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

There are only 3 levels in SQL.

It took me years as a data analyst to reach the last one.

(you're gonna wanna save this 🏷️)

Here are all the skills you need to know in each level: 

Level 1: Newbie 
(the basic syntax) 
↳ select
↳ from
↳ where
↳ order by 
↳ group by 
↳ aggregate functions
↳ NULLs
↳ basic joins
↳ string manipulation 
↳ date manipulation
↳ arithmetic operators 

Level 2: False Confidence
(intermediate skills)
↳ CTEs
↳ subqueries
↳ set operations
↳ self joins
↳ window functions
↳ views & temp tables 
↳ case
↳ optimization
↳ pivoting & unpivoting 

Level 3: Reality sets in 
(advanced business problems)
↳ recursive CTEs
↳ indexing
↳ data modeling
↳ dealing with stakeholders

Level 4: Dunning-Kruger effect
(imposter syndrome all over again)
↳ realizing skills don’t matter and you still don’t know what you’re doing

♻️ repost this to your network and save this for later!

Jess Ramos

⚡️ Data Science & AI Content Creator w/ 500K+ followers | Developer Advocate & Solopreneur | LinkedIn Learning Instructor | Big Data Energy⚡️ | helping you leverage tech & AI

LinkedIn’s job match feature that helps you strategically focus your job search on all the right roles. Instead of mass applying for ALL the roles you can find, you can now apply for the ones that match your skills and experience the best.

This means that you’re optimizing your job search since you’re applying for roles that you’re MOST qualified for. In this challenging and competitive job market, you truly have to be strategic, data-driven, and work smarter— not harder!

LinkedIn is getting a major upgrade since they’re not JUST recommending roles to you anymore— they’re also telling you how good of a fit it is for you!!

I’m so excited to be partnering with LinkedIn on this since it’s been the biggest platform for my career growth over the past few years 💙

With LinkedIn dare to discover where you best fit with job match: linkedin.com/jobs
#ad #LinkedInPartner

Brand partnership • ⚡️ Data Science & AI Content Creator w/ 500K+ followers | Developer Advocate & Solopreneur | LinkedIn Learning Instructor | Big Data Energy⚡️ | helping you leverage tech & AI

I'll save you about $1M in consulting fees & 12 months of disappointment:

Your data strategy IS your AI strategy.

Most AI transformations are failing not because of the models and technology but because of what's upstream: the data.

Over the years it's been a never-ending cycle of promises:

↳ Machine learning was going to change everything. 
↳ AI was going to change everything. 
↳ Generative AI was going to change everything.
↳ Now Agentic AI is going to change everything.

The technology is never the problem.

Most organizations are sitting on databases full of duplicates, missing fields, undocumented schemas, and business logic that lives entirely inside one person's head who left the company in 2019. 

And that's exactly what you're feeding into these models. And now we're enabling agents to act autonomously at scale with confidence.

Agent confidence is NOT human confidence.

And the most dangerous part is that non-technical (and even some technical stakeholders) don't understand that. They see the little verified check mark, and take it as absolute truth.

We have to fix what's upstream before we can accelerate what's downstream.

♻️ repost to give your network a laugh today!

37% of companies plan to replace entry level roles w/ AI.

But IBM is doing the opposite. 

They're tripling their entry-level roles due to limits of AI adoption.

They're thinking about the longterm strategy instead of the quick financial savings.

Mid-level and senior-level talent doesn't just magically appear later on if you haven't had entry-level roles learning and growing within your company. 

Not only that, but they're rewriting their roles to account for AI adoption and use so people focus more time on the things that are harder to replace with AI.

Turns out implementing AI enterprise-wide is harder than we thought.

⚡️ Data Science, & AI Content Creator w/ 500K+ followers | Developer Advocate & Solopreneur | LinkedIn Learning Instructor | Big Data Energy⚡️ | helping you leverage tech & AI

Yo, I'm Matt Mike 👋

(yes, Mike is my actual last name).

My career path has been far from linear. Here are the cliff notes (that's still a thing, right?)

2015: graduated with an English degree
2016: became a teacher
2017: became a dean of students
2019: left education for B2B sales
2019: hated sales. took a pay cut to become an operations analyst
2021: got promoted to operations supervisor

2022 and onward: became a business analyst (FIRST DATA JOB). Started documenting the journey on social media where I now have a combined following of 100,000+ tech professionals. I've been through a few different data roles with progressive complexity and now do BI consulting while continuing to help others progress their tech careers through content.

Because of my background, I have a passion for helping people with non-technical backgrounds break into tech and advance their careers. My technical passion is business intelligence analytics.

🤝🏻 Brands: mjomike@gmail.com
🎥 YouTube: @mattmike
📩 Newsletter: https://thdatapoint.substack.com/

Matt Mike

I changed careers at 31.

I was a teacher, then a Dean of Students first.

From there, I spent a few years in a non-technical corporate role. But that was my introduction to technical tools.

I decided I wanted to get into data analytics and went for it. The process start to finish took about 6 months.

People often think that you should be settled into a career in your 30s, but it's never too late to pivot from one area to another.

We put a lot of pressure on ourselves to "figure it out" but life isn't always so linear.

If you want a change. Make it happen.

BI Developer & Analytics Consultant 🤘 | YouTube @ Matt Mike | Former Educator & Dean of Students

SQL is a fundamental analytics skill.

It's the language of databases and how we query those databases for the information we need.

My latest read, "𝑆𝑄𝐿 𝑓𝑜𝑟 𝐷𝑎𝑡𝑎 𝐴𝑛𝑎𝑙𝑦𝑡𝑖𝑐𝑠" shows you how to do just that.

But it goes far beyond the basics.

Into things like:

↳ Manipulating data with Python
↳ Presenting, transforming, and updating data
↳ Defining datasets from existing datasets
↳ Window functions
↳ Performant SQL
↳ Processing JSONS and Arrays
↳ Advanced data types
↳ Inferential statistics

And best of all, it's practical.

Big thanks to the authors:

Jun Shan​
Haibin Li
​Matt Goldwasser​
Upom Malik​
Benjamin Johnston

Looking forward to learning some cool stuff :)

Grab the book here if you're interested: https://packt.link/0oLy1

#packtpartner

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