#Data Driven Insights

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#Data Driven Insights Reel by @jessramosdata (verified account) - Comment "project" for my full video that breaks each of these projects down in detail with examples from my own work. If you're using the Titanic, Ir
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@jessramosdata
Comment “project” for my full video that breaks each of these projects down in detail with examples from my own work. If you’re using the Titanic, Iris, or COVID-19 dataset for data analytics projects, STOP NOW! These are so boring and over used and scream “newbie”. You can find way more interesting datasets for FREE on public data sites and you can even make your own using ChatGPT or Claude! Here are the 3 types of projects you need: ↳Exploratory Data Analysis (EDA): Exploring a dataset to uncover insights through descriptive statistics (averages, ranges, distributions) and data visualization, including analyzing relationships between variables ↳Full Stack Data Analytics Project: An end-to-end project that covers the entire data pipeline: wrangling data from a database, cleaning and transforming it. It demonstrates proficiency across multiple tools, not just one. ↳Funnel Analysis: Tracking users or items move from point A to point B, and how many make it through each step in between. This demonstrates a deeper level of business thinking by analyzing the process from beginning to end and providing actionable recommendations to improve it Save this video for later + send to a data friend!
#Data Driven Insights Reel by @sundaskhalidd (verified account) - Repost to share with friends ♻️ Here's how to become a data analyst in 2026 and beyond? 📈 The original video was 5 minutes long and I had to cut it d
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@sundaskhalidd
Repost to share with friends ♻️ Here’s how to become a data analyst in 2026 and beyond? 📈 The original video was 5 minutes long and I had to cut it down to 3 minutes because instagram. One part that got cut off was the job market. Should I post a part 2? what are other skills that would you add to the list?? #dataanalysis #dataanalyst #sql #python
#Data Driven Insights Reel by @fitwit_krish (verified account) - Ep44- Stop learning everything!! Are you learning everything in data analytics?? that'sthe biggest mistake and the reason people stay stuck with out
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@fitwit_krish
Ep44- Stop learning everything!! Are you learning everything in data analytics?? that’sthe biggest mistake and the reason people stay stuck with out getting a job. Interviews don’t test random topics. They test specific skills. Right tools and project scenario based knowledge. As an experienced data analyst with over 8 years of experience i have created a detailed pdf from my data analyst journey on which topics needs to be covered. Which needs to be ignored. How to prepare your own project based portfolio. Answer questions with right tools and skill. Below are the details included in pdf. ✔️ What to learn (and what to skip) ✔️ Skills interviewers actually ask ✔️ Role-wise roadmap (Fresher → Job ready) ✔️ Project clarity + interview direction This is only for serious learners. Hence i made it as a paid one which costs a minimal fee. Follow and comment EP-44. I’ll send you the link directly. [data analytics, journey, road map, data analyst, jobs] #dataanalyst #journey #roadmap #skills #growth
#Data Driven Insights Reel by @the.datascience.gal (verified account) - Here's a roadmap to help you go from a software engineer to a data scientist 👩‍💻 👇 If you're tired of writing vanilla apps and want to build ML sy
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@the.datascience.gal
Here’s a roadmap to help you go from a software engineer to a data scientist 👩‍💻 👇 If you’re tired of writing vanilla apps and want to build ML systems instead, this one’s for you. Step 1 – Learn Python and SQL (not Java, C++, or JavaScript). → Focus on pandas, numpy, scikit-learn, matplotlib → For SQL: use LeetCode or StrataScratch to practice real-world queries → Don’t just write code—learn to think in data Step 2 – Build your foundation in statistics + math. → Start with Practical Statistics for Data Scientists → Learn: probability, hypothesis testing, confidence intervals, distributions → Brush up on linear algebra (vectors, dot products) and calculus (gradients, chain rule) Step 3 – Learn ML the right way. → Do Andrew Ng’s ML course (Deeplearning.ai) → Master the full pipeline: cleaning → feature engineering → modeling → evaluation → Read Elements of Statistical Learning or Sutton & Barto if you want to go deeper Step 4 – Build 2–3 real, messy projects. → Don’t follow toy tutorials → Use APIs or scrape data, build full pipelines, and deploy using Streamlit or Gradio → Upload everything to GitHub with a clear README Step 5 – Become a storyteller with data. → Read Storytelling with Data by Cole Knaflic → Learn to explain your findings to non-technical teams → Practice communicating precision/recall/F1 in simple language Step 6 – Stay current. Never stop learning. → Follow PapersWithCode (it's now sun-setted, use huggingface.co/papers/trending, ArXiv Sanity, and follow ML practitioners on LinkedIn → Join communities, follow researchers, and keep shipping new experiments ------- Save this for later. Tag a friend who’s trying to make the switch. [software engineer to data scientist, ML career roadmap, python for data science, SQL for ML, statistics for ML, data science career guide, ML project ideas, data storytelling, becoming a data scientist, ML learning path 2025]
#Data Driven Insights Reel by @aanooook - Here's thing i wish i knew before becoming a data analyst 📊 1. SQL is your best friend - it gets you through 80% of the work. 2. Excel isn't basic -
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@aanooook
Here’s thing i wish i knew before becoming a data analyst 📊 1. SQL is your best friend — it gets you through 80% of the work. 2. Excel isn’t basic — pivot tables & formulas are used daily. 3. Visualization tools (Tableau/Power BI) make you stand out. 4. Communication > technical sometimes — if you can’t explain insights, they don’t matter. 5. You don’t need 100 certifications — projects & practice speak louder. 6. Most of your time is data cleaning — not fancy dashboards. 7. Business understanding is key — knowing why the data matters is more valuable than just coding. 8. Networking gets you jobs faster than applications — LinkedIn visibility + projects > sending 500 resumes [data analytics,data analyst, corporate, data]
#Data Driven Insights Reel by @vee_daily19 - If you want to crack Data Science jobs in the next 30 days, here's the three step process which you will follow which literally no one talks about. .
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@vee_daily19
If you want to crack Data Science jobs in the next 30 days, here’s the three step process which you will follow which literally no one talks about. . . . #datascience #data #interview
#Data Driven Insights Reel by @itssimplyjordan (verified account) - How I'd become a Data Analyst in 2026 ⬇️ 1️⃣ Get in the door (any role) Data Analyst titles are hard to land, degree or not. So get into any role at
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@itssimplyjordan
How I’d become a Data Analyst in 2026 ⬇️ 1️⃣ Get in the door (any role) Data Analyst titles are hard to land, degree or not. So get into any role at a tech forward company with an analytics team/department . Sales. Ops. Data entry. Work up! Prove your value. That’s exactly what I did. 2️⃣ Improve what’s in front of you Look for small things you can control: • Excel • MS Access • Power Query Invoices research (ms access), trends, reports doesn’t matter, anything YOU can do. 3️⃣ Learn only what you need Target the tools you’re already working with/access too. (DataCamp and Codecademy worked for me) 4️⃣ Build something real Not tutorials. Build a tool people (and you) actually use even if it’s simple. Examples could be: Using forms and VBA/SQL in ms access to build a form for people to researching invoices! 5️⃣ Show your work Demo it. Explain the impact. Who uses it. Why it matters. And how it helps! 6️⃣ Say yes to opportunities Take on EVERYTHING, prove you can do the work, even if it adds more stress. That’s how you stack proof for the next role. No degree required. 👉 Follow if you’re breaking into data. #dataanalyst #howto #breakintotech #nodegree #2026goals
#Data Driven Insights Reel by @phoebeslifeindata - watch this if you want to become a data analyst in 2026, these are my top simple tips 📊 1. Learn SQL: its the tool you'll use to get data from datab
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@phoebeslifeindata
watch this if you want to become a data analyst in 2026, these are my top simple tips 📊 1. Learn SQL: its the tool you’ll use to get data from databases, and then use to analyse business performance 2. Learn Excel or something similar: it’s great for ad hoc analysis and building engaging charts and diagrams 3. Get familiar with a reporting tool, you don’t need to be great at this just an understanding is fine 4. The core skills are communicating your insights clearly and understanding business metrics Save this and come back to it when you’re planning what to learn, I have links on my profile for courses/guides for each of these aspects!
#Data Driven Insights Reel by @sdw.online (verified account) - 🟠Comment '30' and I'll send you data project tutorials on YouTube I made for you🟠
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@sdw.online
🟠Comment '30' and I'll send you data project tutorials on YouTube I made for you🟠
#Data Driven Insights Reel by @jayenthakker - You probably Googled "How to learn Data Analytics"… And got 100 tabs open. Courses, tools, bootcamps, blogs, YouTube videos, each saying "Start here.
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@jayenthakker
You probably Googled “How to learn Data Analytics”… And got 100 tabs open. Courses, tools, bootcamps, blogs, YouTube videos, each saying “Start here.” But no one told you what not to do. No one gave you a starter kit that actually made sense. So I built. And now I use this same plan to guide beginners I mentor. Here’s the Data Analytics Starter Kit I wish everyone should have👇 1. Start with Excel → It’s not outdated, it’s underrated. → Master formulas, Pivot Tables, and charts. 2. Then SQL → Learn how to query real data. → SELECT, WHERE, GROUP BY, and JOIN. That’s 80% of your job. 3. Add one viz tool → Pick Tableau or Power BI. → Focus on storytelling, not fancy dashboards. 4. Forget 100-hour courses Instead, build 3 small projects: ⤷ A Sales Dashboard in Excel ⤷ A Customer Retention Report in SQL ⤷ A Visual Story in Tableau 5. Use GitHub + LinkedIn → Document your projects. → Share your process. → Visibility builds credibility. 6. Give it 6–8 weeks Learn 1 skill → Apply it → Move to the next. If you're just starting out, don't chase 10 tools. Build your foundation first. Want my full Data Analytics Starter Kit with a roadmap, tool list, and project ideas? Drop "Community" to join my community here. #datavisualization #dataanalyst #datascience #data #sql #excel #python #career #careerswitch #trending #learning #interviewtips #india #metricminds
#Data Driven Insights Reel by @data_pumpkin - In my first years as a data scientist, I wasted hours on broken SQL, slow pandas scripts, messy Flask deployments, and "works on my machine" chaos. T
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@data_pumpkin
In my first years as a data scientist, I wasted hours on broken SQL, slow pandas scripts, messy Flask deployments, and “works on my machine” chaos. These 4 tools fixed that: • dbt → modular, documented SQL transformations • Polars → faster, cleaner alternative to pandas • FastAPI → quick, reliable model deployment • Docker → consistent environments, no more deployment nightmares If you’re just starting out, learning these early will save you months of frustration.

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