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#Data Scientist Reel by @sundaskhalidd (verified account) - Comment 'Projects' to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist i
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@sundaskhalidd
Comment ‘Projects’ to get 5 Data Scientist Project ideas and a plan 👩🏻‍💻 ♻️ repost to share with friends. Here is how to become a data scientist in 2026 and beyond 📈 the original video was 4 min Andi had to cut it down to 3 because instagram. Should I do a part 3v what are other skills that you would add to the list and let me know what I should cover in the next video 👩🏻‍💻 #datascientist #datascience #python #machinelearning #sql #ai
#Data Scientist Reel by @askdatadawn (verified account) - This is the EXACT order I would learn Data Science in 2026. Hi 😊 my name is Dawn. I've been a Data Scientist at Meta, Patreon and other startups. An
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@askdatadawn
This is the EXACT order I would learn Data Science in 2026. Hi 😊 my name is Dawn. I’ve been a Data Scientist at Meta, Patreon and other startups. And have coached 20+ clients into landing their dream Data jobs in the past year. 1️⃣ Learn SQL SQL is a must-have skill for every data professional because it’s the primary way you get data OUT of a database. It’s also a very easy coding language to learn, so I would start there. Use Interview Master to learn and practice SQL (link in bio): → Learn SQL: www.interviewmaster.ai/content/sql → Practice SQL: www.interviewmaster.ai/home 2️⃣ Start building Product Sense & Business Sense Product sense & business sense basically means you know how to use Data to solve real problems. I would start building this “soft” skill early because (1) it takes time to really learn this, and (2) as you’re learning Stats and Python, you already have context on how these might be used in the real world. I found the book: Cracking the PM Career to be super helpful before I landed my first Data Science job. 3️⃣ Learn Statistics How much Stats do you need for Data Science? Just the foundations, but you need to know it really really well. → Descriptive statistics → Common distributions → Probability and Bayes’ Theorem → Basic Machine Learning models → Experimentation concepts → A/B experiment design Check out Stanford’s Introduction to Statistics, which is free on Coursera. 4️⃣ Learn Python Python is the #1 skill for Data Scientists in 2025, but I put it 4th on this list because I find that it builds on skills 1-3. I learned Python on my own using DataCamp’s Python Data Fundamentals (link in bio). 5️⃣ Use AI-assisted coding tools Many data scientists are already using tools, like Claude Code & Cursor, to 2x their productivity. And also many companies are evaluating you on your use of AI during interviews. #datascience #datascientist
#Data Scientist Reel by @shailjamishra__ (verified account) - Confused between becoming a Data Scientist or an AI Engineer? Both roles are powerful-but require different skills, tools, and thinking. Comment "Ro
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@shailjamishra__
Confused between becoming a Data Scientist or an AI Engineer? Both roles are powerful—but require different skills, tools, and thinking. Comment “Roles” and I’ll send you a detailed roadmap for both 🚀 Got questions or feeling stuck? Drop your doubts in the comments—I’ll personally help you get clarity and move forward on your journey. #datascientist #datascience #ai #aiengineer #careergrowth
#Data Scientist Reel by @young4stem - Where are all our data scientists at! 👀👇🏻 #young4stem #datascience #job #reel #stem #computerscience
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@young4stem
Where are all our data scientists at! 👀👇🏻 #young4stem #datascience #job #reel #stem #computerscience
#Data Scientist 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 Scientist Reel by @chrisoh.zip - The best projects serve a real use case Comment "data" for all the links and project descriptions #tech #data #datascience #ml #explore
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@chrisoh.zip
The best projects serve a real use case Comment “data” for all the links and project descriptions #tech #data #datascience #ml #explore
#Data Scientist Reel by @codewithboi - Data Science is art paired with logic 😊. In my soft girl in tech era 💕. Calm lights, chaotic datasets #datascience #stem #womeninstem #study #com
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@codewithboi
Data Science is art paired with logic 😊. In my soft girl in tech era 💕. Calm lights, chaotic datasets #datascience #stem #womeninstem #study #computerscience #coding #programming #tech #explore #ai #python #dev #tools #study #datascientist #data #design #software #codinglife #programmer #datascience #build #learning #growth #technology #information
#Data Scientist Reel by @devshree.17 - Data Science Roadmap from a Googler❤️ Recently I spoke to several friends here in the Bay Area, one of them is a data scientist at google, some are d
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@devshree.17
Data Science Roadmap from a Googler❤️ Recently I spoke to several friends here in the Bay Area, one of them is a data scientist at google, some are data scientists at walmart, and a few others working in California! Based on their 4-10 years of experience in the field, I have designed a beginner friendly roadmap: ✅Covering 4 Month Timeline ✅Topics to cover and their resources ✅Frequently asked questions #datascience #google #softwareengineer #indiansinusa #jobsearch
#Data Scientist Reel by @techjobsin2 - How much does a DATA SCIENTIST make? #viral #reels #techjobsin2minutes #amazon
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@techjobsin2
How much does a DATA SCIENTIST make? #viral #reels #techjobsin2minutes #amazon
#Data Scientist Reel by @woman.engineer (verified account) - 📍How to prepare for Data Scientist role in 2026 🚀 CORE SKILLS YOU MUST MASTER: Programming You must be fluent in: ● Python ● NumPy ● Pandas ● S
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@woman.engineer
📍How to prepare for Data Scientist role in 2026 🚀 CORE SKILLS YOU MUST MASTER: Programming You must be fluent in: ● Python ● NumPy ● Pandas ● Scikit-learn Writing clean, readable, bug free code Data transformations without IDE help Expect: ● Data cleaning ● Feature extraction ● Aggregations ● Writing logic heavy code SQL Almost every Data Science role tests SQL. You should be comfortable with: ● Joins - inner, left, self ● Window functions ● Grouping & aggregations ● Subqueries ● Handling NULLs Statistics & Probability: ● Probability distributions ● Hypothesis testing ● Confidence intervals ● A/B testing ● Correlation vs causation ● Sampling bias Machine Learning Fundamentals. You must know: ● Supervised vs Unsupervised learning ● Regression & Classification ● Bias Variance tradeoff ● Overfitting / Underfitting Evaluation metrics: ● Accuracy ● Precision / Recall ● F1-score ● ROC-AUC ● RMSE FEATURE ENGINEERING & DATA UNDERSTANDING: ● This is where strong candidates stand out. ● Handling missing data ● Encoding categorical variables ● Feature scaling ● Outlier treatment CORE SKILLS YOU MUST MASTER: Programming You must be fluent in: ● Python ● NumPy ● Pandas ● Scikit-learn Writing clean, readable, bug free code Data transformations without IDE help Expect: ● Data cleaning ● Feature extraction ● Aggregations ● Writing logic heavy code SQL Almost every Data Science role tests SQL. You should be comfortable with: ● Joins - inner, left, self ● Window functions ● Grouping & aggregations ● Subqueries ● Handling NULLs Statistics & Probability: ● Probability distributions ● Hypothesis testing ● Confidence intervals ● A/B testing ● Correlation vs causation ● Sampling bias Machine Learning Fundamentals. You must know: ● Supervised vs Unsupervised learning ● Regression & Classification ● Bias Variance tradeoff ● Overfitting / Underfitting Evaluation metrics: ● Accuracy ● Precision / Recall ● F1-score ● ROC-AUC ● RMSE +++ for more look at the comment #datascientist #aiengineer #softwareengineer #datascience #dataengineer
#Data Scientist Reel by @chrispathway (verified account) - Here is a full roadmap on how to get started with Data Science. Comment "DATA" for the full roadmap pdf. #datascience #machinelearning #coding #ai #u
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@chrispathway
Here is a full roadmap on how to get started with Data Science. Comment “DATA” for the full roadmap pdf. #datascience #machinelearning #coding #ai #university
#Data Scientist Reel by @darshcoded - most people fail data science interviews not because they are not smart enough but because they prepared the wrong things. SQL, statistics, machine le
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@darshcoded
most people fail data science interviews not because they are not smart enough but because they prepared the wrong things. SQL, statistics, machine learning fundamentals, python, and case studies, these are the five things every data science interview is actually testing you on and most people only prepare for one or two of them. I put together the exact free resources I would use to prepare for every single round. save this post because if you have a data science interview coming up this is the only roadmap you need. #datascience #cs #intern #sql #python

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