🎯 Mark Freeman II

Gable

• Joined as employee-one to lead GTM at Gable to help us reach our first customers.

• Reported directly to the CEO and led the development and execution the go-to-market
strategy which resulted in a successful launch, 4000+ leads, and 300+ meetings with
senior data engineers and leaders, ranging from scaleups to Fortune 500 companies—
this was integral for our ability to raise a Series A. (https://bit.ly/4eDwbPE)

• Co-authored the O’Reilly book "Data Contracts: Developing Production Grade Pipelines at Scale,"
contributing all the technical chapters, and positioning Gable as thought leaders of an
emerging category within the data market. (https://bit.ly/3XIu4mF)

• Served as a technical expert by presenting at conferences on data engineering topics and providing thought leadership via blogs.

• Built Gable’s marketing and sales stack with an emphasis on data capture, automation,
and analytics that enabled automated qualification, conversion into meetings, and nurture
campaigns for leads—while establishing the underlying data model.

• Served as a sales engineer, mapping technical requirements to Gable’s product roadmap,
building champions among directors plus data leaders, creating data strategy decks for
clients, and closing multiple accounts.

• Served as a data scientist, providing insights and visualizations for Gable’s executive team
for presenting sales metrics to the board and potential investors.

Tech Lead, GTM Engineering

On the Mark Data

I help brands connect to data professionals via content strategy, GTM strategy, and influencer marketing. Learn more at onthemarkdata.com.

• Built a brand with an audience of 40k+ data professionals via social media content, technical blogs, and video-- which has harnessed over 14MM impressions.

• Established the weekly Scaling DataOps newsletter, with 7k+ subscribers, where I interview data leaders and executives on data best practices in scaling data infrastructure.

• Built a revenue stream around sponsored content from top data brands such as Nvidia, SuperDataScience, Scale AI, and much more.

Founder

O'Reilly

Currently writing a book on data contracts.

⭐️ "This book has the potential to become the definitive guide for data contracts! I can't wait to dive into the rest of it and unlock even more valuable insights!"

⭐️ "The initial read is clear and provide a good potted history of the data problem and how it has organically morphed due to so many factors. I can't wait for the rest of the book"

⭐️ "This book has the potential to become the bible of data contracts especially as the world moves towards intelligent applications. Data is essential to applications that use AI, and having a good contract is paramount towards establishing trust between systems and having a seamless experience. Looking forward to the full publication!"

Author - Data Contracts: Developing Production Grade Pipelines at Scale

LinkedIn

⭐️ "Took a few hours out of my Saturday to dive into data engineering with DBT, and had the opportunity to pair program with Mark... Great work on the new course, Mark. I sure learned a lot, and I'm confident others will benefit from it as well. As someone who's relatively new to data engineering, this short course was an eye-opener. It expanded my understanding of the scalability and possibilities within the field."

⭐️ "I completed Mark's course over the weekend and thought it was a GREAT introduction to using DBT. For people looking for a low-stakes, easy-to-follow introduction to DBT as a tool for data engineering, I think you would be pressed to find something more effective."

⭐️ "I would like to thank Mark... for creating such a comprehensive, engaging & pure hands on course on dbt. He explained the concepts clearly & demonstrated them with practical examples and exercises."

⭐️ "I was excited when I saw this course on your page, i know this is going to be the course I needed to start my DBT journey and you did not disappoint, Thank you"

LinkedIn Learning Instructor, Data Engineering

Humu

• Build ETL pipelines to bring third-party data sourced from APIs to combine with our product data, creating rich data assets to drive product feature creation and customer analytics.

• Built buy-in, developed the transition plan, and led the migration of the orchestration of data transformations from manually created scheduled queries to data build tool (dbt)-- which enabled version control of our semantic layer, data quality checks, and data discoverability of data assets within our data warehouse.

• Led the development of an internal data quality engine to increase test coverage of core data pipelines and implement data observability.

Senior Data Engineer

Driving forward the organization’s data strategy by:
• 1) Conducting user interviews to understand the organization’s data pain points.
• 2) Identifying the need for data marts to increase data access.
• 3) Scoping overhaul of our data pipelines and warehouse.
• 4) Sourcing data ingestion vendors.
• 5) Fostering leadership buy-in via building minimum viable products.

Senior Data Scientist (Data Engineering)

• Led the development of the Data Science team’s custom R package and managed the internal distribution and upgrading.

• Implemented CI/CD utilizing Docker and CircleCI to ensure changes to the Data Science codebase passes tests and prevent breaks to our internal packages.

• Assisted in doubling the Data Science team via interviewing, onboarding, and technical mentorship for upskilling colleagues.

Senior Data Scientist

• Developed and put into production an NLP pipeline via Python that takes raw text data, classifies respective texts' themes, and ranks and assigns text to relevant insights within customer reports (https://bit.ly/3yAeBqu).

• Designed and put into production via Python an algorithm to classify user activity—output became org’s main product health metric and was validated with both statistical significance and UX research interviews (https://bit.ly/3yt5QhY).

• Support the VP of Product in strategic product decisions by refining business questions, conducting log analytics via SQL and R, and creating reports shared org-wide.

Data Scientist

IndexoRx

• Made it to the final round of interviews for Y Combinator, where we presented our pitch deck to their investment team and described the business opportunity within the pharmacy space in reducing the burden of DIR fees.

• Developed an MVP of our data product that utilized analytics and machine learning to rank users at risk for lowered medication adherence and forecast DIR fees.

• Conducted deep market research on pharmacy business models, pharmacy supply chains, and PBM reimbursement processes.

Founder, Chief Data Officer

Bay Area Community Health Advisory Council

• Lead organization’s evaluation efforts through developing a standardized survey utilizing CSAT and NPS scores, transitioning administration from paper to Google Forms, and providing analysis and data visualizations of services data—resulting in a 24% increase in survey response rate and 2 reports used by org’s Executive Director for fundraising

Member / Evaluation & Analytics Committee Chair

Verana Health

• Utilize deep understanding of medical claims and EHR data, in conjunction with strong SQL and PySpark skills, to work with data that contains over 63 million patients, 274 million patient encounters, and over 10 billion data points amongst 55 separate EHRs

• Developed and deployed into production an algorithm via Apache Spark that classified insurance types for over 250 million records, utilizing unstructured EHR data, that improved both accuracy from 40% to 84% and reduced null values from 16% to <1%

• Support sales team in 21 potential contracts by determining viability of our data assets through defining research and business questions, developing SQL and PySpark code to build cohorts, providing insights on quality issues within data, and creating data reports to communicate findings to non-technical staff

• Conduct an algorithm assessment on a key variable and identified errors impacting 1.3 billion records, as well as provide recommendations to the algorithms team to rectify the error—assessment was utilized to help certify new data architecture

Data Scientist (Data Quality)

Stanford University School of Medicine

• Developed R script for department’s ETL process, that transforms data into cleaned variables and removed duplicate observations based on defined logic—reducing data cleaning time of analysts by 10 hours per project

• Completed an exploratory analysis through conducting 48 multiple regression models to understand relationship between biometric data and self-reported wellbeing data, thus identifying 3 viable hypotheses to further investigate and ensuring the lab diverts resources to impactful studies 

• Updated methods of a previous propensity score match analysis, lowering the SDM for all variables from 0.17-0.21 to <0.10, resulting in the analysis now meeting required match assumptions and improving the manuscript strength for publishing: matching accounts for observed confounder bias

Data Analyst

Collective Health

• Developed and operationalized a Python script, utilizing pandas, that identifies discrepancies between insurance enrollment data and network partner data— reduced manual work process time from 98 to 16.3 hours a year (time saved grows with number of added clients)

• Developed and operationalized a Python script, utilizing pandas, that takes 3 sources of data and creates custom reports for respective insurance partners’ specification— reduced manual work process time from 52 to 4.3 hours a year

• Utilize SQL to pull necessary data to resolve insurance eligibility issues and other related operations work

Operations Associate

onthemarkdata.com | Senior data engineer helping brands with GTM and content strategy.

I'm a community health advocate turned data engineer interested in the intersection of social impact, business, and technology. My life’s mission is to improve the well-being of as many people as possible through data—especially among those marginalized.
 
As the founder of On the Mark Data, I use this platform to help brands connect to data professionals through captivating content.
 
I received my M.S. from the Stanford School of Medicine and I'm also certified in Entrepreneurship and Innovation from the Stanford Graduate School of Business. In addition, I have worked within numerous startups where I have put machine learning models into production, integrated data analytics into products, and led migrations to improve data infrastructure.
 
The culmination of my technical skills, content success, and experience within startups would make me an asset whether you are looking to create a successful content strategy, engage in influencer marketing campaigns, or develop a go-to-market strategy for your startup.

You can learn more about opportunities to collaborate, my media appearances, or my content at onthemarkdata.com.

• Joined as employee one to lead GTM at Gable to help us reach our first customers.
• Worked with the CEO to establish our GTM strategy, content strategy, and messaging for technical audiences.
• Building internal data products to drive our GTM strategy and sales.

It didn't feel real until I had the physical copy of the book in my hands, but I'm officially a published author!

This is just such a full circle moment for me as I was reading these O'Reilly animal books when I was dreaming of making a career in data.

If you told me nearly 7 years ago that I would have my name on one I wouldn't believe you!

This represents about two years of life of nights and weekends on top of helping build a startup (Gable) and my own company (On the Mark Data).

Writing a book and building a company was hard... but I would easily do it all over again.

Hundreds of user interviews and discovery calls informed us what would be meaningful in the book.

While the book provided us clear synthesis of what we were seeing in industry.

This created a symbiotic relationship where we would build, write, get feedback, and repeat.

A huge shoutout to the O'Reilly team and my co-authors Chad and Ben!

An even bigger shoutout to everyone who supported us, as it wouldn't have been possible without you all.

Data Engineer | Tech Lead @ Gable.ai | O’Reilly Author: Data Contracts | LinkedIn [in]structor (30k+ Learners) | Founder @ On the Mark Data

No alternative text description for this image

Here's my tech stack for the dev environments I create for LinkedIn Learning courses (all open source) that you can use for yourself:

🐋 Dev Environment:
- Docker Compose - Containers and configs
- VSCode Dev Containers - IDE in container
- GitHub CodeSpaces - Browser cloud compute

📦 Databases:
- Postgres - Transactional Database
- Minio - Data Lake
- DuckDB - Analytical Database

⚙️ Ingestion + Orchestration + Logs:
- Python Scripts - Simplicity over a tool
- Data Build Tool - SQL queries on DuckDB
- Alembic - Python-based database migrations
- Psycopg - Interact with postgres via Python

📋 CI/CD:
GitHub Actions - Simple for students
 
📊 Data:
Data[.]gov - Public real-world datasets

👩🏽‍💻 Coding Surface:
Jupyter Notebooks - Quick and iterative
VS Code - Update and implement scripts

This setup is extremely powerful as you have a full data platform that sets up in minutes, it's filled with real-world data, you can query it right away, and you can see the logs. Plus, since we are using GitHub codespaces, it's essentially free to run in the browser with just a couple clicks!

Innovation isn't about creating completely brand new technologies and practices. Instead, it can be applying a proven method to a new domain.

Under this framing, data engineering teams can learn a lot from clinical research methodologies.

My stats professor in grad school used to joke around that someone messed up on research design if he had to use advanced statistics.

Why? You can avoid jumping through so many technical hoops if you control for people and process!

Here's how I see it applying to data systems:

A - clinical) How do we ensure fidelity (ie research staff follow protocols correctly) across the steps of data capture for the study?

A - data) How do we ensure the business processes for capturing and processing data stays consistent across staff (eg data entry)?

B - clinical) How do we store patient data, and what is the least amount of information we need to reduce risk for a HIPAA violation?

B - data) What data values are considered PII, how do we retain and delete such records, and what regulations do we need to handle?

C - clinical) What are we trying to compare (eg treatment vs control), and thus what measurements and demographics do we need to capture to properly assess this?

C - data) What telemetry do we need on a product to answer questions about usage of a product or service?

Nine times out of ten, the problem you are solving for already has a solution in a different context!

Stop looking at problems at a surface level and instead understand the "pattern" of the problem, and the find similar patterns in spaces where the problem has both been solved and well documented. It's why interdisciplinary teams are the most innovative!

This is also exactly how we landed on "shift left data" in our data contract book by the way! We saw the same problem pattern in DevOps and how they used "shift left" to address it.

"What do you mean my ROI would only be 1.5x? I need it to be a 4x for it to make sense..."

That brutal honesty was one of the best lessons I've learned in getting adoption and budget for data infrastructure projects.

I spent days making a proposal for a c-suite exec I was trying to sell to. I identified their core problems, was getting multiple meetings, and we were talking about numbers... and that's where I messed up.

My mistake was assuming this was their only problem they were considering for purchasing, and not part of a portfolio of problems they were strategically trying to solve.

I wasn't "competing" against a problem I knew I could clearly solve and generate ROI on. Instead, I was competing with the opportunity cost of not pursuing otherr solutions with various multiples of ROI for different problems.

That meeting completely changed how I think about value to execs. It's funny how obvious it sounds looking back, but I'm hard headed and needed to learn by experience.

Fast forward to today, and I feel like I have had enough reps to dive into the business technicals of what value actually means and map it to my experiences.

This week I'm reading the book Valuation, which was essentially the playbook used by McKinsey consultants. Specifically, I want to take my understanding of what is valuable to a business to next level with economic theory and technical business fundamentals.

I think more technical people should study this as well! Move away from parroting the abstract phrase "we need to focus on driving business value" and instead provide a clear plan to what that actually means and why a leader shoukd choose to invest in your initiative over others.

Allowing others to explore AI >>> Gatekeeping AI

I still remember the first time I saw a neural network work in real-time (shoutout the MarI/O Youtube video by SethBling) and just the magic I felt.

That was the moment I realized I needed to make a career in data and the moment I got over my fear of coding.

So many non-technical, but highly skilled in their domain, professionals are now experiencing exactly what I felt years ago.

Why try and police that?

Yeah, that vibe coded app is absolute crap and one agentic action away from deleting its own databases... but that person shipped something.

That's already more progress than the technical naysayers shouting from the sideline.

Data Career Jumpstart

🚀 Helping professionals break into data science 

Data Career Jumpstart is an online, data science school designed to teach you data science by doing practical, personal projects, enabling you to build an online portfolio that blows recruiters minds 🤯

Check this out at Data Career Podcast wherever you listen to podcasts!

Podcast Host

MIT Professional Education

Data Engineer

Snow Data Science

⛰️ Snow Data Science is an analytics firm that provides data solutions

📫 Send me a DM if you’d like to learn more about our services

We offer the following services:
▪️ Machine Learning 
▪️ Data Visualization
▪️ Algorithm Development
▪️ Data Sourcing
▪️ Data Cleaning
▪️ Dashboard / Web App

ExxonMobil

- Developed tool to make automated, data-driven decisions to improve capital planning ($330M/year)
- Created enterprise-wide web application via cloud computing to democratize manufacturing data to all engineers 
- Lead US wide analytics interest network of 200+ providing advice, expertise, and training
- Modeled complex supply chain processes that include 3,000+ variables to provide economic clarity via predictive analytics
- 3x company-wide data analytics competition champion that included 100+ participants

Vaporsens

- Implemented machine learning and advanced algorithms to identify and quantify unknown chemicals
- Solely responsible for all analytics in startup company
- Generated 30% revenue growth through convincing data visualizations and customer relations
- Performed and presented data analysis to Fortune 500 companies to secure collaborations

University of Utah College of Engineering

- Published first-author paper in International Federation of Automatic Control’s special issue Intelligent Manufacturing Systems (see link to read paper)
- Created new algorithm for clustering sensor data with over 1 million data points
- Constructed novel process to perform outlier detection in large industrial systems utilizing machine learning

Big Data Researcher

🖥️       Developed tool in Python to make automated, data-driven decisions to improve capital infrastructure planning and spending, providing an estimated savings of $330,000,000/year
🧮        Investigated complex mathematical refinery models to ensure accuracy in making crude oil acquisitions

Optimization Engineer

Machine learning and statistical data analysis 
-	Understanding of complex statistical strategies and strong critical thinking methods. 
-	Innovation and implementation of advanced algorithms for chemical sensing system to identify and quantify unknown chemicals applying machine learning and data analysis tools and methods.  
-	Programmed own data analysis pipeline and edited and updated chemometrics package from past chemomitrican to perform key calculations and capture vital data. 
-	Development of unique chemical fingerprints and chemical library. 
-	Assistance to engineering side through microcontroller production and coding for flow measurement and device communication used in future commercial products.

💽   Built bio-chemical organic semiconductor sensors 
☠️  Handled dozens of narcotics and explosives 
🧐  Lead quality assurance initiative to ensure $60k in product worked properly
🧬   Conducted 100's of complex experiments

R&D Engineer

University of Utah College of Science

☺️   Worked for free, part-time for 3 months. I owe everything good in my life to those 3 months.
🌎  Conducted scientific experiments for Doctor Sohn’s Iron Novel Development Lab
🔬  Used ICP-MS to analyze chemical samples for purity
📉   Performed data analysis in Excel to ensure high quality

I worked for free, part-time for 3 months. I owe everything good in my life to those 3 months.

Laboratory Assistant

I help people land their first data job within 90 days

Hey! I'm Avery! 👋🏻   My mission is to help working professionals break into data science. Let's connect!

🚀 Creator of Data Career Jumpstart; the data bootcamp that helps you start your data career by building an online portfolio full of projects!
Course includes: 
1) Data Skills (Python, R, SQL, Data Viz, ect)
2) How To Do Projects & Build A Data Portfolio 
3) How To Build Your Personal Brand 
4) How To Land Your Dream Job That Is Remote, High-Paying, and Fun 👩‍💻👨‍💻

📧  Sign Up To Get Updates: DataCareerJumpstart.com!

⛰ Running an analytics firm called Snow Data Science where we take on all sorts of data projects. From building custom dashboards, to predictions via machine learning, we are helping organizations utilize the power of their data. Have a project in mind? Send me a DM. Or visit, SnowDataScience.org. 

🏀   Landed a project with the Utah Jazz via LinkedIn post

⛽   Ex-ExxonMobil Data Scientist: solved complex supply chain problems, made beautiful data visuals, and taught data science to 300+ engineers! 2x company wide analytics champion. 

🔬 I fell in love with data at Vaporsens. I was hooked on finding meaning and insight from their chemical sensor data. The feeling was addicting. I felt like a super hero. I've never looked back.

📕 First author published as undergrad in IFAC. Novel research on anomaly detection in manufacturing data using machine learning. Feel free to read: https://www.sciencedirect.com/science/article/pii/S2405896319309073

📊 I am obsessed with data. Don't believe me? My dog has her own fitbit 🐶 My iPhone app usage has a dashboard 📱 My Chick-Fil-A visits are already predicted for next month 🐥  I run, breath, and sleep data 📊 What more proof can I give? 🤪

💬 Message me to talk about: How to break into data science, how to build a data science portfolio, and how to build a personal brand. 

Find me on:
🌐  DataCareerJumpstart.com
💻  GitHub: https://github.com/AveryData
🎥  YouTube: AVery Data
📷 Instagram: https://www.instagram.com/datacareerjumpstart/
✏️  Medium: https://medium.com/@averyjs
🎉 TikTok: @verydata

📧  Sign Up To Get Updates On My Latest Course: DataCareerJumpstart.com!

Avery Smith

Learn Data Analytics here: Matt Mike 
Learn Data Science here: Dawn Choo 
Learn Data Engineering here: Zach Wilson
Learn Statistics here: Joshua Starmer PhD
Learn Python here: Luke Barousse
Learn Excel here: Matt Brattin 
Learn Tableau here: Andy Kriebel 
Learn Data Visualization here: Cole Nussbaumer Knaflic 
Learn Data Storytelling here: Brent Dykes
Learn Data Interviews here: Nick Singh 📕🐒  
Learn Business Intelligence here: Alex Freberg
Learn Salary Negotiation here: Sundas Khalid
Learn Data Governance here: George Firican
Learn Data Strategy here: Vin Vashishta
Learn Data Freelancing here: Benjamin Rogojan
Learn Data Contracts here: 🎯 Mark Freeman II  


👉 Learn How to Land Your First Data Job here: https://lnkd.in/eYZh7_-U 👈



♻ Repost to share with others
🔖 Save to revisit down the road
💬 Comment anyone else I missed!

Founder @ Data Analytics Accelerator (CYBER MONDAY SALE IS LIVE)

If I woke up today and suddenly wasn't a Data Analyst, here's the 7-step plan I'd follow to start all over and land my first data job:

Step 1: Learn only 3 data tools: Excel, SQL, Tableau. That's it. There's too many other tools. These are the ones that matter the most. Ignore everything else. 

Step 2: Explore data roles. I’d learn what data titles/roles are out there. Business, financial, marketing, healthcare, etc. The goal? Find one that matches my background and interests. 

Step 3: Build proof. I need a hiring manager to to trust me. I’d pick an interesting datasets and make a project. Tangible evidence that I can actually work with data. 

Step 4: Make a home. I’d put those projects online in a portfolio. Somewhere easily visible. I'd design something that makes recruiters say “wow.”

Step 5: Polish my profiles. Resume and LinkedIn would get a full makeover. They're the only hiring managers see first. ATS friendly. Keyword stuff. Quantitative results. 

Step 6: Apply a lot. 100+ jobs. Because you can’t get hired if you don’t apply. I wouldn't get discouraged. I know I'll get rejected 5% of the time, but I only care about the 5%

Step 7: Cram interviews. Wouldn’t even worry about them until I landed one. Then I'd study like crazy. Practice behavioral & technical questions.

Follow this exact plan: https://lnkd.in/eYZh7_-U

datacareerjumpstart

snow-datascience

JSESSIONID

li_at

linkedin.com

Talks about #analytics, #datascience, #engineering, #dataanalytics, and #datavisualization

How to Become an Analyst in 2026

📈 I help people land their first data job (even with no experience) 📊 Join 10k+ other analysts & get my newsletter! 🎙️ Host of The Data Career Podcast

Here are 4 reasons I wouldn't become a Data Scientist in 2026 (and what I'd do instead): 👇

1️⃣ It takes too long
Data scientists do a lot of machine learning. ML requires a lot of math and programming. Math takes a long time to learn and understand. So does programming. It just takes forever to learn this stuff (especially on your own time and resources). 

2️⃣ You probably need a master's degree
Unfortunately, data science roles have a high bar to clear education wise. Most require Master's. I wish it wasn't true - but that's reality. That's basically 2 years and $20,000 out the window before you're even employable. 

3️⃣ Other roles have more openings
Believe it or not, there are 2x more open Data Analyst roles than data science (and 10% more Data Engineering roles). There's a big demand for Data Analyst and Data Engineer right now. Data science? Maybe not as much (see why below). 

4️⃣ Companies don't see quick ROI 
Data science projects are long. They're complex. And the fail most the time. It's hard to see clear ROI. Data analyst on the other hand, have a more immediate value add. It just is easier to do good reporting than it is to do machine learning. Companies want quick wins in this economy. 

So what should you do instead? 

I think you should become a data analyst. It's a much easier role to land. There's a lot more openings. You still get paid great. You can pivot very quickly. And you can get paid to learn data science or data engineer later. It's the "gateway" role into the data world. 

🤷♂️ That's my 2 cents

I explain it in depth in my latest podcast episode:
📺 https://lnkd.in/e2ccZGMC
🎧 https://lnkd.in/eRbrX2yK

Dear all, tomorrow is December. 

Here's a 30-day FREE "Become a Data Analyst" study plan for you: 

December 1: Roadmap - https://lnkd.in/eHpVVEmh
December 2: Skills - https://lnkd.in/efiG9Yyr
December 3: Entry-Level Jobs - https://lnkd.in/ewCuVdFv
December 4: Get Paid to Learn - https://lnkd.in/ex9ztUex
December 5: Best Locations - https://lnkd.in/eBUfEuBb
December 6: Data Jobs Dying? - https://lnkd.in/e7WUqhnr
December 7: Break
December 8: Get Ahead - https://lnkd.in/eAKEqBAr
December 9: Best Books - https://lnkd.in/eajhbnPr
December 10: Data Storytelling - https://lnkd.in/edAx7wbq
December 11: Break
December 12: Healthcare Analytics - https://lnkd.in/eFp5YaWH
December 13: Google Cert - https://lnkd.in/eJ5UK2Ud
December 14: Break
December 15: Data Salaries - https://lnkd.in/eXNdFpPZ
December 16: Finding Jobs - https://lnkd.in/ehJ9EaWt
December 17: Cold Messages - https://lnkd.in/ehJ9EaWt
December 18: Find Datasets - https://lnkd.in/emEJR7Vf
December 19: Learning Ladder - https://lnkd.in/eabdThfs
December 20: Hiring Process - https://lnkd.in/emdprJDm
December 21: Break
December 22: Interview Calculator - https://lnkd.in/ezt8AhUJ
December 23: Project Ideas: https://lnkd.in/eHUqx_2F
December 24: Break
December 25: Break
December 26: Job Hunting - https://lnkd.in/ey4SJG9f
December 27: Data Analyst Checklist - https://lnkd.in/eUmKdQ6m
December 28: Break
December 29: Confidence - https://lnkd.in/eG_hDGdC
December 30: Data Habits - https://lnkd.in/e77VEK-A
December 31: Be proud of yourself 

Spend 20 min/day. You'll be AMAZED at how much you learn. 

Found this useful, please hit "reshare" so others can benefit as well. 

Enjoy!
Avery

SELECT turkey, mashed_potatoes, stuffing, pumpkin_pie
FROM thanksgiving_table
WHERE football_on = TRUE
AND blessed = TRUE;

Result: 1 happy family!!

Happy Thanksgiving from the Smiths! 🦃🍂🏈

Dear all, there's a lot of tools in the data world.

But you don't need to master them all. Here are the BEST data tools to start with:

1) Excel
Excel is still King in the data analytics world. Old? Yup. Boring? Maybe. But it's still VERY popular. It's worth learning Excel. It's required in 39% of data analyst job listings. It's also not that hard to learn. You can learn most of it in a week. Start with filtering, sorting, VLOOKUPS/XLOOKUPS, pivot tables, pivot charts, scatter plots, bar charts. That's a great start. 

2) SQL
SQL is the Queen in the data analytics world. And honestly probably the Emperor of the data universe. Learn it. You won't regret it. It's required in 31% of data jobs. It's a bit harder than Excel as it is a programming language, but it's not bad. Learn SELECT, FROM, WHERE, AND, IN, NOT, GROUP BY, AVG, MAX, SUM, HAVING and that's enough to build momentum. 

3) Tableau
Tableau is the Prince of data analytics and the most popular data viz tool. It's required in 21% of data analytics roles. Learn how to make charts & dashboards. I think bar charts, scatter plots, line charts, KPI's, and data visualization principles is enough. Power BI is it's twin. If you learn Tableau, you'll have no problem learning Power BI later.

4) Python
Python is the Wizard in the data kingdom. It's powerful, flexible, and can basically do anything: data cleaning, data modeling, data visualization. But it's hard to master - takes a long time - and is only required 14% of the time. Learn at your own risk. 

5) Power BI
Power BI is the Prince's little brother (also a prince, I guess). I personally think Power BI is easier to use than Tableau, but according to the data, it's only required in 13% of data jobs. 7% less than Tableau. And the fact that it's hard to install (especially on a Mac), lead me to recommend focusing on Tableau first. 

6) R
R is the Royal Scholar of the data kingdom. It's great at statistics and some other things, but not everything. It's not required that often - only 8%. And that 8% is probably government jobs and academia where it has a strong hold. Easier than Python, but less powerful. Personally: Python > R 

Hope this helps,

Avery

p.s. if you enjoyed this & want to learn all these skills from me by building real-world portfolio projects, check out The Accelerator: https://lnkd.in/eYZh7_-U (literally best time to join)

What data skills should you learn

It feels extremely overwhelming to see all the super-qualified people applying to an entry-level data analyst job. Especially when you're just getting started But here's a story that should give you hope 👇

I was talking to my data hiring manager friend who explained how he hired his last entry-level position. He didn't actually hire the person with the most data experience. He didn't hire the smartest candidate. Or who was best at SQL.

He hired the person with medium data skills, but a very strong domain background. 

He said, "I can teach them data stuff, but teaching them about the domain would take years"

👉 So apply to data jobs within your domain:

Teacher? Apply to education data jobs.
Accountant? Apply to financial data jobs.
Physical Therapist? Apply to health data jobs. 

I promise you've got this! Don't give up.

𝐅𝐫𝐞𝐬𝐡 𝐝𝐚𝐭𝐚 𝐫𝐨𝐥𝐞𝐬 𝐭𝐡𝐚𝐭 𝐚𝐫𝐞 𝐡𝐢𝐫𝐢𝐧𝐠 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰!
(Apply before the roles close)

Principal Data Analyst - Risk Management (Capital One)
Link: https://lnkd.in/exYN94D4

Associate HR Data Analyst (Mortenson)
Link: https://lnkd.in/eTpXGXiu

Marketing Analyst (Backroads)
Link: https://lnkd.in/ecGiUsif

Financial Analyst, Sales (Dave & Buster’s/Main Event)
Link: https://lnkd.in/eFAyDwut

Data Analyst (Remote - US) (Jobgether/Partner)
Link: https://lnkd.in/eihR-8us

Junior Financial Analyst - Commercial Real Estate (Colliers)
Link: https://lnkd.in/emt7c7Bm

Senior Financial Analyst (Adtalem Global Education)
Link: https://lnkd.in/eafGDX6x

Data Specialist (WSB)
Link: https://lnkd.in/egupsQN8

Data Analyst - BI (LetsGetChecked)
Link: https://lnkd.in/eA-TkzRZ

Business Analyst (Steampunk)
Link: https://lnkd.in/eWifk9Pq

Accountant/Financial Analyst (City of Dunedin)
Link: https://lnkd.in/e73Rn5CY

Business Intelligence Engineer (ALTEN Technology USA)
Link: https://lnkd.in/e_VeCQs7

Quality Systems PLM Business Analyst (Insulet)
Link: https://lnkd.in/ebGfpath

Software Engineer 2 - Report Developer (Power BI + Tableau) (U.S. Bank)
Link: https://lnkd.in/eHs2v_sz

Happy applying and good luck!
Find more roles here: https://lnkd.in/e4HAmxxQ

in a world where with more and more different data tools, i find myself wanting to be the guy on the right....

agreed? 

p.s. sub to my free newsletter: https://lnkd.in/ep36uEaC

Landing a data job in 2026 will be hard.
Most people will unfortunately fail. 

Why? I think because it used to be easy. 
Just learn the skills and apply to 25 jobs and you're good.

That's not the case any more.
It's sadly much more involved now.

Still possible? Even with no technical background?
Yes. Here's what I would do if I was in your shoes:

Learn the skills. That's still very important. 
But it's not what lands you the job. It's what qualifies you for the job. 

Landing the job is much more about two forgotten concepts.
Networking & Portfolio Projects.

If you're ignoring these, you're likely to fail.
Luckily, it's not too late or difficult to start these.

Start an online portfolio. Even if it's ugly or empty.
Use LI, Carrd, GitHub - doesn't matter. 

Build at least one good project. Bonus points for more.
Grab a data set and answer: who, how much, when, where, etc.

Optimize your LinkedIn and resume. Spend hours.
These are the tools to actually get noticed.

But don't leave getting noticed to chance. Reach out.
Find recruiters and hiring managers and message them. 

And reach out to friends & family about data.
Just ask, "do you know any data analysts?" 

Weird? A little. Awkward? yes. 
Worth it? 100%

Want the exact step-by-step guide? https://lnkd.in/eYZh7_-U
Cyber Monday sale going on today.

If you want to become a data scientist, I have some advice. DON'T try to become one right now. At least not directly.

I know that sounds backwards, but hear me out.

I shared the bitter truth about data science in this week’s newsletter.

Check it out. I’ll:
➤ Break down why most people struggle on the data science path
➤ Show the smarter route most people overlook
➤ Share a few data jobs hiring right now

It’s the advice I wish I heard early on.

I have bad news. If you want to become a Data Scientist, I don’t think you should try to be one. At least right now. Lemme explain. Data science isn't

Don't become a data scientist in 2026...

Dear Future Analysts,

There are ONLY 3 ways to learn data analytics:

1) Pay to Learn
2) Learn for Free
3) Get Paid to Learn

If you're like me, then you want to go with number 3. Here's how I got paid to learn: 👇

I got my foot in the door EARLY. I wasn't trying to land a six-figure data scientist job. I took what I could get right away: basic analysis, nothing fancy. I didn't know Tableau, SQL, Python, or R. I pretty much only used Excel.

Eventually, my role required me to learn SQL. So guess what? I watched videos & took courses on company time and learned SQL. 

Later my job required me to upgrade to Python. I learned it on company time. 

Now? I'm a SQL & Python expert. But I got paid to become one.

I'm nothing special. You can do the same thing.

I actually run a program that teaches you how to do this. We're starting a special new cohort this Thursday 👇

https://lnkd.in/eYZh7_-U

53 days left in 2025. If I wanted to become a data analyst, these are the 53 steps I'd take (in order):  

 1. Become aware of all the data positions available (not just data analyst) 
 2. Look at 50+ job descriptions & see what skills are required 
 3. Understand that certs carry little weight in the data field 
 4. Fill free time by listening to data podcasts 
 5. Start learning data analysis w/ Excel (VLOOKUPS, Pivot Tables, etc) 
 6. Build a sales or marketing project w/ Excel 
 7. Post it on LinkedIn as a LinkedIn Article 
 8. Feature it on my LI Profile 
 9. Make sure my LI profile picture is clear 
10. Ensure I have a LI cover photo
11. Construct my LI headline (include keywords)
12. Rewrite my LI about section (keyword stuff, but tell my story) 
13. Make my LI experience section full of actionable bullets 
14. Try to "twist" my previous job titles to sound more like a "data analyst" 
15. Start learning data visualization in Tableau 
16. Build an education dashboard in Tableau (line, bar, KPI's, etc) 
17. Post that project on LI as an article. Feature on profile 
18. Start posting 1x/week on LI (sharing what I learned this week) 
19. Start leaving 5 comments / week on LI 
20. Build an external portfolio on Carrd 
21. Start learning basic SQL 
22. Build a financial analytics project in SQL w/ real data 
23. Post it on LinkedIn as an article & feature it 
24. Add it to my new personal portfolio 
25. Add my other two projects to my Carrd portfolio 
26. Start applying for 5 jobs / week 
27. Send 1 cold message / week 
28. Get better at SQL by building a more advanced project w/ healthcare data 
29. Create a video summary of the project sharing my findings 
30. Post it on LinkedIn 
31. Add it to my portfolio 
32. Increase applications to 10 jobs / week 
33. Send 2 cold messages / week 
34. Improve at Tableau by doing a more difficult project w/ sports data 
35. Document my learnings in an article 
36. Share on LinkedIn as an article 
37. Add to my portfolio 
38. Up the applications to 15 jobs / week
39. Up the cold messages to 5 / week 
40. Discover the STAR method of interviewing 
41. Learn the absolute basics of Python 
42. Build a basic Python project w/ manufacturing data 
43. Do a writeup about my learnings 
44. Post on LI portfolio 
45. Share to my Carrd portfolio 
46. Start doing mock interviews w/ behavioral questions 
47. Ramp up applications to 25 jobs / week
48. Force myself to send 12+ cold messages 
49. Start learning the fundamentals of statistics & R. 
50. Build a project in R w/ HR data
51. Post that project on LI & share to portfolio 
53. Build a capstone project that encapsulates all I've learned

If you like this plan, I'm starting a special new cohort of my data analyst bootcamp this Thursday (Nov 13th) & I will walk you through this exact roadmap. 

👉 Click to learn more: https://lnkd.in/eYZh7_-U   👈

Fresh data roles that are hiring right now!
(Apply before the roles close)

Lead Data Analyst (Vanguard)
Link: https://lnkd.in/eNarWSKx

Senior Technical Data Analyst - Operations E2E Data Intelligent Systems (NVIDIA)
Link: https://lnkd.in/e5YEMe96

Staff Business Analyst (PlayStation)
Link: https://lnkd.in/eajFadCt

Experienced Business Intelligence and Data Analyst (Virtual) (The Boeing Company)
Link: https://lnkd.in/eVCdhHyT

Performance Marketing Analyst - Mid Level (USAA)
Link: https://lnkd.in/e7VWVBXd

Clinical Research Data Specialist (University of Miami/UHealth)
Link: https://lnkd.in/eaZYdYj3

Financial Analyst I (Leader Bank)
Link: https://lnkd.in/edYEwS4d

Business Intelligence Engineer (Auto-Owners Insurance)
Link: https://lnkd.in/ez6uBSuk

Business Analyst Intern (8 - month) (Kenvue)
Link: https://lnkd.in/egVkNYYG

Data Analyst - BIHA (Vida)
Link: https://lnkd.in/e3quausc

Data Analyst (Wilen)
Link: https://lnkd.in/evp5jfQe

Financial Analyst (Premier Research Labs)
Link: https://lnkd.in/evhQPiZH

Entry level Data Analyst (ASTA Corporate Resource Solutions Inc)
Link: https://lnkd.in/e5NA6zN7

Entry Level Data Analyst
Link: https://lnkd.in/ec5hH8jS

Sales Data Analyst Intern (Fun and Moving)
Link: https://lnkd.in/eY8mTgDP

Senior IT Data Analyst (Emerald Resource Group)
Link: https://lnkd.in/edrefa8z

Happy applying and good luck!
Find more roles here: https://lnkd.in/e4HAmxxQ

My data analyst strategy for you 👇

- Learn 1 new data thing / day 
- Do 1 analytics project / month
- Read 10 pages of a data book / day
- Interact with 2 LinkedIn posts /day
- Post learnings on LinkedIn 3x /week

👉 Do this for 3 months and watch what happens

👉 Do it with me: https://lnkd.in/e8aqWevH
(closes tomorrow)




🔖 Save for reference later
♻ Repost if you found helpful
🔥 Follow for more tips

Here are the 7 coolest data jobs I saw open this week:

🧩 Data Analyst - Words with Friends
I used to be obsessed with this game. Analyze all my games.
https://lnkd.in/ev9ytgrT

🚚 Data Analyst - Hello Fresh
Food delivery = the future. I bet there's cool things to analyze here.
https://lnkd.in/ebZpvCsX

🌮 Data Analyst - Ole Mexican Foods
Who wouldn't want to analyze Mexican food all day?
https://lnkd.in/ea8jT_yR

🍔 Marketing Analyst - Red Robin
Been awhile since I've been to Red Robin - they could use some marketing help lol
https://lnkd.in/eiQvFbe6

🍕 Financial Analyst - Domino's Pizza
My guess is pizza sales continue to rise in 2026, but you let me know.
https://lnkd.in/e2Bbewx7

💸 Financial Analyst - Lottery Company
I've never done the lotto, but working for one sounds super interesting.
https://lnkd.in/eptnGM6h

🚀 Business Intelligence Engineer - Blue Origin (Jeff Bezos Space CompanY)
The JD didn't say a trip to the moon was included but seems rad.
https://lnkd.in/ejNXBmMf

Apply, apply, apply!
Share, share, share!
Follow, follow, follow! 

You've got this! 

Your data friend,

Avery

I’m excited to announce I’ve built a brand new software called Matches with Replit's help!

Matches automagically pairs & schedules 1:1 meetings for your community members. We’ll be using it inside The Accelerator to help our students network.

Honestly, Replit made it SO easy to build this & it’s only going to get easier. They just announced today their new AI Integrations with OpenAI, Claude, Gemini, and more. Basically they’re making it a one-stop-shop for shipping software quickly.

Guys, your ideas don’t have to stay ideas! You can make them reality!

Use Replit!

🎁 Try Replit: https://lnkd.in/e8CavjZZ (and get extra credits when you use my referral link)

#ReplitPartner

Replit

🥳 I'm pumped! Tonight at 7PM ET I'm kicking off The Insider Cohort of The Accelerator with some aspiring data analysts! Want to come?!

We'll be going over the exact roadmap for them to land data jobs in 2026. And I was curious if you might want to attend? Here's the link: https://lnkd.in/eE5-JX-e

I'll be showing them how to:

 1. Network like a pro
 2. Optimize their resume
 3. Land lots of interviews
 4. Create their very own portfolio
 5. Build a beautiful report in Excel
 6. Make stunning charts in Tableau
 7. Analyze real-world data with SQL
 8. Learn the fundamentals  of Python & R
 9. Turn their LinkedIn into a recruiter magnet
10. Accept an absolutely incredible data job offer ($$$)

It will give you a great roadmap for 2026 and give you a small taste of how awesome my data analyst bootcamp is! So if you're curious, come! Seriously, it would be super fun to have you. https://lnkd.in/eE5-JX-e


See you in a few hours,

AS


pssst...not too late to join the full  bootcamp either (discount expires tonight): https://lnkd.in/eYZh7_-U

Which ones of these 7 data career myths is keeping you stuck on your data journey?

7 false myths keeping you stuck in your data journey

Data interviews are super scary & hard, but luckily there's one simple thing you can do to make them suck less...

Practice!

When I finally landed my first data analyst interview, I can't tell you how nervous I was. It felt my whole career was depending on it! I was so anxious I had my wife conduct mock interviews with me. And it worked! I got the offer! I owe a lot of my career to that job and I owe that job to practicing my interview questions (shout out my wife!) 

But how do you practice interviews?

I made you a FREE practice mock interview with 3 commonly asked questions that you can practice TODAY! I ask you the question, you pause the interviews and answer, and then I show you a good and bad answer. It's judgement free practice. 

Interviews stop being scary when you've already done a rehearsal of what you're going to say. I promise the more you practice, the less nervous you'll get. The less nervous you get, the better you'll do. The better you perform, the more offers you'll get. The more offers you get, the more money you make. Practice = $$$. 

Ready to practice?!? 
Video version: https://lnkd.in/eREgAfbT
Audio version: https://lnkd.in/e6WzyPRh


My challenge to you is to do one of the following:
A) Try the practices right now 
B) Save this post to revisit later this week
C) Reshare this post so others can try it

You've got this!

AS

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The Battle

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