Amney Mounir

People only celebrate with you when you land the job. Not when you're sending applications every day. Not when rejection emails keep coming. Not when you are about to give up. Job searching is incredibly lonely. You try not to overthink every rejection. You try your best not to give up. You try to stay positive. Here are 3 things I wish someone had told me: 1. Being ghosted doesn't mean you're not qualified 2. Rejection doesn't mean you're not good 3. One YES can change your entire life You don't need 100 offers. You only need 1. 𝗬𝗢𝗨𝗥 𝗧𝗜𝗠𝗘 𝗪𝗜𝗟𝗟 𝗖𝗢𝗠𝗘. I'm rooting for you. ❤️ ——— ♻️ Repost if this motivated YOU to keep up 👋 Follow me, Amney, for more Data & AI content.

8 habits that helped me grow as a Data Analyst: (𝘚𝘢𝘷𝘦 𝘵𝘩𝘪𝘴. 𝘐𝘵 𝘮𝘪𝘨𝘩𝘵 𝘩𝘦𝘭𝘱 𝘺𝘰𝘶 𝘵𝘰𝘰.) 1. Understand the problem before jumping into data 3. Communicate insights in a simple way 4. Automate work you repeat often 5. Take initiative instead of waiting for requests 6. Focus on impact, not just being busy 7. Keep learning and be willing to change your mind 8. Get comfortable saying "I don't know" These habits helped me earn: → More trust from stakeholders → Opportunities to work on bigger projects → And eventually faster career growth and ownership This will take time. But making these small steps overtime will make a huge difference. And make you grow as a Data Analyst. ——— ♻️ Repost if you found this insightful 👋 Follow me, Amney, for more Data & AI content

Great Data Analysts don't just share numbers. They make the story easy to understand. I've seen a lot of analyses that are pulling the right data but missing to share the right message, with no clear insights and next steps. A few things that you can do to help you nail it: 𝟭) 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗺𝗮𝗶𝗻 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 If someone only reads the first section, what are the 2–3 things they should remember? 𝟮) 𝗚𝗶𝘃𝗲 𝘀𝗼𝗺𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 What problem are you trying to solve? Why does this analysis matter? 𝟯) 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁 𝘁𝗵𝗲 𝗸𝗲𝘆 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Keep them short. Keep them simple. Avoid making people search for the answer. 𝟰) 𝗘𝗻𝗱 𝘄𝗶𝘁𝗵 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 What should happen next? What decision are we making? This is how your analysis can gain clarity. And clarity is what drives impact. ——— ♻️ Repost this if you found it useful! 👋 Follow me, Amney, for more Data & AI content.

We don't 𝘰𝘯𝘭𝘺 do SQL, Analysis and use AI. We are way more than that as Data Analysts. Most of our time is spent on: - Data Cleaning - Exploratory Analysis - Meetings - Alignment - Prioritization - Defining Success - Influencing Decisions - Execution Understanding the problem, aligning people, and actually making something happen is where the real work and impact is. Don't get fooled by what they tell you at school. Being a Data Analyst is way more than that. And requires more soft skills than hard skills. ——— ♻️ Repost this if you found it useful. 👋 Follow me, Amney, for more Data and AI content

Top Data Analysts are NOT built on experience alone. Experience matters. But it's just one layer of many. The best ones I've worked with also had: - Strong relationships with stakeholders - Humility to admit when they're wrong - A desire to keep learning from others - Strong execution to actually make things happen And ironically, the people who think they're the best are often the hardest ones to work with. In my opinion, this is what separates people with a lot of experience from the very best Data Analysts. Do you agree? ♻️ Repost this if you found it useful. 👋 Follow me, Amney, for more Data & AI content

I put together this simple AI vocabulary cheat sheet. It covers 20 terms I think everyone should know: 1) LLM 2) Generative AI 3) Prompt Engineering 4) Context Window 5) Hallucinations 6) AI Agents 7) RAG 8) MCP 9) Semantic Search 10) Human-in-the-Loop ...and more. This list will help you understand enough so you can have better conversations, and use AI effectively. Save this for later. Which AI term should I be adding to the list? 👇