Dawn Choo's Recent LinkedIn Posts

Anyone going to HumanX conference in San Francisco? I'm wondering if it's worth attending!

Our first LinkedIn Learning course is LIVE!! This has been a GOAL of mine for years. And now it's come true.. thanks to the incredible LinkedIn Learning squad ♥️ Our course is on 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘯𝘨 analytics & reporting workflows, using n8n. Any data professional has been through this: • Hours each week pulling data from multiple sources • Copy-pasting the same report into 5 Slack channels • Those "hey can you resend that report?" messages But n8n makes it so easy to automate, without code! You can even add an AI layer to • Highlight the most important insights • Create metric summaries • Draft emails We so appreciate any support you can give! Take our course here: https://lnkd.in/g_V3_Ux3 ♻️ Repost so others can see it!

Claude Code is good. Claude Code + Skills is powerful. Everyone is talking about Skills in Claude Code. 𝗕𝘂𝘁 𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 𝗲𝘃𝗲𝗻? Skills are r͟e͟u͟s͟a͟b͟l͟e͟ ͟i͟n͟s͟t͟r͟u͟c͟t͟i͟o͟n͟ ͟f͟i͟l͟e͟s͟ that teach Claude Code how to do something new or follow a workflow. I like to think of them like playbooks, where each playbook is responsible for a discrete, complete task. Or more simply.. 𝘚𝘬𝘪𝘭𝘭𝘴 𝘦𝘹𝘵𝘦𝘯𝘥 𝘸𝘩𝘢𝘵 𝘊𝘭𝘢𝘶𝘥𝘦 𝘊𝘰𝘥𝘦 𝘤𝘢𝘯 𝘥𝘰 𝘣𝘦𝘺𝘰𝘯𝘥 𝘪𝘵'𝘴 𝘣𝘶𝘪𝘭𝘵-𝘪𝘯 𝘤𝘢𝘱𝘢𝘣𝘪𝘭𝘪𝘵𝘪𝘦𝘴. 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝗰𝗮𝗿𝗼𝘂𝘀𝗲𝗹 𝘁𝗼 𝗳𝗶𝗻𝗱 𝗼𝘂𝘁: ✔️ What the two types of Skills are ✔️ How to build your own Skill ✔️ How to use a Skill 🔖 Save this for later ♻️ Repost to share it with your network

Your resume is getting lost in a sea of applicants. Here’s how to stand out 👇🏽 We all know this. The job market is brutal. One single job posting can get hundreds of applications in 24 hours. You don’t want to just be one of the many. You want to stand out. So here’s a simple hack I use - I build a customized website for every job that I apply to. On the website, I include ↳ Reasons why I’m a good fit for THIS role ↳ My customized resume ↳ And… a picture of me already working at the company All of this is built with Airtable’s AI agents. Here’s how it works: 𝗦𝘁𝗲𝗽 𝟭: I copy and paste the full job description (usually I find them on LinkedIn) 𝗦𝘁𝗲𝗽 𝟮: The Airtable field agents extract all the important information - Company, team and role - Required skills - Preferred skills 𝗦𝘁𝗲𝗽 𝟯: Then more field agents generate: ✔️ Customized resume bullet points and summary ✔️ An image of me wearing the company’s shirt ✔️ A polished website… 𝘪.𝘦. 𝘮𝘺 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭𝘪𝘻𝘦𝘥 𝘱𝘪𝘵𝘤𝘩 𝘵𝘰 𝘵𝘩𝘦 𝘩𝘪𝘳𝘪𝘯𝘨 𝘮𝘢𝘯𝘢𝘨𝘦𝘳 & 𝘳𝘦𝘤𝘳𝘶𝘪𝘵𝘦𝘳 Every time I add a new job listing, the whole thing runs automatically. No additional prompting needed, because the field agents remember the context. 𝗦𝘁𝗲𝗽 𝟰: I include the website on my application & outreach messages And that’s it. It takes less than 10 minutes per job application. You can use this EXACT template I built with Airtable → https://lnkd.in/ebfRCyHT #AirtablePartner

Two years ago, on this day, I started posting on LinkedIn. Since then I have: • Quit my full-time job to be a content creator • Built and launched Interview Master • Worked with so many dream brands • Spoke for the first time on a stage!   I never set out to be a content creator. I just wanted to build a side income stream. But somehow 190,000 people have chosen to join me. And LinkedInfluencing is now my full-time job! My boyfriend just asked me "How are you feeling?" "Grateful", I said. An overwhelming gratitude for all of you: ↳ For laughing at my jokes even when they're not funny ↳ For your words of encouragement when I was down ↳ For reposting content that I spend hours working on And huge thanks to these people who have supported me from the very beginning • Clara Ma for reviewing every SINGLE one of my early posts and giving me the courage to hit post. • Jeremiah Clark for holding my hand through every up and down, and showing me AI is not as scary as I thought, and for building Interview Master with me. • Venkata Naga Sai Kumar Bysani for commenting on probably every one of my posts in the last 2 years, and for showing me what consistency looks like. Can't wait to see what Year 3 has in store for me. LFG!

LinkedIn really hitting us with the most important news this Monday morning.

I'm speaking at the Optimized AI conference in Atlanta! On mastering Claude Code for Data professionals.. in exactly 20 minutes. But why Claude Code? I've been talking to dozens of working Data professionals on which AI tools they love the most, and Claude Code is by far & away, the most LOVED tool. 𝘗𝘭𝘶𝘴, 𝘐 𝘭𝘰𝘷𝘦 𝘪𝘵 𝘵𝘰𝘰. Also, my girl Jess Ramos is doing a KEYNOTE talk!! So you should come watch us both :) Tickets still available here: https://lnkd.in/eaV9GAyd Use code "FINAL-25" for 15% off! Conference details: Next Monday & Tuesday, March 30th & 31st Atlanta, GA Optimized AI Conference

There are levels to SQL. You don't need to be an expert to be a Data Analyst. The bar for SQL changes based on job level. Of course.. it's always better to know more. But focus on building a strong foundation first. 𝗠𝘆 𝘀𝘁𝗼𝗿𝘆: I landed an Analytics jobs after teaching myself SQL. I thought... "*I must be pretty good at SQL."* Boy, I was wrong. I knew enough SQL for an entry-level role. I definitely was not good at SQL. I learned on the job and gradually got better. ------ So what level of SQL do you need per job level? 𝘊𝘩𝘦𝘤𝘬 𝘰𝘶𝘵 𝘵𝘩𝘦 𝘪𝘮𝘢𝘨𝘦 𝘣𝘦𝘭𝘰𝘸! 𝗛𝗼𝘁 𝘁𝗶𝗽𝘀 𝗳𝗼𝗿 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗴𝗼𝗼𝗱 𝗮𝘁 𝗦𝗤𝗟 #1: Before you even begin learning SQL, make sure you understand relational databases. #2: Make a cheatsheet as you're learning -- you'll retain information better. #3: Build the habit of practicing one SQL question every day. I have a SQL practice platform Interview Master. Check it out -> www.interviewmaster.ai ------ ♻️ Repost this, if you found this helpful!

I've had 9 jobs in the past 15 years. I wish my younger self knew this 👇 More money ≠ more happiness. But money made a difference (𝘵𝘰 𝘴𝘰𝘮𝘦 𝘦𝘹𝘵𝘦𝘯𝘵) I could ↳ buy more clothes ↳ go on more vacations ↳ afford to eat out more often And of course, I had to worry less. But some of the jobs I had came with: ↳ working late nights & weekends ↳ unkind, unhelpful managers ↳ so much stress I got sick Not fun fact: I was so stressed in my previous job, that it retriggered my autoimmune disease 🫣 So I wish my younger self knew that making more money wasn't going to make it happier. My stance now on what really matters in a job: ↳ working with people who I enjoy ↳ having passion for the space ↳ my physical & mental health ♻️ Repost if you enjoyed this post!

On Saturday night during SXSW, we co-hosted a small partner dinner with the incredible Jean Kang in Austin. No stage. No presentations. Just a table full of founders, partners and creators who care deeply about supporting small business owners. What stood out most wasn’t just the ideas shared. It was the energy that comes from bringing the right people together. Community isn’t a soft concept anymore. It’s becoming one of the most powerful forces in modern companies. When people who share a mission gather in the same room, conversations turn into collaboration. Collaboration turns into momentum. And momentum turns into real progress for the people those companies serve. At Flodesk, that sense of community has always been core to how we think about building. Not just creating tools, but creating spaces where founders, creators and partners can learn from each other and grow together. Grateful to everyone who joined us in Austin and helped make the night what it was. Rebecca Shostak Jess Ramos Sundas Khalid Dawn Choo Hannah Zhang Anjali Viramgama Madeline Zhang Eric Pan Alberta Devor Jasmine Wong Colin Rocker Devin Howlett Sho Dewan Grace Ling 🐰 Maya Grossman Arjay McCandless Gabriela Gironas Dominick Namis Madeline Levy Joanna Waterfall Neal Jean Mike Peditto

There are levels to SQL. You don't need to be an expert to be a Data Analyst. The bar for SQL changes based on job level. Of course.. it's always better to know more. But focus on building a strong foundation first. 𝗠𝘆 𝘀𝘁𝗼𝗿𝘆: I landed an Analytics jobs after teaching myself SQL. I thought... "*I must be pretty good at SQL."* Boy, I was wrong. I knew enough SQL for an entry-level role. I definitely was not good at SQL. I learned on the job and gradually got better. ------ So what level of SQL do you need per job level? 𝘊𝘩𝘦𝘤𝘬 𝘰𝘶𝘵 𝘵𝘩𝘦 𝘪𝘮𝘢𝘨𝘦 𝘣𝘦𝘭𝘰𝘸! 𝗛𝗼𝘁 𝘁𝗶𝗽𝘀 𝗳𝗼𝗿 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗴𝗼𝗼𝗱 𝗮𝘁 𝗦𝗤𝗟 #1: Before you even begin learning SQL, make sure you understand relational databases. #2: Make a cheatsheet as you're learning -- you'll retain information better. #3: Build the habit of practicing one SQL question every day. I have a SQL practice platform Interview Master. Check it out -> www.interviewmaster.ai ------ ♻️ Repost this, if you found this helpful!

Claude Code is good. Claude Code + Skills is powerful. Everyone is talking about Skills in Claude Code. 𝗕𝘂𝘁 𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 𝗲𝘃𝗲𝗻? Skills are r͟e͟u͟s͟a͟b͟l͟e͟ ͟i͟n͟s͟t͟r͟u͟c͟t͟i͟o͟n͟ ͟f͟i͟l͟e͟s͟ that teach Claude Code how to do something new or follow a workflow. I like to think of them like playbooks, where each playbook is responsible for a discrete, complete task. Or more simply.. 𝘚𝘬𝘪𝘭𝘭𝘴 𝘦𝘹𝘵𝘦𝘯𝘥 𝘸𝘩𝘢𝘵 𝘊𝘭𝘢𝘶𝘥𝘦 𝘊𝘰𝘥𝘦 𝘤𝘢𝘯 𝘥𝘰 𝘣𝘦𝘺𝘰𝘯𝘥 𝘪𝘵'𝘴 𝘣𝘶𝘪𝘭𝘵-𝘪𝘯 𝘤𝘢𝘱𝘢𝘣𝘪𝘭𝘪𝘵𝘪𝘦𝘴. 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝘁𝗵𝗶𝘀 𝗰𝗮𝗿𝗼𝘂𝘀𝗲𝗹 𝘁𝗼 𝗳𝗶𝗻𝗱 𝗼𝘂𝘁: ✔️ What the two types of Skills are ✔️ How to build your own Skill ✔️ How to use a Skill 🔖 Save this for later ♻️ Repost to share it with your network

Your resume is getting lost in a sea of applicants. Here’s how to stand out 👇🏽 We all know this. The job market is brutal. One single job posting can get hundreds of applications in 24 hours. You don’t want to just be one of the many. You want to stand out. So here’s a simple hack I use - I build a customized website for every job that I apply to. On the website, I include ↳ Reasons why I’m a good fit for THIS role ↳ My customized resume ↳ And… a picture of me already working at the company All of this is built with Airtable’s AI agents. Here’s how it works: 𝗦𝘁𝗲𝗽 𝟭: I copy and paste the full job description (usually I find them on LinkedIn) 𝗦𝘁𝗲𝗽 𝟮: The Airtable field agents extract all the important information - Company, team and role - Required skills - Preferred skills 𝗦𝘁𝗲𝗽 𝟯: Then more field agents generate: ✔️ Customized resume bullet points and summary ✔️ An image of me wearing the company’s shirt ✔️ A polished website… 𝘪.𝘦. 𝘮𝘺 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭𝘪𝘻𝘦𝘥 𝘱𝘪𝘵𝘤𝘩 𝘵𝘰 𝘵𝘩𝘦 𝘩𝘪𝘳𝘪𝘯𝘨 𝘮𝘢𝘯𝘢𝘨𝘦𝘳 & 𝘳𝘦𝘤𝘳𝘶𝘪𝘵𝘦𝘳 Every time I add a new job listing, the whole thing runs automatically. No additional prompting needed, because the field agents remember the context. 𝗦𝘁𝗲𝗽 𝟰: I include the website on my application & outreach messages And that’s it. It takes less than 10 minutes per job application. You can use this EXACT template I built with Airtable → https://lnkd.in/ebfRCyHT #AirtablePartner

Two years ago, on this day, I started posting on LinkedIn. Since then I have: • Quit my full-time job to be a content creator • Built and launched Interview Master • Worked with so many dream brands • Spoke for the first time on a stage!   I never set out to be a content creator. I just wanted to build a side income stream. But somehow 190,000 people have chosen to join me. And LinkedInfluencing is now my full-time job! My boyfriend just asked me "How are you feeling?" "Grateful", I said. An overwhelming gratitude for all of you: ↳ For laughing at my jokes even when they're not funny ↳ For your words of encouragement when I was down ↳ For reposting content that I spend hours working on And huge thanks to these people who have supported me from the very beginning • Clara Ma for reviewing every SINGLE one of my early posts and giving me the courage to hit post. • Jeremiah Clark for holding my hand through every up and down, and showing me AI is not as scary as I thought, and for building Interview Master with me. • Venkata Naga Sai Kumar Bysani for commenting on probably every one of my posts in the last 2 years, and for showing me what consistency looks like. Can't wait to see what Year 3 has in store for me. LFG!

This roadmap got me Meta & Google Data Science jobs. Steal this to land a Data Science offer in 2026 👇 ---- I was a Data Scientist at Meta for 4 years. But it took me 6 years to land my first Data Science job. Through my many failed interviews, I learned a LOT about landing a Data Science job. And since then I have landed offers from 10+ different companies. I'm sharing the e͟x͟a͟c͟t͟ roadmap that worked for me. My roadmap is simple, curated and focused. The goal is to only spend time on learning what you NEED to land the job! Hope this helps you ✨ ---- 𝘐 𝘩𝘢𝘷𝘦 𝘢𝘭𝘴𝘰 𝘢𝘥𝘥𝘦𝘥 𝘮𝘺 𝘤𝘶𝘳𝘢𝘵𝘦𝘥 𝘭𝘪𝘴𝘵 𝘰𝘧 𝘳𝘦𝘴𝘰𝘶𝘳𝘤𝘦𝘴 𝘧𝘰𝘳 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 & 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘦 𝘪𝘯 𝘮𝘺 𝘳𝘰𝘢𝘥𝘮𝘢𝘱... 𝘣𝘶𝘵 𝘢𝘭𝘢𝘴 𝘓𝘪𝘯𝘬𝘦𝘥𝘐𝘯 𝘩𝘢𝘴 𝘢 𝘸𝘰𝘳𝘥 𝘭𝘪𝘮𝘪𝘵 𝘧𝘰𝘳 𝘱𝘰𝘴𝘵𝘴 :( 𝟭. 𝗣𝗶𝗰𝗸 𝗼𝗻𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲: 𝗦𝗤𝗟 𝗼𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 Write down your choice. Put it on your fridge. You are going to focus on just that one language. BUT you are going to get so good at it, that you don't need to use ChatGPT or Google when coding. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝘀𝗲𝗻𝘀𝗲 & 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗲𝗻𝘀𝗲 This is what will help you stand out in interviews. Because most other candidates do NOT have this skill. In short, this is how to use Data, Machine Learning & Analytics to drive business strategy. I read the book -- Cracking the Product Manager Interview -- cover-to-cover, two times over, before landing my first Data Science offer *𝘠𝘦𝘴, 𝘐 𝘬𝘯𝘰𝘸 𝘪𝘵 𝘪𝘴 𝘧𝘰𝘳 𝘗𝘳𝘰𝘥𝘶𝘤𝘵 𝘔𝘢𝘯𝘢𝘨𝘦𝘳𝘴, 𝘣𝘶𝘵 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘩𝘰𝘸 𝘐 𝘭𝘦𝘢𝘳𝘯𝘦𝘥 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺. 𝟯. 𝗕𝗿𝘂𝘀𝗵 𝘂𝗽 𝗼𝗻 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 Only focus on basic Stats and the core ML models. Don’t worry about advanced ML topics, like deep learning, computer vision, LLMs. Those will come later! 𝘛𝘩𝘦 𝘴𝘵𝘢𝘵𝘴 𝘤𝘰𝘯𝘤𝘦𝘱𝘵𝘴 𝘵𝘩𝘢𝘵 𝘺𝘰𝘶 𝘔𝘜𝘚𝘛 𝘬𝘯𝘰𝘸 𝘪𝘴 𝘪𝘯 𝘮𝘺 𝘳𝘰𝘢𝘥𝘮𝘢𝘱. 𝟰. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 — 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 Make these two lists: (1) Data Scientists you know, and (2) people in your target industry regardless of role Send every one of these folks a message on LinkedIn asking for a coffee chat. 𝘗𝘭𝘦𝘢𝘴𝘦 𝘸𝘳𝘪𝘵𝘦 𝘺𝘰𝘶𝘳 𝘮𝘦𝘴𝘴𝘢𝘨𝘦𝘴 𝘺𝘰𝘶𝘳𝘴𝘦𝘭𝘧. 𝘈𝘐-𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦𝘥 𝘮𝘦𝘴𝘴𝘢𝘨𝘦𝘴 𝘭𝘰𝘰𝘬 𝘪𝘯𝘴𝘪𝘯𝘤𝘦𝘳𝘦 𝘢𝘯𝘥 𝘱𝘦𝘰𝘱𝘭𝘦 𝘪𝘨𝘯𝘰𝘳𝘦 𝘵𝘩𝘰𝘴𝘦. 𝟱. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗹𝗶𝗸𝗲 𝗶𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 Start right away. Seriously, start practicing for interviews TODAY. If you only start after landing your first interview, it is already too late. ------ ♻️ Repost if you found this helpful

If I were to start my Data Analyst career from scratch in 2026, THIS is the roadmap I would follow: 𝟭/ 𝗟𝗲𝗮𝗿𝗻 𝗮𝗯𝗼𝘂𝘁 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 Understand basic database concepts - Definition and purpose of databases Master data table fundamentals - Rows, columns, and primary keys - Common data types Grasp the concept of relationships between tables - One-to-one, one-to-many, and many-to-many relationships - Foreign keys and table connections 𝟮/ 𝗟𝗲𝗮𝗿𝗻 𝗘𝘅𝗰𝗲𝗹 Start with the basics - Workbooks, worksheets, cells - Common data formats - Sorting and filtering data Learn formulas and functions - Common functions (SUM, AVERAGE, COUNT) - Complex functions (VLOOKUP, IF statements) - User-defined functions Create & customize charts - Chart types and their use cases - Customizing chart components - Conditional formatting for trends Master pivot tables for data analysis - Summarizing large datasets - Interpreting and presenting data insights 𝟯/ 𝗟𝗲𝗮𝗿𝗻 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 Start with the common commands - SELECT, FROM, WHERE - ORDER BY, LIMIT, DISTINCT Learn aggregations - Functions: SUM, COUNT, AVG, MIN, MAX - GROUP BY and HAVING clauses Master JOINs - Types: INNER, LEFT, RIGHT, FULL OUTER, CROSS - When to use each type Learn CTEs and sub-queries - CTEs using WITH clause - Sub-queries for data pre-processing 𝟰/ 𝗟𝗲𝗮𝗿𝗻 𝗮 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗼𝗹 Foundations of data visualization - Chart types and their applications - Data types and variables Learn data sources and preparation - Connecting to various sources - Cleaning and structuring data Master creating charts and dashboards - Common and complex chart creation - Interactive visualizations with filters - Combining charts into dashboards 𝟱/ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗲𝗻𝘀𝗲 𝗳𝗼𝗿 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Understand business fundamentals - Basic concepts: revenue, costs, profit - Key metrics across target industries Learn how businesses use data for decisions - Case studies on data-informed strategy - Translating business questions to data queries Develop communication skills - Presenting technical findings to non-technical audiences - Creating executive summaries - Storytelling with data Apply analytics to business problems - Portfolio projects from various industries - Gaining experience through internships or projects 𝟲/ 𝗕𝗼𝗻𝘂𝘀 𝗽𝗼𝗶𝗻𝘁𝘀: 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝘁𝗼 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 Dive into window functions - Definition and usage - Construction and aggregation commands - Functions: RANK(), LAG(), NTILE() Learn how to optimize SQL queries - Effective use of indexes - Writing efficient subqueries and joins Understand basics of Extract, Transform and Load processes - ETL process overview - Loading strategies and pipeline scheduling ------ ♻️ Did you find this useful? If so, repost it so others can see it too. 👋🏼 I post about the Data career every day. Follow me for more!

The Data Science job market has become like Tinder. It's not fair, but it's real. On Tinder, • 95% of people get 0 dates • 5% of people get all the dates In the job market, • 95% of Data Scientists get 0 job offers • 5% of them are dominating the offers. Anecdotally, I'm seeing this across my network. I had a friend who got offers from seven different companies in Q4 of last year. Another friend who got laid off, but landed 5 offers within a few months. So what sets the top 5% Data Scientists apart? 𝟭/ 𝗜𝗻𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝗲 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘀𝗸𝗶𝗹𝗹𝘀 No matter what interview question they get, they can very clearly describe their thought process, their analytical approach, and also any trade-offs. They pick only the most important things to say. They do not ramble and they do not dump a bunch of information on the interviewer. 𝟮/ 𝗗𝗲𝗲𝗽 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗼𝗳 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆... 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝘂𝘀𝗲 𝗱𝗮𝘁𝗮 𝘁𝗼 𝗴𝗲𝘁 𝘁𝗵𝗲𝗿𝗲 Every data scientist knows SQL and Python. Plus, with AI, the value of coding skills has decreased. But on the flip side what becomes increasingly valuable is the part of data science and analytics that happens before and after the coding. So that means knowing how to ✔️ Ask the right questions ✔️ Find opportunities to build the right models ✔️ Interpret & drive business value with insights+models 𝟯/ 𝗕𝗶𝗴 𝗯𝗿𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗻𝗮𝗺𝗲𝘀 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝗿𝗲𝘀𝘂𝗺𝗲 The reality is brand names open doors. If you have a big name on your resume, flaunt it. If you don't have one yet, go get one. Even a short contract or a visible project with a recognized company can change how your resume looks. ♻️ Repost if you found this useful!

If I were to start my Data Analyst career from scratch in 2026, THIS is the roadmap I would follow: 𝟭/ 𝗟𝗲𝗮𝗿𝗻 𝗮𝗯𝗼𝘂𝘁 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 Understand basic database concepts - Definition and purpose of databases Master data table fundamentals - Rows, columns, and primary keys - Common data types Grasp the concept of relationships between tables - One-to-one, one-to-many, and many-to-many relationships - Foreign keys and table connections 𝟮/ 𝗟𝗲𝗮𝗿𝗻 𝗘𝘅𝗰𝗲𝗹 Start with the basics - Workbooks, worksheets, cells - Common data formats - Sorting and filtering data Learn formulas and functions - Common functions (SUM, AVERAGE, COUNT) - Complex functions (VLOOKUP, IF statements) - User-defined functions Create & customize charts - Chart types and their use cases - Customizing chart components - Conditional formatting for trends Master pivot tables for data analysis - Summarizing large datasets - Interpreting and presenting data insights 𝟯/ 𝗟𝗲𝗮𝗿𝗻 𝗕𝗮𝘀𝗶𝗰 𝗦𝗤𝗟 Start with the common commands - SELECT, FROM, WHERE - ORDER BY, LIMIT, DISTINCT Learn aggregations - Functions: SUM, COUNT, AVG, MIN, MAX - GROUP BY and HAVING clauses Master JOINs - Types: INNER, LEFT, RIGHT, FULL OUTER, CROSS - When to use each type Learn CTEs and sub-queries - CTEs using WITH clause - Sub-queries for data pre-processing 𝟰/ 𝗟𝗲𝗮𝗿𝗻 𝗮 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗼𝗹 Foundations of data visualization - Chart types and their applications - Data types and variables Learn data sources and preparation - Connecting to various sources - Cleaning and structuring data Master creating charts and dashboards - Common and complex chart creation - Interactive visualizations with filters - Combining charts into dashboards 𝟱/ 𝗗𝗲𝘃𝗲𝗹𝗼𝗽 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗲𝗻𝘀𝗲 𝗳𝗼𝗿 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 Understand business fundamentals - Basic concepts: revenue, costs, profit - Key metrics across target industries Learn how businesses use data for decisions - Case studies on data-informed strategy - Translating business questions to data queries Develop communication skills - Presenting technical findings to non-technical audiences - Creating executive summaries - Storytelling with data Apply analytics to business problems - Portfolio projects from various industries - Gaining experience through internships or projects 𝟲/ 𝗕𝗼𝗻𝘂𝘀 𝗽𝗼𝗶𝗻𝘁𝘀: 𝗨𝗽𝘀𝗸𝗶𝗹𝗹 𝘁𝗼 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗦𝗤𝗟 Dive into window functions - Definition and usage - Construction and aggregation commands - Functions: RANK(), LAG(), NTILE() Learn how to optimize SQL queries - Effective use of indexes - Writing efficient subqueries and joins Understand basics of Extract, Transform and Load processes - ETL process overview - Loading strategies and pipeline scheduling ------ ♻️ Did you find this useful? If so, repost it so others can see it too. 👋🏼 I post about the Data career every day. Follow me for more!

Data Analysts won’t be replaced by AI. But a Data Analyst will be replaced by another Data Analyst, who is using AI. I avoided AI tools for a long time. I resisted what AI could do for me. Worrying that it would replace my thinking. Worrying that it would replace my skills. Worrying that it would replace me. But now I’m seeing AI as a tool. Just like how an abacus is a tool. Just like how Excel is a tool. Just like how SQL is a tool. They are simply means to make our work easier. They free up time for us to do high-value, strategic work. In 2026, every Data Analyst will need to use AI at work. The question is: which AI tools should you use? There are hundreds of tools (+ 𝘯𝘦𝘸 𝘰𝘯𝘦𝘴 𝘳𝘦𝘭𝘦𝘢𝘴𝘦𝘥 𝘸𝘦𝘦𝘬𝘭𝘺) I’ve been using and researching AI tools for the past months. Everything from ChatGPT to n8n to Notion. ---- Here are my favorite tools: *𝘕𝘰𝘵 𝘴𝘱𝘰𝘯𝘴𝘰𝘳𝘦𝘥, 𝘫𝘶𝘴𝘵 𝘮𝘺 𝘩𝘰𝘯𝘦𝘴𝘵 𝘳𝘦𝘤𝘰𝘮𝘮𝘦𝘯𝘥𝘢𝘵𝘪𝘰𝘯𝘴 𝗕𝗲𝘀𝘁 𝗰𝗼𝗱𝗶𝗻𝗴 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗳𝗼𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗻𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝘀: Cursor 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿 𝗶𝗻-𝗱𝗲𝗽𝘁𝗵 𝗰𝗼𝗱𝗶𝗻𝗴 𝘁𝗮𝘀𝗸𝘀: Anthropic Claude Code 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗹𝗶𝗴𝗵𝘁𝘄𝗲𝗶𝗴𝗵𝘁 𝗮𝗽𝗽𝘀: Replit 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻𝘀: Gamma 𝗕𝗲𝘀𝘁 𝗳𝗼𝗿 𝘄𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗘𝘅𝗰𝗲𝗹: Microsoft Co-pilot ---- Honestly, this is just scratching the surface. There are so many more tools that every Data Analyst can use on a daily basis (𝘦.𝘨. 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯, 𝘥𝘢𝘪𝘭𝘺 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘷𝘪𝘵𝘺 𝘵𝘰𝘰𝘭𝘴 𝘦𝘵𝘤). I’ll share more about this in the coming weeks! ♻️ Repost if you found this useful 👋🏽 I’m writing about this in more detail in my next newsletter, coming out tomorrow! Sign up to receive it (for free) in your inbox: www.askdatadawn.com

My back is sore. My feet hurt. My voice gone. First SXSW in the books! 🤠 Despite my physical ailments, my mind is buzzing! I wish I took more photos, but I guess it's a good thing that I was present in the moment. Learnings & takeaways coming soon... But first, photo dump!! Thank you SXSW for the most fun parties and interesting panels! 𝘗𝘚: 𝘥𝘰𝘦𝘴 𝘢𝘯𝘺𝘰𝘯𝘦 𝘬𝘯𝘰𝘸 𝘸𝘩𝘢𝘵 𝘬𝘪𝘯𝘥 𝘰𝘧 𝘣𝘪𝘳𝘥 𝘯𝘦𝘴𝘵 𝘵𝘩𝘪𝘴 𝘪𝘴?

I've had 9 jobs in the past 15 years. I wish my younger self knew this 👇 More money ≠ more happiness. But money made a difference (𝘵𝘰 𝘴𝘰𝘮𝘦 𝘦𝘹𝘵𝘦𝘯𝘵) I could ↳ buy more clothes ↳ go on more vacations ↳ afford to eat out more often And of course, I had to worry less. But some of the jobs I had came with: ↳ working late nights & weekends ↳ unkind, unhelpful managers ↳ so much stress I got sick Not fun fact: I was so stressed in my previous job, that it retriggered my autoimmune disease 🫣 So I wish my younger self knew that making more money wasn't going to make it happier. My stance now on what really matters in a job: ↳ working with people who I enjoy ↳ having passion for the space ↳ my physical & mental health ♻️ Repost if you enjoyed this post!

This roadmap got me Meta & Google Data Science jobs. Steal this to land a Data Science offer in 2026 👇 ---- I was a Data Scientist at Meta for 4 years. But it took me 6 years to land my first Data Science job. Through my many failed interviews, I learned a LOT about landing a Data Science job. And since then I have landed offers from 10+ different companies. I'm sharing the e͟x͟a͟c͟t͟ roadmap that worked for me. My roadmap is simple, curated and focused. The goal is to only spend time on learning what you NEED to land the job! Hope this helps you ✨ ---- 𝘐 𝘩𝘢𝘷𝘦 𝘢𝘭𝘴𝘰 𝘢𝘥𝘥𝘦𝘥 𝘮𝘺 𝘤𝘶𝘳𝘢𝘵𝘦𝘥 𝘭𝘪𝘴𝘵 𝘰𝘧 𝘳𝘦𝘴𝘰𝘶𝘳𝘤𝘦𝘴 𝘧𝘰𝘳 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 & 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘦 𝘪𝘯 𝘮𝘺 𝘳𝘰𝘢𝘥𝘮𝘢𝘱... 𝘣𝘶𝘵 𝘢𝘭𝘢𝘴 𝘓𝘪𝘯𝘬𝘦𝘥𝘐𝘯 𝘩𝘢𝘴 𝘢 𝘸𝘰𝘳𝘥 𝘭𝘪𝘮𝘪𝘵 𝘧𝘰𝘳 𝘱𝘰𝘴𝘵𝘴 :( 𝟭. 𝗣𝗶𝗰𝗸 𝗼𝗻𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲: 𝗦𝗤𝗟 𝗼𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 Write down your choice. Put it on your fridge. You are going to focus on just that one language. BUT you are going to get so good at it, that you don't need to use ChatGPT or Google when coding. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝘀𝗲𝗻𝘀𝗲 & 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗲𝗻𝘀𝗲 This is what will help you stand out in interviews. Because most other candidates do NOT have this skill. In short, this is how to use Data, Machine Learning & Analytics to drive business strategy. I read the book -- Cracking the Product Manager Interview -- cover-to-cover, two times over, before landing my first Data Science offer *𝘠𝘦𝘴, 𝘐 𝘬𝘯𝘰𝘸 𝘪𝘵 𝘪𝘴 𝘧𝘰𝘳 𝘗𝘳𝘰𝘥𝘶𝘤𝘵 𝘔𝘢𝘯𝘢𝘨𝘦𝘳𝘴, 𝘣𝘶𝘵 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘩𝘰𝘸 𝘐 𝘭𝘦𝘢𝘳𝘯𝘦𝘥 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺. 𝟯. 𝗕𝗿𝘂𝘀𝗵 𝘂𝗽 𝗼𝗻 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 Only focus on basic Stats and the core ML models. Don’t worry about advanced ML topics, like deep learning, computer vision, LLMs. Those will come later! 𝘛𝘩𝘦 𝘴𝘵𝘢𝘵𝘴 𝘤𝘰𝘯𝘤𝘦𝘱𝘵𝘴 𝘵𝘩𝘢𝘵 𝘺𝘰𝘶 𝘔𝘜𝘚𝘛 𝘬𝘯𝘰𝘸 𝘪𝘴 𝘪𝘯 𝘮𝘺 𝘳𝘰𝘢𝘥𝘮𝘢𝘱. 𝟰. 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 — 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 Make these two lists: (1) Data Scientists you know, and (2) people in your target industry regardless of role Send every one of these folks a message on LinkedIn asking for a coffee chat. 𝘗𝘭𝘦𝘢𝘴𝘦 𝘸𝘳𝘪𝘵𝘦 𝘺𝘰𝘶𝘳 𝘮𝘦𝘴𝘴𝘢𝘨𝘦𝘴 𝘺𝘰𝘶𝘳𝘴𝘦𝘭𝘧. 𝘈𝘐-𝘨𝘦𝘯𝘦𝘳𝘢𝘵𝘦𝘥 𝘮𝘦𝘴𝘴𝘢𝘨𝘦𝘴 𝘭𝘰𝘰𝘬 𝘪𝘯𝘴𝘪𝘯𝘤𝘦𝘳𝘦 𝘢𝘯𝘥 𝘱𝘦𝘰𝘱𝘭𝘦 𝘪𝘨𝘯𝘰𝘳𝘦 𝘵𝘩𝘰𝘴𝘦. 𝟱. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝘀 𝗹𝗶𝗸𝗲 𝗶𝘁’𝘀 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 Start right away. Seriously, start practicing for interviews TODAY. If you only start after landing your first interview, it is already too late. ------ ♻️ Repost if you found this helpful

I left with more questions than answers. Yes, going to SXSW does that to you. But in the best way possible.. I learned so much from the panelists at SXSW. But the best part of the conversations was that they made me THINK.. they led me to more questions! Sharing my favorite highlights from the week (𝘴𝘰𝘳𝘳𝘺 𝘪𝘧 𝘵𝘩𝘪𝘴 𝘪𝘴 𝘢𝘭𝘭 𝘰𝘷𝘦𝘳 𝘵𝘩𝘦 𝘱𝘭𝘢𝘤𝘦! 𝘐 𝘸𝘢𝘴 𝘣𝘰𝘶𝘯𝘤𝘪𝘯𝘨 𝘣𝘦𝘵𝘸𝘦𝘦𝘯 𝘤𝘳𝘦𝘢𝘵𝘰𝘳 & 𝘋𝘢𝘵𝘢 & 𝘵𝘦𝘤𝘩 𝘦𝘷𝘦𝘯𝘵𝘴.) 1️⃣ Children are in the 98th percentile of creativity, but by adults are 2nd percentile? Something about our education & upbringing is stifling our creativity. What is it? What even IS creativity? Our unique, random chaos. Find your weird. Know your weird Be your weird. Inspired by Zac Clark and Mike Pell 2️⃣ Want to sell digital products, but don't know how to start? Always start SMALL and test it with your audience. Use the feedback to slowly iterate on something bigger. Don’t spend days or weeks building products that no one wants! Inspired by conversations with the lovely Stan team: Lauren Giovannetti, Sara Mengesha, Emily Talas 3️⃣ Youtube is the most watch platform on TV. YouTube is also where brands are heavily investing in creator marketing. Brands are leaning into YouTube to help with their AI search. If you’re a creator, now is the time to lean into YouTube. I definitely am going to. Inspired by conversations & panels with Tara Knight 🧩, Creator Match 🧩 and John Battaglia, Carmela Luzzi 4️⃣ B2B creator marketing is growing *crazy fast Brands know that people don’t trust brands, they trust peers. That’s where creators come in. But how to build an audience that 𝘛𝘙𝘜𝘚𝘛𝘚 𝘺𝘰𝘶? ↳ Show your quirks: don’t feel like you have to be perfect on camera ↳ Show all sides of a product: if you’re using a product (even if it’s promoted), don’t be afraid to call out what doesn’t work well for you yet. No product is perfect! Inspired by the panel with Dedrick Boyington-Warmack, Fiona Turko, AJ Eckstein 🧩, and Will McTighe 5️⃣ The amount of Data captured & analyzed in sports will blow your mind Some examples of Data captured, monitored & used in sports: ↳ NBA teams are capturing the decibels of cheers in a stadium to predict if a moment will go viral ↳ The Masters measures the statistics and probability of EVERY single shot played (back to Tiger Woods early days) - Player movement and location data is measured down to multiple data points per second during NFL games I’ve already started to go down the rabbit hole of understanding how data is used in various sports. More to come on this! Inspired by the panel with Kameryn Stanhouse IBM, Jordan Kolosey, Brad Smith, Moro Ojomo Also special shoutout to Flodesk , Nikon, beehiiv, The Female Quotient, Stan for hosting some incredible pre- and post- SXSW events!