Sr. Data Analyst @ Crunchbase | Founder @ Big Data Energy⚡️ | LinkedIn [in]structor | Empowering career growth in data | Breaking industry stereotypes

💌 partnerships@bdeanalytics.com
Hi 👋🏼 I’m Jess Ramos, the founder of Big Data Energy ⚡️ and a content creator with a community of over 450,000 followers across LinkedIn, YouTube, Instagram, and Substack. I'm an ex-corporate data analyst and data scientist turned entrepreneur. 

I'm also a Top Voice on LinkedIn and one of the leading voices in B:B marketing in tech partnering with big brands like IBM, Claude, Lyft, and the NFL!

After spending years in high-growth tech startups, I realized my true passion is working for myself as a developer advocate striving towards making tech, data, and AI more accessible to all. Through my business, I offer brand partnerships in the form of social media content, conference attendance, and live events. I also teach SQL and data courses for current and aspiring analysts.  

Highlights:

- Creator of Big SQL EnergyⓇ ⚡️ (the world’s greatest SQL course!)
- Partnered with brands like IBM, AWS, NFL, Atlassian, Lyft, Claude, Notion, and Snowflake
- Advocate for making data careers more accessible & human in an AI-driven world

Brands: partnerships@bdeanalytics.com
🎥 Youtube: @JessRamosData
📸 Instagram: @jessramosdata
💌 Newsletter: https://jessramosdata.substack.com/
🤳🏼 TikTok: @jessramosdata

Jess Ramos, MSBA

Everyone thinks when they get a job in data it'll be the most sexiest job ever full of so many intellectual smart things... 

like coding all day, using AI, and being super techy. 

Basically saving the world one query at a time!

But the reality is that data analytics is still a business job because data analysts are parts of businesses and corporations. 

So much of the day revolves around communication, catering to stakeholders, and driving insights that point to higher-level goals and KPIs.

Things that will make the execs happy and look good in front of the board are ultimately the most important tasks-- not the research and exploring you're excited to do with all of your pent-up curiosity. 

If you don't advocate for those tasks and get buy-in from leadership, they're likely a no-go.

Sometimes, it can be draining, but it makes the quiet days where you can explore data and have fun so much more appreciated. 

Is being a data analyst what you expected or not?

Jess Ramos ⚡️

Tech, Data Science, & AI Content Creator w/ 450K+ followers | Developer Advocate & Solopreneur | LinkedIn Learning Instructor | Big Data Energy⚡️ | helping you leverage tech & AI

A popular (male) data influencer has been stealing content and ideas from me and Dawn Choo  

And I'm not very happy about it. 

They copied my LinkedIn cover photo many months ago stealing my original design of "select * from jess_ramos" pointing to my cover photo. 

I didn't say anything because it's the highest form of flattery and it's not worth my time.

But then a week ago, they copied Dawn's instagram video *word for word* stealing the exact concept, text, and script. 

I commented calling them out and they DMed me saying they "tagged Dawn in the comments giving her credit." 

When I said that still doesn't make it okay to copy people, they changed their whole story and said they "filmed it weeks before and Dawn coincidentally came up with the EXACT same idea” copying them.

And they sent a fool proof screenshot with a timestamp they edited in their phone LOL

How about we stop copying other creators and stealing ideas from women??

And even worse, lying about it?

People talk. Is that really how you want your reputation to be?

Tech, Data Science, & AI Content Creator w/ 450K+ followers | Developer Advocate | Big Data Energy⚡️ | Remote Work & Entrepreneurship

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You'll spend 80% of your time data cleaning.

So don't skip these lessons!

It's not as fun and pretty as AI & ML, but it's the bread and butter you need.

Save these functions for later:

↳ Replace(): replace values
↳ Cast(): change data types
↳ Coalesce(): return first non null value
↳ Ifnull(): replace nulls
↳ Concat(): combine string values

And save these videos to improve your skills:
Data Cleaning w/ SQL & AI: https://lnkd.in/enFBW4_5
Handling Nulls the RIGHT way: https://lnkd.in/ehaQky-Z
Recategorizing data w/ Case Statements: https://lnkd.in/eDPgdcWv

Data cleaning in SQL for data science and data analytics doesn’t have to be hard 🔥

What are your favorite functions for data cleaning??

♻️ repost for your network and save this for later!

Top 5 SQL Data Cleaning Functions

Learning how to set corporate boundaries professionally is truly an art. If you don't learn this... you'll likely be pushed around and taken advantage of. 

These are boundaries that I set for myself (and have accidentally broken in the past!):

1. Do not respond to any notifications or messages outside of your working hours. You never want to set the precedent and expectations that you will— because then if you ever don’t, your manager will feel let down and disappointed in you.

2. Overcommunicate and manage up. If your manager wants to be super hands on, keep them super updated with frequent status updates and decisions. That way they can weigh in if they want to and will build trust in you. And you can keep them from pestering you daily with updates.

3. Take time for yourself and enjoy your personal life (& PTO) GUILT FREE. Just because they have no hobbies doesn’t mean you can’t enjoy your life. You shouldn't feel guilty for using your benefits and not working past your contracted hours.

Have you had a manager like this? Are you healing from one like this?

Tech, Data Science, & AI Content Creator w/ 400K+ followers | Big Data Energy⚡️| Technical Educator | Remote Work & Entrepreneurship

Is data science still the sexiest job of the 21st century?

It’s changed a lot over the years: 

↳ 2012: DS was deemed as the sexiest job of the 21st century— and it was. It required a phd and deep statistical modeling knowledge resulting in very high salaries and demand.  

↳ 2021: This was the rise of bootcamps that over promised and underdelivered and the market became more saturated. However, tech companies were hiring so much, no one really noticed the oversaturation and many people broke in. 

↳ 2026: Hiring has slowed down greatly and so has the demand to become a data scientist. Everything has been in a lull feeling slow, and people are having to adapt their skills to include AI & agents to get jobs. 

The field isn’t dying, it’s just adapting and reshaping into new kinds of roles. 

Skills and tools are changing (as always), and you should only go into a data role if you actually enjoy it!

I think it’s worth it, what about you?

♻️ Repost for your network and 🏷️ save this for later!

Vincent Granville

Chief AI Scientist, GenAItechLab.com

Full-stack entrepreneur with multimillion-dollar exit. Co-Founder at BondingAI.io. Author (Wiley, Elsevier). Creator of open-source Python libraries / Web APIs such as xLLM and GenAI-Evaluation. Builder of high performance in-memory agentic multi-LLMs for professional users and enterprise, with real-time finetuning, self-tuning, no weight, no training, no latency, no hallucinations, no GPU. Made from scratch, leading to replicable results, leveraging explainable AI, adopted by Fortune 100. With a focus on delivering concise, exhaustive, relevant, and in-depth search results, references, and links. 

Vincent is a leading expert, mathematician, patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay.  Vincent also founded and co-founded a few startups, including one with a successful exit (Data Science Central acquired by Tech Target). He is also a top influencer for brands such as Nvidia, SingleStore, and The Linux Foundation.

From 2005 to this day, Vincent successfully arbitraged the real estate market, buying, selling and renting out homes with significant growth and return over time: Building wealth starting from negative net worth when he first arrived in the US in 1995.

Chief AI Scientist

Privately funded GenAI and ML Optimization research lab, creating and disseminating open source intellectual property. Developing explainable and generative AI, synthetic data, computer vision, NLP and generic ML technologies. Check out our certifications, Python libraries, eBooks, articles, courses and podcasts, including our best seller "Synthetic Data and Generative  AI" (Elsevier). Visit our company website at MLtechniques.com.

I’m happy to share that I’m starting a new position as CAIO at BondingAI!

Chief AI Officer. Building architecture, IP, and new AI agents for xLLM, including: • Predictive analytics on retrieved tables from PDFs, databases, the Internet or Excel repositories • Anomaly detection for fraud detection / cybersecurity • Medical data: automatically recovering insights (electrocardiogram data, predicting heart attacks) and data compression • Tabular data synthesis: best and fastest on the market, no deep neural network involved • No-Blackbox deep neural networks and other alternatives (96% correct prediction for next token)

See also new book on these topics, entitled "No-Blackbox, Secure, Efficient AI and LLM Solutions", at https://lnkd.in/g5cmxHE3

See also my alternative to DNNs, where there is no gradient descent, and the minimum of the loss function is obtained in closed form with no iterative algorithm. There is a whole chapter on the topic in my new book, see table of contents at https://lnkd.in/g5cmxHE3

Quantitative Researcher | Author of O’Reilly’s Deep Learning for Finance

Most people default to MSE when training regression models.

That’s fine, until it isn’t.

Mean Squared Error (MSE) aggressively penalizes large errors.
- Great when your data is clean.
- Terrible when it isn’t.

In real financial and economic data, outliers are not bugs, they are features.

That’s where Huber loss quietly does a better job.

What’s the difference?
• MSE squares every error
 → Large mistakes dominate the optimization
 → Models become overly sensitive to rare shocks

• Huber loss behaves differently
 → Quadratic for small errors (like MSE)
 → Linear for large errors (like MAE)

In plain terms:
Huber protects your model from being hijacked by a few extreme observations.

Source: LinkedIn

"If solved, it would destroy modern cryptography."
No, it would not. Modern cryptography does not rely on prime numbers. You get a lot better with irrational numbers. Details in my book 0 and 1 – From Elemental Math to Quantum AI at https://lnkd.in/gQD86nAP

An MIT lecturer just used ChatGPT to "understand" a problem mathematicians haven’t solved in 160 years.

And he failed big time.

Let me explain what actually happened.

He published a piece on Forbes showing his ChatGPT conversations on the Riemann Hypothesis.

It's one of the deepest unsolved problems in mathematics. 
If solved, it could destroy modern cryptography.

And there's a 1 million $ prize for anyone who is capable of doing so. 

The lecturer claimed he didn’t understand it, as he is not a mathematician. But he believed ChatGPT could help.

He tried to prove this by asking very naive and wrong questions to ChatGPT, just like a layman would do.

And in the end, he did not understand the problem.
He just understood ChatGPT’s simplifications of his own confusion.

This is the new failure mode we’re not talking about enough.

1) Generative AI doesn’t increase understanding, it just increases confidence.

ChatGPT is very good at: 
– sounding coherent
– regurgitating internet materials
– producing explanations that feel complete, but are usually wrong

What it CANNOT do is guarantee correctness, depth, novelty, or epistemic understanding.

But the user doesn’t see that. They see fluency.

That’s textbook Dunning–Kruger, now automated.

2) Explaining is different from knowing.

The article proudly shows how GPT can: 
– simplify definitions
– paraphrase known explanations
– restate standard intuitions and basic concepts

There is no mathematical insight or reasoning. Just random words.

The Riemann Hypothesis isn’t unsolved because people don’t understand the math. 

It’s unsolved because existing demonstration attempts failed.

No amount of conversational fluency changes that.

3) AI doesn’t know when it’s wrong, and neither do non-expert users.

ChatGPT doesn’t know:
– which simplifications are harmless
– which nuances are essential
– which mistakes invalidate the reasoning

And users who lack domain expertise cannot tell the difference.

So errors don’t look like errors. They look like insights.
That’s the danger.

The article concludes that ChatGPT helps us understand why the Hypothesis is unsolved.

But the interaction itself shows the opposite: 
– shallow questions
– circular explanations
– analogies that replace rigor
– philosophical confusion dressed as insights

This is just an illusion of understanding.

It's not true that AI lowered the barrier to knowledge. What it did was lowering the barrier to "feeling informed".

And that’s far more dangerous.

Because when everyone believes they understand everything, expertise collapses, standards erode, and confidence detaches from competence even more.

And that’s exactly why automation bias + Dunning-Kruger effect is such a dangerous mix.

Fluency is not understanding. 
And AI is very good at masking the difference.

----
Follow me Chiara Gallese, Ph.D. for more on AI & Tech risks

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