#Data Analysis Tutorial

Your LinkedIn profile is costing you opportunities 👀 Here’s what most data scientist profiles look like: ❌ headline = just job title ❌ about section = empty or boring resume copy ❌ experience = list of tasks nobody cares about What actually works (read until the end to get the prompts) **Your headline** // this is how recruiters FIND you. it needs to say what problems you solve, not just what your title is **Your about section** // the first 2 lines show before “see more” - if these don’t hook someone, they’re scrolling past. make them count. **Your experience** // “built ML models” ≠ impact. “built ML model that cut processing time 40% and saved 500 hours/month” = hiring managers taking notes My entire career shifted because of LinkedIn: → landed my current role through a DM → got interviewed on super data science podcast years later You don’t need to become a content creator. you just need a profile that works while you sleep. I made 3 AI prompts to optimize each section in under an hour 📬 Comment “LINKEDIN” and I’ll send them #data #jobs #ai

Ever felt like you’re too late, too non-tech, or just too overwhelmed to break into Al or Data? I made this post for you. Comment ‘Al’ for the guide. I’ve seen marketers, designers, HR pros & freshers switch to data and thrive. You’re one decision away from a future-proof career. Save this post, tag someone who needs a reality check, and drop your fear below. Let’s unpack it together. #datascience #Alcareer #upskill #careerchange #DataAnalyst AlforEveryone TechCareer

Which AI tool do you use the most in your day-to-day work? ㅤ #datascience #dataanalytics #techcareers #aiworkflow #careeradvice #aitools

with the rise in AI being used for simple data analysis and the job market becoming more competitive, it’s so important to have more than just technical skills to become a data analyst 💻 alongside the core technical skills like SQL, Excel, a reporting tool and maybe Python/R, the most important additional skills are: 1. Understanding business metrics relevant to the industry you’re trying to work in 2. Being able to communicate your insights clearly, and knowing which is the best method 3. Knowing how to validate data - you can’t always take data at face value! Know how to use debuggers like GTM to understand how data is being tracked

If you want to land a data job in 2026, use The Warm Introduction Method. Comment “Script” to get my full LinkedIn networking framework for data scientists. #data #tech #productivity

with the rise in AI being used for simple data analysis and the job market becoming more competitive, it’s so important to have more than just technical skills to become a data analyst 💻 alongside the core technical skills like SQL, Excel, a reporting tool and maybe Python/R, the most important additional skills are: 1. Understanding business metrics relevant to the industry you’re trying to work in 2. Being able to communicate your insights clearly, and knowing which is the best method 3. Knowing how to validate data - you can’t always take data at face value! Know how to use debuggers like GTM to understand how data is being tracked

you don’t need to start over. you need to start where you are. 🤍 if you’re a data analyst that might look like: asking ChatGPT to explain a SQL error instead of googling it using AI to write the first draft of your data story instead of staring at a blank doc dropping a messy dataset into Claude and asking it what’s interesting automating the part of your weekly report that takes 45 minutes but adds zero value you’re not behind. you’re just one small experiment away from feeling like yourself again but faster. 💛 save this and try one this week. #data #womenindata #careerintech #datascience #analytics

What if I will tell you Data Analyst roles are OVER 🚨 The future is all about building AI-powered tools that do the work for you! 💡 Want to learn how to build your own Text2SQL Agent? COMMENT AI below and I’ll send you the full guide! 👇

Ever felt like you’re too late, too non-tech, or just too overwhelmed to break into Al or Data? I made this post for you. Comment ‘Al’ for the guide. I’ve seen marketers, designers, HR pros & freshers switch to data and thrive. You’re one decision away from a future-proof career. Save this post, tag someone who needs a reality check, and drop your fear below. Let’s unpack it together. #datascience #Alcareer #upskill #careerchange #DataAnalyst AlforEveryone TechCareer

A lot of people ask me why they’re applying to data science roles but not getting shortlisted. Most of the time, it comes down to a few very fixable things. Reason 1: Applying to the wrong roles ✦ Fix: Slow down and be selective. Apply to roles where you match most of the core requirements, not everything under “data” or “AI.” Read the role carefully and ask yourself: can I clearly do this job today or grow into it fast? Reason 2: Resume not tailored ✦ Fix: Stop using one resume everywhere. For each role, tweak your bullets to match what the job actually cares about. Use the language from the job description and show alignment, not just a list of skills. Reason 3: No real projects or portfolio ✦ Fix: Build a few strong, end-to-end projects. Pick a domain, solve a real problem, explain your decisions, and show results. One solid project beats ten half-done ones. Reason 4: No networking or referrals ✦ Fix: Don’t rely only on job portals. Talk to people in the industry, comment thoughtfully, attend events, and ask for referrals once you’ve built genuine connections. Most roles are filled through people, not applications. None of this is about working harder. It’s about being more intentional. If you fix these four things, your chances improve a lot.

I still Google basic SQL and Python syntax Because nobody memorizes everything..we just know how to find the right answer fast. 2. I spend more time cleaning data than actually analyzing it Most of the job isn’t sexy dashboards!! it’s fixing broken spreadsheets and messy tables. 3. Half my insights come from common sense, not fancy models The data usually confirms what good business judgment already suspected. 4. I’ve built dashboards that nobody really uses Sometimes stakeholders just want a number to back up a decision they already made. 5. I’ve rerun the same analysis five different ways just to be safe Because being “almost right” in data is still being wrong. 6. I still get imposter syndrome when someone throws a new tool at me Even after years in the field, tech moves fast and nobody truly feels caught up. 7. I’ve learned that communication matters more than perfect analysis If people don’t understand it, it doesn’t matter how accurate it is. Follow if you’re building a real career in data & AI and lowkey felt this ai, data analytics career, career advice, growth

Follow and comment “profile” to have a chance to get your Linkedin profile reviewed. If you’re currently looking for a data job, ironing out your Linkedin profile could be the difference between getting ignored and getting interviews. Reviewing @jessramosdata profile today. #data #tech #student