

Follow Raghu Reddy for monthly visa bulletin breakdowns and EB-1A insights.

3 cities. 3 days. The biggest EB-1A event ever. Period.

🎯 Free LIVE Info Session: Obtaining a Green Card Through the EB-1A Visa Path

Join me as I share my journey and walk you through the EB-1A process, including eligibility, evidence strategy, timelines, and common pitfalls.

The session also includes a live Q&A to address your questions.
📍 Tampa, FL – Saturday, Feb 28 | 3:00–6:00 PM EST - 
https://luma.com/r510t1fy
📍 Atlanta, GA – Sunday, Mar 1 | 3:00–6:00 PM EST - https://luma.com/1z9fno4g
📍 Charlotte, NC – Monday, Mar 2 | 4:30–7:00 PM EST - https://luma.com/8w799rg0

Whether you’re in tech, research, business, healthcare, or emerging fields - this session will help you understand if EB1A is the right path for you.

Disclaimer: This session is for general informational purposes only and does not constitute legal advice. Individual results may vary.

My hands were shaking.

I just got off the phone with a client.

His EB-1A was approved.

With only 10 days to spare before his daughter turned 21.

Let me explain why this matters:

When a child of an EB-1A applicant turns 21, they "age out."

They can no longer ride on the parent's green card application.

They'd have to start their own immigration journey from scratch.

This father had been fighting for a long time, Delays. Uncertainty.

His daughter was 20 years, 355 days old when the approval came.

10. Days.

Think about that.

The difference between your child getting a green card and having to restart the entire process from zero was 10 days.

This is why I tell every parent:

→ Don't wait until it's "the right time"
→ File early. Build your profile NOW.
→ Every month you delay is a month your child can't get back.

The immigration system doesn't care about your family. You have to.

Disclaimer: This content is for informational purposes only and does not constitute legal advice.

I want to be honest for a second.

A client once told me: "Raghu, I don't have the money. But I need EB-1A."

That conversation changed how I think about immigration.

Here's what most people don't realize about EB-1A costs:

Now compare it to:

→ Staying on H-1B for 10-20 more years waiting for EB-2/EB-3 
→ Lost promotions because you can't switch jobs freely
→ The stress of depending on one employer for your legal status
→ Your kids aging out at 21

The math:
10 years of career limitations 

I told that client: "Let's figure this out."

We built a payment plan.
We prioritized the strongest evidence first.
We filed strategically.

6 months later: Approved.

Free to switch jobs. Free to start a business. Free.

EB-1A isn't an expense. It's the best career investment you'll ever make.

Disclaimer: This content is for informational purposes only and does not constitute legal advice.

My lawyer friends are going to kill me for this...

But someone needs to say it out loud:

You don't need to be "extraordinary" to file for the Extraordinary Ability green card. You just need the right strategy.

And I'm flying to 3 cities in 3 days to prove it.

🎤 Free LIVE Info Session: Obtaining a Green Card Through the EB-1A Visa Path
I'll break down the full EB-1A process — eligibility, evidence strategy, timelines, and the costly mistakes most people make — plus a live Q&A where you can ask me anything.

📍 Tampa, FL – Sat, Feb 28 | 3–6 PM EST
https://luma.com/r510t1fy
📍 Atlanta, GA – Sun, Mar 1 | 3–6 PM EST
https://luma.com/1z9fno4g
📍 Charlotte, NC – Mon, Mar 2 | 4:30–7 PM EST
https://luma.com/8w799rg0

Tech. Research. Business. Healthcare. Emerging fields — doesn't matter.

If you've been sitting on the sidelines thinking "that's not for someone like me," this session is literally built for you.

Disclaimer: This session is for general informational purposes only and does not constitute legal advice. Individual results may vary.

10 things you've been told about EB-1A criteria that are completely wrong.

Let me set the record straight.

USCIS lists 10 criteria for EB-1A. You need to meet 3.

But here's what nobody tells you:

Myth 1: "You need awards" → You need RECOGNITION, not trophies.
Myth 2: "More criteria = better" → Quality of evidence matters more than quantity.
Myth 3: "Publications mean papers" → Media coverage counts too.
Myth 4: "You need 100+ citations" → Context matters more than raw numbers.
Myth 5: "Judging means formal panels" → Peer review counts.
Myth 6: "Original contribution means patents" → Open-source, frameworks, methods all count.
Myth 7: "High salary proves extraordinary ability" → It helps but isn't sufficient alone.
Myth 8: "You need a PhD" → No education requirement for EB-1A.
Myth 9: "Only researchers qualify" → Engineers, founders, artists, athletes all can.
Myth 10: "You need to be globally famous" → Top of YOUR niche is enough.

The 3 most commonly approved criteria in 2025:

→ Critical or leading role (Criterion 6) 
→ Authorship of scholarly articles (Criterion 7)
→ Judging the work of others (Criterion 4)

67% approval rate in Q3 2025. Still strong.

But 40-50% of applications now receive RFEs.

The bar is rising. Your evidence needs to be airtight.

Disclaimer: This content is for informational purposes only and does not constitute legal advice.

I couldn't believe it.

A friend of mine got his EB-1A approved.

His priority date is current.
He's been in the US only 5 years.

Let me put that in perspective:

Most Indian-born professionals on EB-2 or EB-3 wait 10-15 years for a green card.

Some wait 20+.

My friend? Five years. Total.

From landing in the US to green card in hand.

Here's what made the difference:

He didn't wait to "become extraordinary."
He started documenting his work from Day 1.

→ Every conference talk — saved
→ Every publication — tracked citations
→ Every peer review invitation — documented
→ Every media mention — archived

By year 3, he had more than enough evidence.
By year 4, he filed.
By year 5, approved. The priority date is current for the rest of the world.

The EB-1 category has NO per-country backlog for most applicants.

While EB-2 India is stuck at 2014 priority dates, EB-1 India is at 2023. 

That's a 9-year difference. 

EB-1A isn't just faster. It's a completely different timeline.

Disclaimer: This content is for informational purposes only and does not constitute legal advice.

It felt like a punch to the gut.

"We have issued a Request for Evidence on your EB-1A petition."

40-50% of EB-1A applications now receive an RFE.

If you got one, don't panic.

Here's what I've learned from handling 260+ EB-1A cases:

The top 5 reasons USCIS issues RFEs:

1. Weak "final merits" argument
You met 3 criteria but didn't show WHY your work matters at a national level.

2. Vague recommendation letters
"She is excellent at her job" means nothing.
"Her algorithm reduced processing time by 40% across 3 Fortune 500 deployments" means everything.

3. No context for achievements
USCIS officers aren't experts in your field. They need YOU to explain significance.

4. Outdated evidence
Awards from 10 years ago with nothing recent signals a stale profile.

5. Quantity over quality
20 recommendation letters ≠ strong case.
5 targeted letters from credible sources = strong case.

The good news?

60% of RFE responses result in approval.

That's the same rate as applications that never got RFEs.

An RFE is not a denial. It's a second chance to make your case.

Disclaimer: This content is for informational purposes only and does not constitute legal advice.

Did you know that EB-2 NIW approval rates collapsed by 46% in one year?

From 79.99% in FY 2023 to 43.31% in FY 2024.

Meanwhile, EB-1A held steady at 67%.

Let that sink in.

For years, immigration attorneys told people:

"EB-2 NIW is easier. Start there."

That advice is now outdated.

Here's the current comparison:

EB-1A:
→ 67% approval rate (Q3 2025)
→ 15-day premium processing
→ No per-country backlog (ROW = current)
→ Self-petitioned
→ No employer needed

EB-2 NIW:
→ 54% approval rate (Q3 2025)
→ 45-day premium processing
→ Significant backlogs (India/China)
→ Self-petitioned
→ Stricter "national interest" standard now applied

The math is clear.

If you qualify for EB-1A, there is no reason to choose EB-2 NIW instead.

EB-1A has:
→ Higher approval rates
→ Faster processing
→ Shorter wait times
→ Better outcomes

Stop listening to 2023 advice in 2026.

The landscape has changed. Your strategy should too.

Disclaimer: This content is for informational purposes only and does not constitute legal advice.

📊 February EB-1A Approval Recap

Momentum continues.

✅ 11 approvals in Feb

Where the approvals came from:

🔹 AI & Machine Learning: 36% Search, personalization, real-time decision systems, AI governance.

🔹 Cloud & Distributed Systems: 27% Database technologies, BI infrastructure, performance engineering.

🔹 Specialized Engineering: 27% Semiconductor design, optical AR/VR, embedded systems for automated power.

🔹 Enterprise Technologies: 9% SAP, financial cloud integration.

Experience profile:

📈 Mean (avg) experience: 14.5 years 📊 Median experience: 14 years. Half below 14. Half above.

What stands out this month:

AI and ML dominated again — over a third of all approvals. But this month also showed strong representation from highly specialized fields like semiconductor physical design, optical science for AR/VR, and embedded systems for automated power.

You don't have to be in software to qualify. If your work is pushing a field forward, EB-1A is built for you.

The data keeps proving the same thing. It's not about the title. It's about how your contributions are framed, documented, and presented.

If you're a senior engineer, architect, or tech leader wondering whether EB-1A is realistic for you — the data says yes, if the story is built right.

Set up a call using the link: https://lnkd.in/gwH47EiG

Happy to share more insights.

Disclaimer: Content and any other data shared are for informational purposes only and do not constitute legal advice.

Someone has to say this.

The H-1B system in 2026 is fundamentally different from what it was 2 years ago.

Here's what changed:

1. Weighted lottery is HERE.
Starting FY 2027 (March 2026 registration):
→ Level 4 wage = 4 entries (107% higher odds)
→ Level 1 wage = 1 entry (48% LOWER odds)

If you're early career, your chances just got cut in half.

2. $100,000 proclamation fee.
Per new H-1B petition. Upheld by federal court in December 2025.

Your employer now pays $100K+ just to sponsor you.

Think they'll do that for a mid-level role?

3. Social media screening.
H-1B and H-4 applicants must now disclose social media accounts.
Accounts must be set to public during adjudication.

4. Processing holds.
USCIS placed holds on adjudications for nationals of "high-risk" countries.

The old playbook of "get H-1B → wait for green card" is breaking.

This is why I tell every tech professional:

If you have extraordinary achievements, EB-1A is your path out.

→ No employer sponsorship needed
→ No lottery
→ No $100K fee
→ 15-day premium processing

The system is changing. Your strategy should too.

Disclaimer: This content is for informational purposes only and does not constitute legal advice.

Data Scientist | 150K LinkedIn | BCBS Of South Carolina | SQL | Python | AWS | ML | Featured on Times Square, Favikon, Fox, NBC | MS in Data Science at UConn | Proven record in driving insights and predictive analytics |

Lead Data Analyst/Data Scientist at Blue Cross Blue Shield of South Carolina, driving revenue growth and operational efficiency through predictive analytics, experimentation, and machine learning.

I build end-to-end data solutions that influence executive decision-making, from churn prediction models and A/B testing frameworks to executive dashboards tracking multimillion-dollar portfolios. My work spans the full ML lifecycle: data pipelines, feature engineering, model deployment, and stakeholder communication.

What Sets Me Apart:

I don't just analyze dat, I translate it into business strategy. Whether it's identifying high-risk customer segments, optimizing conversion funnels through rigorous experimentation, or presenting insights to C-suite leadership, I bridge the gap between technical depth and business impact.

Beyond the 9-to-5:

I'm on a mission to democratize data science education. With 220K+ professionals in my LinkedIn community and 20K+ newsletter subscribers, I share battle-tested insights on SQL, Python, machine learning, and breaking into data science. My content focuses on real-world applications that actually matter in industry, not academic theory.

I've also been recognized nationally:
- Featured in Times Square, Fox, NBC, and major media outlets for career journey
- 3rd Place, Travelers NESS Statathon
- 1st Place, Well4Tech Health Hackathon  
- Favikon recognition for data community contributions

My Technical Stack:

Python (Pandas, Scikit-Learn, XGBoost), SQL, PySpark, R | AWS (S3, Glue, Athena, SageMaker), Azure, Snowflake, Databricks | Tableau, Power BI, Looker | A/B Testing, Bayesian Statistics, Predictive Modeling

Open to discussing data strategy, collaboration, or your journey into data science.

Let's connect: saikumarbysani13@gmail.com

Venkata Naga Sai Kumar Bysani

A close friend I’ve known for 9 years was just laid off.

She’s on H-1B and has 55 days to find a new role or leave the country.

The job market right now is brutal. Layoffs everywhere. Hiring freezes. Hundreds of applicants for every role. And for people on work visas, this isn’t just stressful, it’s existential.

I’m sharing this on behalf of Bhavana Gurram

𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐬𝐡𝐞’𝐬 𝐝𝐨𝐧𝐞:
↳ Software Developer at Paycom - built tax filing systems across all 50 U.S. states and 4 international regions
↳ Reduced manual effort by 67% through automation during peak filing periods
↳ Previously at Deloitte - created automation saving 300+ hours/month and built notification systems handling 400k+ messages monthly
↳ Master’s in CS from UT Dallas (3.91 GPA)
↳ Bachelor’s from NIT India - cleared JEE Mains (top 0.01%)

𝐓𝐞𝐜𝐡 𝐬𝐭𝐚𝐜𝐤: Java, C#, Python, React, Spring Boot, .NET, MySQL

She’s looking for Full Stack Developer or Software Developer roles.

I’ve known Bhavana for almost a decade. She’s not just talented, she’s the kind of person who makes teams better.

If this resonates and you’re in a position to help, please reach out or pass this along.

And if not, even a simple like, comment, or repost helps more than you think.

𝐂𝐨𝐧𝐭𝐚𝐜𝐭 𝐝𝐞𝐭𝐚𝐢𝐥𝐬:
↳ LinkedIn: https://lnkd.in/dqmAsrHw
↳ Email: bhavanagurram14@gmail.com
↳ Portfolio: https://lnkd.in/d5r_3SBe

55 days.
Let’s help her find the right team.

♻️ Please repost, it could truly change everything.

Data Scientist | 200K+ Data Community | 3+ years in Predictive Analytics, Experimentation & Business Impact | Featured on Times Square, Fox, NBC

Resume

16 Data Analytics Concepts Every Analyst Should Know in 2026


If you want to break into data analytics (or level up this year), learn these concepts:

𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬:
1 SQL Joins and Window Functions
↳ https://lnkd.in/dapaFjWv

2 Data Cleaning with Python (Pandas)
↳ https://lnkd.in/dzzzFBvM

3 Exploratory Data Analysis (EDA)
↳ https://lnkd.in/dPhTBNm3

4 Statistical Significance & Hypothesis Testing
↳ https://lnkd.in/d-nbprUd

𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠:
5 Dashboard Design Principles
↳ https://lnkd.in/dAG2iAFj

6 Data Storytelling for Stakeholders
↳ https://lnkd.in/dXa6BmtU

7 Choosing the Right Chart Type
↳ https://lnkd.in/dWqGE9ed

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐦𝐩𝐚𝐜𝐭:
8 Defining KPIs and Metrics That Matter
↳ https://lnkd.in/dY85xhkB

9 Cohort Analysis
↳ https://lnkd.in/dqcPRH-v

10 Funnel Analysis
↳ https://lnkd.in/d2qgN2rm

11 A/B Testing & Experimentation
↳ https://lnkd.in/di4p3EKg

𝐌𝐨𝐝𝐞𝐫𝐧 𝐓𝐨𝐨𝐥𝐬 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞:
12 dbt for Data Transformation
↳ https://lnkd.in/d7bszA99

13 Cloud Data Warehouses (Snowflake, BigQuery)
↳ https://lnkd.in/dh3A-QPG

14 Version Control with Git
↳ https://lnkd.in/dGucMpYm

𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:
15 Prompt Engineering for Data Analysis
↳ https://lnkd.in/df6pbWxB

16 Using LLMs as a Thinking Partner
↳ https://lnkd.in/dKW_vgMt

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
SQL and Python are still non-negotiable. But in 2026, the analysts getting hired know how to combine technical skills with AI tools and business thinking.

Master the fundamentals. Then stack the modern skills on top.

What else should make this list? 👇

Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp

♻️ Repost to help someone starting their data analytics journey

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6

If you're preparing for a data analyst/data science role (in 1 month or less), you must read these case studies!


1. 𝐍𝐞𝐭𝐟𝐥𝐢𝐱'𝐬 𝐀/𝐁 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦
→ Run 1000+ experiments simultaneously
→ Make data-driven decisions for 230M+ subscribers
→ Statistical rigor with automated analysis pipelines

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dsBsHNqT

2. 𝐏𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭'𝐬 𝐕𝐢𝐬𝐮𝐚𝐥 𝐒𝐞𝐚𝐫𝐜𝐡 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧
→ Built SearchSage to map queries directly to their PinSage embeddings
→ Processes 250M+ visual searches monthly with sub-second latency
→ Increased engagement by 42% for related searches

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dFxS_RWh

3. 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧'𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞
→ Powers 800M+ member profiles with Pro-ML platform
→ Frame feature store ensures consistency across training & serving
→ Photon-Connect automates advanced AI model creation at scale

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/d27PeXEY

4. 𝐒𝐥𝐚𝐜𝐤'𝐬 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐌𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞
→ Handles 2B+ messages daily across millions of channels
→ Achieves <500ms global delivery using stateful Channel Servers
→ Scales to 16M channels per host with consistent hashing

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/d8ZYqFzX

5. 𝐋𝐲𝐟𝐭'𝐬 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐢𝐜𝐢𝐧𝐠 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦
→ Uses online reinforcement learning for real-time price optimization
→ Balances supply/demand while maintaining service reliability
→ Improved ETAs by 25% through marketplace optimization

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/deienatS

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐟𝐨𝐜𝐮𝐬 𝐨𝐧:

• What metrics and KPIs drove business decisions
• How they handled data quality and governance
• Statistical methods and ML algorithms chosen
• Tools and tech stack for specific use cases

Don't get distracted by fancy algorithms; start with the business problem.

Most successful analytics projects solved core issues with clear metrics and thoughtful experimentation.

Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp

♻️ Save it for later or share it with someone who might find it helpful!

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6

Stop applying to jobs manually in 2026. 

These 5 websites will save you hours every week. 

(Save this!)

𝟏. JobCopilot
↳ Auto-applies to dozens of jobs per day while you sleep 
↳ Scans 500,000+ company career pages daily for new openings 
→ https://lnkd.in/dEVsCgSA

𝟐. Jobright.ai
↳ AI matches you to jobs based on your skills, not just titles 
↳ According to JobRight, users report 2x more interviews with tailored resume suggestions 
→ https://lnkd.in/dB-jxX_z

3. Teal 
↳ Scores your resume against job descriptions with keyword matching 
↳ Built-in job tracker + 40 email templates for follow-ups 
→ https://lnkd.in/d-gM8gTv

4. Simplify
↳ One-click autofill for job applications (free Chrome extension) 
↳ 200M+ applications submitted, and candidates reportedly hear back more often 
→ https://simplify.jobs

𝟓. Hunter.io
↳ Find recruiter and hiring manager emails for direct outreach 
↳ Free tier available with 25 searches per month 
→ https://hunter.io

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬: 
→ Use JobCoPilot or JobRight for volume 
→ Use Teal to tailor your resume for roles you really want 
→ Use Hunter/Apollo to reach out directly when you find your dream job

𝐏𝐫𝐨 𝐓𝐢𝐩: Don't rely on just one tool. Stack them. Auto-apply for volume, tailor for quality, and reach out directly to stand out.

The job market is brutal right now. Work smarter, not harder.

Which of these have you tried? Any others I should add? 👇

♻️ Repost to help someone stuck in application hell

If I had to learn Python for Data Science in 60 days, this is the roadmap I'd follow.

(Most people overcomplicate this. Don't)

You don't need to master everything. You need to master what matters for data roles. Here's the exact path:

𝐖𝐞𝐞𝐤 𝟏-𝟐: 𝐏𝐲𝐭𝐡𝐨𝐧 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬
↳ Variables, data types, loops, conditionals
↳ Functions and lambda expressions
↳ List comprehensions

𝐖𝐞𝐞𝐤 𝟑: 𝐂𝐨𝐫𝐞 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐬
↳ Lists, dictionaries, sets, tuples
↳ When to use each one
↳ This is your foundation

𝐖𝐞𝐞𝐤 𝟒-𝟓: 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬
↳ NumPy for numerical operations
↳ Pandas for data wrangling (spend extra time here)
↳ Matplotlib & Seaborn for visualization

𝐖𝐞𝐞𝐤 𝟔: 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 & 𝐄𝐃𝐀
↳ Mean, median, mode, standard deviation
↳ Correlation and distributions
↳ Exploratory data analysis techniques

𝐖𝐞𝐞𝐤 𝟕-𝟖: 𝐌𝐋 𝐁𝐚𝐬𝐢𝐜𝐬 & 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬
↳ Scikit-learn fundamentals
↳ Data preprocessing and cleaning
↳ Build 2-3 projects to solidify everything

The key is to code every single day. Even 30 minutes of hands-on practice beats 3 hours of passive learning.

𝟓 𝐅𝐫𝐞𝐞 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐭𝐨 𝐆𝐞𝐭 𝐒𝐭𝐚𝐫𝐭𝐞𝐝:
1. Kaggle Learn (free Python & Pandas courses) → https://lnkd.in/dVbp-MtE
2. freeCodeCamp Python for Data Science (YouTube) → https://lnkd.in/dCX4z9rQ
3. Real Python tutorials → https://realpython.com/
4. Python for Data Analysis by Wes McKinney (book) → https://lnkd.in/dBGpzfqD
5. DataCamp's free intro course → https://lnkd.in/dZzqAbeY

♻️ Repost to help someone starting their Python journey

📘 Prepping for data analyst interviews? Check out the book I co-authored with Pritesh and Amney with 150+ real questions: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share tips on data analytics & data science in my free newsletter. Join 23,000+ readers → https://lnkd.in/dUfe4Ac6

Generative AI for everyone

Tableau Desktop Specialist

Coursework:
Introduction to Deep Learning
Business Decision Modeling
Big Data Analytics with Cloud
Intro to Project Mgmt
Data Mgmt and Business Process
Tech Comm in BAPM
Statistics in Business Analytics
Adv Bus Analytics and Pro Management
Predictive Modeling
Data Science using Python
Project Risk Management and Agile methodologies
Data Mining and Business Intelligence
Project Leadership and Communications

•	Extracted Tweets with 'sntwitter' and pre-processed the data using NLTK packages. Built a deep learning model that can emulate the rule-based 'Vader Sentiment Intensity Analyzer' and can conduct sentiment analysis.
•	Achieved an R-square value of 0.7 and an MAE of 0.07 and 0.05 for positive and negative sentiment scores.

Sentiment Analysis on Tweets

•	Spearheaded a project team of 5 and conducted an analysis to deliver data-driven recommendations, resulting in 100% project completion within timeline.
•	Enabled granular data results by leveraging GeoPandas and ZCTA5 shapefiles to extract Zip Codes of listings.
•	Coded a scalable dashboard in Tableau, enabling users to assess 1.5M Airbnb listings risk from state level to zip codes.

Impact of Airbnb on housing supply

•	Researched and scrapped stock data of 9 companies from 3 sectors, from Yahoo Finance for last 5 financial years.
•	Discovered the most-favourable investments, by employing regression analysis and Monte-Carlo simulation to assess risk-return trade-off.
•	Evaluated 3 prominent investment strategies, Buy & Hold, Momentum Trading and S&P 500, and picked Buy & Hold strategy with an ROI of 15% in FY2022.
•	Determined optimal portfolio allocation by performing a comprehensive risk and parameter analysis.

Portfolio Allocation Model

•	Reengineered XG Boost Model for Churn Prediction, resulting in 81% accuracy with an AUC of 0.69 from 66% and 0.60.
•	Accomplished a potential 21% increase in revenue, by conducting a cost-benefit analysis, and presenting the same to senior stakeholders.
•	Minimized customer churn by 10%, by recognizing key contributors to customer churn via data analysis and implementation of corrective actions with team.
•	Awarded 3rd prize out of 150 competing teams, with a $300 incentive.

Customer Churn Analysis for an US Insurance Company, Travelers

Data Scientist | 75K LinkedIn | BCBS Of South Carolina | SQL | Python | AWS | ML | Featured on Times Square, Favikon, Fox, NBC | MS in Data Science at UConn | Proven record in driving insights and predictive analytics |

Lead Data Analyst with a passion for driving data-driven strategies, predictive modeling, and team optimization across various industries including insurance and retail. With over 2 years of experience in data science and analytics, I have a proven track record in reducing churn, improving efficiency, and contributing to millions in annual revenue.

Currently, I lead data initiatives at BCBS Of SC, where I develop dashboards, optimize workflows, and build predictive models to support C-Suite decision-making and business strategies. My work spans multiple tools like SQL, Python, AWS, and Power BI to streamline processes and enhance operational efficiency.

Career Highlights:

•	Optimized workflows, saving 120+ hours of manual work per week, driving efficiency through AWS integration.
•	Built a churn prediction model reducing customer churn by 12%, leading to $7M in retained annual revenue.
•	Collaborated with cross-functional teams on A/B tests, improving policy conversion rates by 12%.
•	Developed real-time dashboards at The LEGO Group, improving sales forecasting accuracy by 15% and decision-making speed by 23%.
•	Recognized for building predictive models, earning recognition in data science competitions like NESS Statathon (3rd place out of 150 participants).

Beyond my work, I've had the privilege of competing and winning several hackathons and data science competitions, including earning 1st prize at Well4Tech’s Data Science Health Hackathon, and NESS Statathon by Travelers. My innovative work has been featured on Times Square billboards, and covered by major media outlets like Fox, NBC, and over 20+ articles. Additionally, I’ve been recognized by Favikon for my contributions to the data community.

I have experience working with a range of data science tools like PySpark, SQL, Power BI, and Python, and hold certifications such as Tableau Desktop Specialist and AWS Machine Learning. As a strong believer in mentorship and continuous learning, I’ve also contributed as a Graduate Teaching Assistant for SQL and led the UConn Data Analytics Club, fostering growth and development within the community.

Feel free to reach out if you’d like to discuss data analytics, data science, or potential collaboration: saikumarbysani149@gmail.com.

I just got back from San Francisco.

And I'm still processing what happened. I left with some of the best memories of my life.

𝐌𝐚𝐧𝐢𝐟𝐞𝐬𝐭 𝐆𝐚𝐥𝐚 brought together some of the most incredible people I've ever met in one room. Creators, founders, and attendees with O1s and EB1s. People building real things.

But here's what hit me the hardest:

𝐓𝐡𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 𝐢𝐧-𝐩𝐞𝐫𝐬𝐨𝐧 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐢𝐬 𝐮𝐧𝐦𝐚𝐭𝐜𝐡𝐞𝐝.

I finally met Deedy Das and Marina Mogilko. I've watched almost every one of Marina's podcasts. Following her content for so long, and then actually meeting her in person? Surreal.

I met the Tech Roast Show crew. Been watching them roast tech people forever. Now we were in the same room laughing together.

But what made this trip unforgettable was the 2 days I spent with fellow creators:

↳ Ferry rides across the bay
↳ Getting to know each other beyond the content
↳ Playing Mafia until 3 AM (I found out I absolutely suck at it. I cannot lie.)
↳ Late-night food runs
↳ Conversations about personal journeys, professional struggles, and everything in between

From Thursday to Saturday, most of us barely slept 4 hours a day.

And honestly? Some of the best days of my life.

You can DM someone for months. Comment on their posts. Engage with their content.

But nothing compares to being in the same room. Sharing a meal. Laughing at 3 AM over a game you're terrible at.

If you ever get the chance to show up in person, take it.

The connections you make will change everything.

So glad that I got to meet you all: Pritesh, Vishakha, Sohan, Varun, Roshni Chellani, Shreya, Sriram, Chintan, Amulya, Muskan, Jugal, Shreya, Narayan and more! You guys are the best!!

The H-1B lottery rejected me twice.

I thought I'd have to leave the country.

(If this is your story, keep reading.)

The uncertainty. The anxiety every March. The constant "what ifs."

Here's what changed everything for people I know: finding the right community.

That's why I'm part of the 𝐄𝐱𝐭𝐫𝐚𝐨𝐫𝐝𝐢𝐧𝐚𝐫𝐲 𝐀𝐛𝐢𝐥𝐢𝐭𝐲 𝐂𝐥𝐮𝐛 (EAC).

It's a private, invite-only community of 1,100+ immigrants working toward or who've already received O-1 and EB-1A visas.

𝐖𝐡𝐨'𝐬 𝐢𝐧𝐬𝐢𝐝𝐞:
→ Members from Google, OpenAI, Meta, Amazon, and fast-growing startups
→ Trailblazers actively building their visa cases
→ Extraordinary visa recipients who've been through it

𝐖𝐡𝐚𝐭 𝐲𝐨𝐮 𝐠𝐞𝐭:
→ Attorney AMAs (ask anything about your case)
→ Mentorship from people who've done it
→ In-person dinners, happy hours, and co-working sessions
→ "Work on your visa" cohorts with attorneys walking through real strategies

𝐔𝐩𝐜𝐨𝐦𝐢𝐧𝐠 𝐞𝐯𝐞𝐧𝐭𝐬:
→ The Extraordinary Gala in San Francisco (March 5)

𝐓𝐡𝐞 𝐛𝐨𝐚𝐫𝐝 𝐢𝐧𝐜𝐥𝐮𝐝𝐞𝐬:
→ Emilio Gonzalez (Former USCIS Director)
→ Deedy Das (Partner at Menlo Ventures)
→ Perry Rhew (Former Federal Judge)

𝐀𝐩𝐩𝐥𝐲 𝐡𝐞𝐫𝐞 → https://lnkd.in/dpNRkhk5

𝐌𝐲 𝐭𝐚𝐤𝐞:

The technical skills got us here. But community and connections help us stay and thrive.

You don't have to figure out the visa maze alone.

♻️ Repost to help an immigrant in your network find their people

Nobody tells you that job searching is a full-time job.

I learned that after 1,300 applications.

I was an immigrant. Fresh out of school. No US experience. Pivoting from electrical engineering to data science. In a brutal market.

Most applications went into a black hole. Ghosted by recruiters. Rejected by ATS systems before a human ever saw my resume.

I didn't know how to network properly. Didn't know what hiring managers actually looked for. Didn't know how to tell my story in a way that made sense.

I figured it out eventually. But it took way longer than it should have.

If you're in the middle of a job search right now, you don't have to go through that alone.

Leland is hosting a free event next week:

𝐉𝐨𝐛 𝐒𝐞𝐚𝐫𝐜𝐡 𝐖𝐞𝐞𝐤 (Feb 23-26)

↳ 8+ live sessions on everything from resumes to salary negotiation
↳ Networking strategies that actually work
↳ Interview prep from people who've been on the other side
↳ Led by former hiring managers from Apple, Google, and Coinbase

It's free. And it's built for people who are serious about landing their next role.

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞 → https://lnkd.in/d2p6FdKm

How many applications deep are you right now? 👇

♻️ Repost to help someone who's stuck in the job search grind.

Data Scientist | 3+ years in Predictive Analytics, Experimentation & Business Impact | 200K+ Data Community | Featured on Times Square, Fox, NBC

If you're not using AI tools as a data analyst in 2026, someone who is will take your job.

Here are 7 ways to future-proof your data analyst career:

1️⃣ 𝐖𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐀𝐈 𝐭𝐨𝐨𝐥𝐬, 𝐝𝐨𝐧'𝐭 𝐟𝐢𝐠𝐡𝐭 𝐭𝐡𝐞𝐦
↳ Copilot, Claude, Codex. Learn to use them well.
↳ Make a plan first, guide the tool, check the output.
↳ They amplify your output, freeing you for deeper analysis.

2️⃣ 𝐒𝐐𝐋 𝐚𝐥𝐨𝐧𝐞 𝐰𝐨𝐧'𝐭 𝐜𝐮𝐭 𝐢𝐭 𝐚𝐧𝐲𝐦𝐨𝐫𝐞
↳ Pick up Python. Learn some ML basics.
↳ Understand how pipelines work.
↳ The role is growing; ensure your skills grow faster.

3️⃣ 𝐁𝐞 𝐭𝐡𝐞 𝐩𝐞𝐫𝐬𝐨𝐧 𝐰𝐡𝐨 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐬 𝐭𝐡𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬
↳ Tools can crunch numbers.
↳ You're the one who knows what those numbers mean for the team, the customer, the revenue.
↳ That's where your irreplaceable value lies.

4️⃣ 𝐒𝐨𝐥𝐯𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐭𝐚𝐬𝐤𝐬
↳ Nobody cares if you used Spark or Pandas.
↳ They care if you found the issue, fixed the leak, saved the money.
↳ Focus on outcomes.

5️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐡𝐚𝐭 𝐫𝐮𝐧 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐲𝐨𝐮
↳ Dashboards are fine.
↳ Automated alerts, self-updating reports, scheduled pipelines? Better.
↳ Make your work do work while you sleep.

6️⃣ 𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐢𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐥𝐢𝐤𝐞 𝐲𝐨𝐮'𝐫𝐞 𝐭𝐚𝐥𝐤𝐢𝐧𝐠 𝐭𝐨 𝐲𝐨𝐮𝐫 𝐦𝐚𝐧𝐚𝐠𝐞𝐫'𝐬 𝐦𝐚𝐧𝐚𝐠𝐞𝐫
↳ Clear beats clever.
↳ If you can't explain what the data says in plain language, the insight dies in your notebook.

7️⃣ 𝐒𝐡𝐚𝐫𝐞 𝐲𝐨𝐮𝐫 𝐰𝐨𝐫𝐤 𝐩𝐮𝐛𝐥𝐢𝐜𝐥𝐲
↳ Post what you're learning. Talk about projects. Help others.
↳ This builds trust, opens doors, and puts you on people's radar before you even apply.

The analysts who truly thrive will be more than just SQL experts. They'll be problem-solvers who deeply understand the business, communicate clearly, and adapt rapidly to new tools and challenges.

Which of these are you focusing on right now? 👇

♻️ Repost to help a data analyst in your network.

It was great collaborating with Anas Riad on this one 🙌

You can learn data analytics for FREE this week!

DataCamp just made all 600+ courses in SQL, Python, Power BI, and more, FREE for one week.

This doesn't happen often. Don't miss it.

I've been using DataCamp for years. What I like: their skill tracks actually align with what hiring managers look for, not outdated theory that collects dust. I've gone through a lot of their content.

Here are my favorites if you're learning SQL, Python, or Power BI:

𝐒𝐐𝐋:
→ SQL Fundamentals Track: https://lnkd.in/dtFYQcG6
→ Data Analyst in SQL Career Track: https://lnkd.in/dNRzrWwk

𝐏𝐨𝐰𝐞𝐫 𝐁𝐈:
→ Power BI Fundamentals Track: https://lnkd.in/dCFAxii7
→ Data Analyst With Power BI Career Track: https://lnkd.in/dM6C5YaB

𝐏𝐲𝐭𝐡𝐨𝐧:
→ Python Data Fundamentals Track: https://lnkd.in/d5pd6E5f
→ Data Analyst with Python Career Track: https://lnkd.in/dBSSMdC3

𝐁𝐨𝐧𝐮𝐬:
→ Projects to practice: https://lnkd.in/dgEiZE7A
→ Browse all Free Week courses: https://lnkd.in/dKmMi5Ds

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
SQL and Python are non-negotiable for data roles. Power BI is a huge plus if you're going into analytics.

Start with SQL if you're new. It's the foundation of everything.

Save this post. Share it with someone who's learning.

Paid partnership with DataCamp.

♻️ Repost to help someone break into data

𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 21,000+ here → https://lnkd.in/dUfe4Ac6

90% of SQL interviews test the same 10 window functions.

(If you know them, you're already ahead)

Most analysts write 15-line subqueries for problems that window functions solve in 3 lines. Interviewers notice.

Here are the 10 SQL window functions every data analyst and data scientist should know:

𝟏. 𝐑𝐎𝐖_𝐍𝐔𝐌𝐁𝐄𝐑()
↳ Assigns a unique sequential number to each row
↳ Use it for: deduplication, pagination, finding first/last records per group

𝟐. 𝐑𝐀𝐍𝐊()
↳ Ranks rows with ties getting the same rank, leaves gaps after
↳ Use it for: leaderboards, finding top N with ties

𝟑. 𝐃𝐄𝐍𝐒𝐄_𝐑𝐀𝐍𝐊()
↳ Ranks rows without leaving gaps, even with ties
↳ Use it for: ranking when you need consecutive numbers

𝟒. 𝐋𝐀𝐆()
↳ Returns the value from the previous row
↳ Use it for: day-over-day changes, comparing to previous period

𝟓. 𝐋𝐄𝐀𝐃()
↳ Returns the value from the next row
↳ Use it for: forecasting, calculating time between events

𝟔. 𝐒𝐔𝐌() 𝐎𝐕𝐄𝐑
↳ Calculates running or cumulative totals
↳ Use it for: running revenue, cumulative user growth

𝟕. 𝐀𝐕𝐆() 𝐎𝐕𝐄𝐑
↳ Computes moving or rolling averages
↳ Use it for: 7-day rolling averages, smoothing trends

𝟖. 𝐍𝐓𝐈𝐋𝐄()
↳ Divides rows into N equal groups
↳ Use it for: quartiles, percentiles, bucketing users

𝟗. 𝐅𝐈𝐑𝐒𝐓_𝐕𝐀𝐋𝐔𝐄()
↳ Fetches the first value in an ordered window
↳ Use it for: first purchase date, earliest event per user

𝟏𝟎. 𝐋𝐀𝐒𝐓_𝐕𝐀𝐋𝐔𝐄()
↳ Fetches the last value in an ordered window
↳ Use it for: most recent activity, latest status per group

I've seen these exact functions show up in take-home assignments at Google, Meta, and Amazon. Master them before your next interview.

Which window function do you use most? 👇

♻️ Repost to help someone ace their next SQL interview

📘 If you're preparing for data analyst interviews, check out the book I co-authored with Pritesh and Amney, 150+ real interview questions with solutions: https://lnkd.in/dyzXwfVp

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 23,000+ readers here → https://lnkd.in/dUfe4Ac6

SQL window functions

Kanishk Jain

You can't analyze data you don't have.
You can't model the data you don't have.
You can't make decisions with data you don't have.

Here's how to get it.

The best datasets aren't on Kaggle.
They're not waiting in your company's database.
They're sitting on websites - unstructured and untouched.

Job listings. Product prices. Competitor intel. Customer reviews.

This is the data that actually moves the needle. And it's not coming to you in a clean CSV.

Building scrapers from scratch with BeautifulSoup or Selenium?

Great for learning. Painful at scale.

You end up managing proxies, CAPTCHAs, anti-bot detection, JavaScript rendering, and constant maintenance when sites change.

The faster path? Use a scraping API.

You send a URL + parameters. It handles everything and returns structured data.

I've been using 𝐎𝐱𝐲𝐥𝐚𝐛𝐬 Web Scraper API.

𝐖𝐡𝐲 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬:
↳ Handles IP blocks and CAPTCHAs automatically
↳ Returns clean JSON, no parsing headaches
↳ Works on Amazon, Google, job boards, any public site
↳ You only pay for successful results

𝐓𝐫𝐲 𝐢𝐭 𝐟𝐫𝐞𝐞: Scrape up to 2K results at no cost.
→ https://lnkd.in/dj8wBePd

What would you scrape first? 👇

♻️ Repost to help someone stuck debugging scrapers

How to Scrape Data from Web

Save this post to save months of job search!

(Not Kidding)

Most job seekers waste 80% of their time on the wrong job boards.

By the time you see it on job boards, hundreds have already applied.

I've been testing a tool that honestly feels like cheating (the good kind): 𝐉𝐨𝐛𝐂𝐨𝐩𝐢𝐥𝐨𝐭.

𝐖𝐡𝐚𝐭 𝐢𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐝𝐨𝐞𝐬:
↳ Monitors 500,000+ company career pages 24/7
↳ Finds jobs before they hit the mainstream boards
↳ Auto-fills those mind-numbing application forms
↳ Answers screening questions in your voice
↳ Tracks everything so you never lose an application
↳ Automatically finds emails and LinkedIn of Hiring Managers (New Feature)

𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐤𝐢𝐜𝐤𝐞𝐫:
While you're sleeping, it's applying. While you're networking, it's applying. While you're prepping for interviews, it's still applying.

One candidate told me they got 5 interviews in their first week. Not because they're special. Because they applied to jobs nobody else had seen yet.

Start using JobCopilot here: https://lnkd.in/dEVsCgSA

While JobCopilot handles the heavy lifting, you focus on what humans do best:
↳ Building genuine connections
↳ Perfecting your interview skills
↳ Networking at events
↳ Preparing killer presentations

Your competition is still copying and pasting into the same forms. You could be getting callbacks from jobs they haven't even found yet.

Stop playing the numbers game manually when AI can play it better.

♻️ Repost to help job seekers skip the struggle.

How I Built A Community of 233K people on LinkedIn

90% of ML projects never make it to production. Here's the 8-step framework that works.

𝐒𝐭𝐞𝐩 𝟏: 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐏𝐫𝐨𝐛𝐥𝐞𝐦
↳ Start with WHY, not HOW
↳ Is ML even the right solution?
↳ Define success criteria upfront

𝐒𝐭𝐞𝐩 𝟐: 𝐃𝐚𝐭𝐚 𝐂𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 & 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧
↳ Check data quality: missing values, duplicates, outliers
↳ EDA: distributions, correlations, patterns
↳ Document your data sources and limitations

𝐒𝐭𝐞𝐩 𝟑: 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠
↳ Handle missing values (imputation, dropping)
↳ Encode categorical variables
↳ Create new features from domain knowledge
↳ This alone can improve performance by 20-30%

𝐒𝐭𝐞𝐩 𝟒: 𝐓𝐫𝐚𝐢𝐧-𝐓𝐞𝐬𝐭 𝐒𝐩𝐥𝐢𝐭 & 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧
↳ Split: 70% train, 15% validation, 15% test
↳ Use stratified split for imbalanced data
↳ Never touch test data until final evaluation

𝐒𝐭𝐞𝐩 𝟓: 𝐌𝐨𝐝𝐞𝐥 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 & 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠
↳ Start simple (logistic regression, decision tree)
↳ Try XGBoost, LightGBM, Random Forest
↳ Track experiments with MLflow or W&B

𝐒𝐭𝐞𝐩 𝟔: 𝐌𝐨𝐝𝐞𝐥 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧
↳ Use appropriate metrics (F1, ROC-AUC, RMSE)
↳ Analyze errors: confusion matrix, feature importance
↳ Does 85% accuracy actually solve the business problem?

𝐒𝐭𝐞𝐩 𝟕: 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭
↳ Build API endpoint (FastAPI, Flask)
↳ Containerize with Docker
↳ Deploy to cloud (AWS, GCP, Azure)

𝐒𝐭𝐞𝐩 𝟖: 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 & 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞
↳ Track prediction accuracy over time
↳ Monitor for data drift and concept drift
↳ Retrain periodically with fresh data

𝐂𝐨𝐦𝐦𝐨𝐧 𝐏𝐢𝐭𝐟𝐚𝐥𝐥𝐬 𝐭𝐨 𝐀𝐯𝐨𝐢𝐝:
❌ Data leakage (using future info to predict past)
❌ Ignoring class imbalance
❌ Deploying without monitoring
❌ Optimizing metrics without business context

𝐏𝐫𝐨 𝐭𝐢𝐩: Your first end-to-end project will be messy, that's normal. Focus on completing the full cycle, then iterate.

𝐖𝐚𝐧𝐭 𝐭𝐨 𝐬𝐭𝐚𝐫𝐭 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐋?

Here are 5 resources I recommend:
1. Machine Learning by Andrew Ng - https://lnkd.in/diqSeD-k
2. Codebasics ML Playlist - https://lnkd.in/dBiYAeN7
3. Krish Naik ML Playlist - https://lnkd.in/dcpAS5gA
4. StatQuest with Joshua Starmer - https://lnkd.in/dhZ3aVhf
5. Sentdex ML Tutorials - https://lnkd.in/dCFPtDv8

Which step do you find most challenging? 👇

♻️ Repost to help someone starting their ML journey

Building an End to End Machine Learning Project

The H-1B lottery rejected me.

I thought I'd have to leave the country.

(If this is your story, keep reading.)

The H-1B lottery closes in 6 days. Thousands of immigrants are about to face the same uncertainty: stay, leave, or find alternatives.

There is an alternative. The O-1 Visa.

No lottery. No cap. No random draw.

You need to meet 3 out of these 8 criteria:

1. 𝐀𝐰𝐚𝐫𝐝𝐬 - Recognition for excellence
2. 𝐌𝐞𝐦𝐛𝐞𝐫𝐬𝐡𝐢𝐩𝐬 - Selective, invitation-based groups
3. 𝐏𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐖𝐨𝐫𝐤 - Your contributions shared publicly
4. 𝐉𝐮𝐝𝐠𝐢𝐧𝐠 - Reviewing others' work
5. 𝐎𝐫𝐢𝐠𝐢𝐧𝐚𝐥 𝐖𝐨𝐫𝐤 - Innovative contributions with impact
6. 𝐌𝐞𝐝𝐢𝐚 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 - Coverage in credible outlets
7. 𝐇𝐢𝐠𝐡 𝐒𝐚𝐥𝐚𝐫y - Top-tier earnings in your field
8. 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐑𝐨𝐥e - Key, high-impact positions

Most tech professionals already meet several of these. They just haven't documented them properly.

𝐌𝐚𝐧𝐢𝐟𝐞𝐬𝐭 𝐋𝐚𝐰 helps turn your existing achievements into a strong O-1 case.

𝐆𝐞𝐭 𝐲𝐨𝐮𝐫 𝐟𝐫𝐞𝐞 𝐞𝐥𝐢𝐠𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐜𝐨𝐧𝐬𝐮𝐥𝐭𝐚𝐭𝐢𝐨𝐧 → https://lnkd.in/dSJnAg7b

That hackathon win? Counts.
Your tech blog? Counts.
Leading that critical project? Counts.

The H-1B lottery closes March 19. Start building your backup plan now. 

I got mine picked on my second attempt. I hope you get yours too. But if you don't, just know there are other paths forward.

All the best, everyone.

♻️ Repost to help someone going through this anxiety

I just analyzed 2 months of my Instagram data in 45 minutes. 5 insights I wish I knew sooner.

No SQL. No Python. Just plain English questions.

It's an open-source AI analyst system built on Claude Code - 18 specialized agents, 39 skills, all markdown. 

You connect your data, ask a business question, and it frames it, explores your data, finds the root cause, builds a narrative, and hands you a branded slide deck with speaker notes.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐢𝐭 𝐟𝐨𝐮𝐧𝐝:

1. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐑𝐞𝐞𝐥𝐬 = 𝐦𝐲 𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐞𝐧𝐠𝐢𝐧𝐞
↳ 8% average engagement rate (highest on my account)
↳ 406 new followers per post

2. 𝐉𝐨𝐛 𝐒𝐞𝐚𝐫𝐜𝐡 𝐑𝐞𝐞𝐥𝐬 = 𝐦𝐲 𝐯𝐢𝐫𝐚𝐥 𝐫𝐞𝐚𝐜𝐡 𝐞𝐧𝐠𝐢𝐧𝐞
↳ 187K average views per post
↳ 3x more reach than anything else
↳ One post hit 700K views

3. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐒𝐭𝐨𝐫𝐲 𝐂𝐚𝐫𝐨𝐮𝐬𝐞𝐥𝐬 = 𝐜𝐮𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞𝐦 𝐢𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞𝐥𝐲
↳ 44 saves per post (1/40th of Job Search content)
↳ Not worth the production time

4. 𝐍𝐞𝐯𝐞𝐫 𝐩𝐨𝐬𝐭 𝐨𝐧 𝐓𝐡𝐮𝐫𝐬𝐝𝐚𝐲𝐬
↳ 27K reach on Thursdays vs 140K on Wednesdays
↳ Same content. Completely different results.

5. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐒𝐭𝐨𝐫𝐲 𝐑𝐞𝐞𝐥𝐬 = 𝐔𝐒 𝐚𝐮𝐝𝐢𝐞𝐧𝐜𝐞 𝐦𝐚𝐠𝐧𝐞𝐭
↳ 56% US viewers on average
↳ Critical for brand deals and course sales

𝐒𝐦𝐚𝐥𝐥 𝐬𝐚𝐦𝐩𝐥𝐞 𝐜𝐚𝐯𝐞𝐚𝐭: Two months of data means these are directional signals, not statistically proven conclusions. But the patterns are clear enough to act on.

This kind of analysis would have taken me days. It took 45 minutes.

My content team (Muskan Agarwal, Cherry Media) is already using this report to plan next month's content.

Thanks for building this, Shane, Sravya, and Hai. They're also running a free session tomorrow on analyzing product data with Claude Code. 

𝐑𝐞𝐠𝐢𝐬𝐭𝐞𝐫 𝐡𝐞𝐫𝐞: https://lnkd.in/d5WX3Cwz
𝐅𝐮𝐥𝐥 𝟓-𝐰𝐞𝐞𝐤 𝐈𝐧-𝐃𝐞𝐩𝐭𝐡 𝐜𝐨𝐮𝐫𝐬𝐞: https://lnkd.in/d9puN3rP

(Repo link and Full report in the comments section 👇)

What would you analyze first if you had this kind of tool? 

♻️ Repost to help a creator or analyst drowning in data

The most common DM I've gotten this month isn't just about data analytics.

It's "How do I break into cybersecurity?"

I'm not a cybersecurity expert. But I know what it's like to start from zero in a completely new field.

I switched from electrical engineering to data science while moving to a different country. No background. No connections. Just a decision to figure it out.

So when people kept asking, I did what I always do: I researched the best starting point.

That's how I found 𝐓𝐫𝐲𝐇𝐚𝐜𝐤𝐌𝐞.

𝐖𝐡𝐚𝐭 𝐢𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞𝐥𝐲 𝐬𝐭𝐨𝐨𝐝 𝐨𝐮𝐭:
↳ 100% hands-on, browser-based labs (no downloads, no setup)
↳ Zero multiple-choice questions
↳ Learn by actually doing the work, not memorizing theory

Over 6.5 million people are already learning on it. There's a reason for that.

And if you get stuck, there's 𝐄𝐜𝐡𝐨, TryHackMe's AI tutor available 24/7. It doesn't just give you answers. It walks you through the thinking and helps you actually understand. For someone starting from zero, that's huge.

You can start completely free. But if you're serious about building a career in cyber, Premium unlocks everything: all learning paths, faster lab machines, and certification prep.

If you've been sitting on the question "where do I even start?" this is your answer.

👉 𝐒𝐭𝐚𝐫𝐭 𝐲𝐨𝐮𝐫 𝐟𝐢𝐫𝐬𝐭 𝐥𝐚𝐛 𝐭𝐨𝐧𝐢𝐠𝐡𝐭: https://lnkd.in/d9bUvVvt (Use code SAI25 for 25% off annual subscription)

No expensive equipment. No CS degree. Just an internet connection and the willingness to start.

♻️ Repost if someone in your network has been asking about breaking into cyber.

Replit 4 is finally here!!

The line between coding and creating keeps getting thinner.

AI agents are becoming collaborators, not just tools.

This is going to change how we build. Can’t wait to test it out :)

Founder at Replit

Software isn’t merely technical work anymore. It’s creative.

Introducing Replit Agent 4. The first AI built for creative collaboration between humans and agents.

Design on an infinite canvas, work with your team, run parallel agents, and ship working apps, sites, slides & more.

https://lnkd.in/gUCayN4H

80% of a data analyst's and data scientist's time is spent on data cleaning.

And most of that time? Fixing mistakes that shouldn't have happened.

(Save this before your next project!)

After years of working with messy datasets, I've seen the same 9 mistakes destroy analyses over and over again.

𝟏. 𝐍𝐨𝐭 𝐂𝐡𝐞𝐜𝐤𝐢𝐧𝐠 𝐟𝐨𝐫 𝐃𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬
↳ Duplicate rows inflate counts and throw off every metric downstream
↳ Run df.duplicated().sum() before anything else

𝟐. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬
↳ NULLs don't just cause errors, they cause wrong answers that look right
↳ First ask WHY the data is missing, then decide how to handle it

𝟑. 𝐖𝐫𝐨𝐧𝐠 𝐃𝐚𝐭𝐚 𝐓𝐲𝐩𝐞𝐬
↳ Numbers stored as text sort alphabetically, not numerically
↳ Run df.dtypes early. Fix types before you do anything else

𝟒. 𝐍𝐨𝐭 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐳𝐢𝐧𝐠 𝐓𝐞𝐱𝐭
↳ "USA", "U.S.A", and "United States" are three separate values to your code
↳ Lowercase everything, strip whitespace, map known variations

𝟓. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐎𝐮𝐭𝐥𝐢𝐞𝐫𝐬
↳ One $1M transaction in a dataset with $100 average will destroy your mean
↳ Plot your distributions before you aggregate anything

𝟔. 𝐁𝐚𝐝 𝐉𝐨𝐢𝐧𝐬
↳ A many-to-many join silently multiplies your rows
↳ Always check row counts before and after. Know your join keys cold

𝟕. 𝐍𝐨 𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐢𝐨𝐧 𝐂𝐡𝐞𝐜𝐤𝐬
↳ Negative ages, future birthdates, revenue below zero, these slip through constantly
↳ Add simple assertions for expected ranges and valid categories

𝟖. 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐢𝐧𝐠
↳ "Why did we drop 10,000 rows?" If you can't answer that six months later, it's a problem
↳ Log every decision: what you changed, why, and what you ruled out

𝟗. 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 𝐀𝐟𝐭𝐞𝐫 𝐘𝐨𝐮'𝐯𝐞 𝐁𝐮𝐢𝐥𝐭 𝐓𝐡𝐢𝐧𝐠𝐬
↳ Finding a data issue after your dashboard is live means rebuilding everything
↳ Clean first. Explore second. Build last. Every time.

𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬:
Bad data in, bad report out. Bad data in, bad model out. Build the habit of checking these 9 things before every analysis, your future self will thank you.
♻️ Repost to help a data professional avoid these mistakes

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