557.7K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
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IBJunior vs Senior: A python program that removes duplicate elements from a list while keeping the original order. #python #learnpython #programming
#datascience #pythonprogramming
@ibrahimmanarbkh
254.2K
KRThe Ultimate Pathfinding Algorithm Race is HERE! 🚀🏁 Who will find the perfect path first?
Watch A*, Dijkstra, BFS, DFS, Greedy, Bi-BFS, and Prim go head-to-head in this stunning coding visualization. 💻 Wait until the end to see which one expands the nodes fastest and finds the most optimal route!
1️⃣ Dijkstra - reliable but exhaustive
2️⃣ A* (A-Star) - the smart heuristic speedster
3️⃣ Greedy - fast but risky
4️⃣ Bi-BFS - meeting halfway! 🤯
5️⃣ BFS - slow and steady
6️⃣ DFS - diving into dead ends
7️⃣ Prim - the spanning tree master
Which one is your go-to in coding interviews? Let me know in the comments! 👇
Don't forget to LIKE and FOLLOW for more satisfying and educational algorithm visualizations! 🌟
#algorithmrace #programming #computerscience #tech #dijkstra #astar #pathfinding #softwareengineering #datastructures #codinglife #webdev #datascience
@kreggscode
1.8M
SHConfused 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
@shailjamishra__
279.2K
THcomment “learn” and I will DM you the links 🔗
#softwareengineer #ai #machinelearning #datascience #swe
@thad.codes
371.7K
DAHere are 3 unique data science projects you can build in a weekend (2026 World Cup)
easy. a World Cup match outcome predictor. predict win, loss, or draw using historical FIFA data. tech stack: Python, Pandas, Scikit-learn, and Streamlit to deploy it.
medium. a player performance dashboard. pull player stats from Transfermarkt, visualize everything, and cluster players by playing style. tech stack: Python, Pandas, Plotly, and Seaborn for visualization with KMeans for clustering.
hard. a real time World Cup sentiment tracker. pull live tweets during matches, run sentiment analysis as goals happen, and visualize how public opinion shifts in real time. tech stack: Python, Tweepy for the Twitter API, HuggingFace Transformers for sentiment analysis, and Plotly Dash for the live dashboard.
comment “World cup” for resources to help you out along the way.
#machinelearning #datascience #ai #python #cs
@darshcoded
107.1K
EQThis Math Concept Rules the World! #Statistics #Math #DataScience #Probability #CentralLimitTheorem Discover the magic of the Central Limit Theorem in this quick visual guide! Have you ever wondered why random data often forms a perfect bell curve? By starting with a single die roll and moving to the sum of 10 dice, we demonstrate how independent random variables converge into a normal distribution. This fundamental principle of statistics explains everything from heights to financial markets. Whether you are a student or just curious about math, this visualization makes the Central Limit Theorem easy to understand. Watch how randomness transforms into order right before your eyes!
@equationsinmotion
359.2K
VOData Science Trivia | Did you get the last one? #datascience #dataanalytics #datascientist #math #finance
@voodiesinterviews
1.7M
SHLists are one of the most frequently used data structures in Python. Whether you’re cleaning data, transforming records, or building quick scripts for analysis, understanding list methods can significantly improve your efficiency.
Here’s what makes them powerful:
• Adding elements dynamically when new data arrives
• Counting occurrences to validate patterns
• Copying lists safely before transformations
• Locating positions of specific values
• Inserting elements at precise indexes
• Reversing sequences for logical operations
• Removing items selectively
• Clearing data structures when resetting workflows
In real-world analytics, these small operations save time, reduce bugs, and keep your code clean.
If you work with Python for data analysis, automation, scripting, or interviews, list methods are foundational. They appear simple, but they control how your data flows.
Save this for revision and quick recall before interviews or while practicing.
[python, pythonlists, listmethods, pythonforanalysis, dataanalysis, datascience, coding, programming, pythonlearning, pythonbasics, pythoninterview, analystskills, datastructures, codingpractice, techskills, analytics, automation, softwaredevelopment, pythondeveloper, learnpython, pythoncode, datacleaning, eda, scripting, developerlife, techcareer, programmingtips, pythoneducation, pythoncommunity, ai, machinelearning, businessanalytics, techgrowth, careerintech, dataengineering, dataanalyticslife, pythonprojects, codingjourney, learncoding, analyticscareer, developercommunity, pythontraining, interviewprep, dataprocessing, techcontent, pythonresources, programminglife, coderlife, pythonpractice, techlearning]
#Python #DataAnalytics #Programming #DataScience #TechCareer
@she_explores_data
505.7K
SRThis video by Prof. Amar G Bose at MIT, and founder at Bose Corporation (he passed away in 2013) has been my go to when I cannot solve a problem, or I teach my students. You should watch the entire video (shared in the comments) to understand the entire context, and more such examples. But the very idea of learning & solving for the first principles (by dividing into smaller pieces till you reach the atoms and the fundamentals) & systems thinking (by connecting those atoms to create larger molecules of knowledge in a hierarchical fashion) is the way to build your foundations in knowledge, and enjoy the taste of learning, and solving. More importantly, this is so scalable that if you solve one problem deeply, it will scale to 100 other problems that you are yet to solve. This is long-term thinking. My only goal of teaching and creating content is inspiring you, helping you, and reminding you of these very elegant and simple principles. All the best! #ai #datascience #mathematics #machinelearning
@srijit.math
67.2K
PRIf you want to compare change in the same people, That’s when paired t test comes in.
Before vs after.
[statistics made simple, phd life, academia, research methods, data storytelling, learn statistics]
#statistics #datascience #dataanalytics #research #machinelearning
@professor_chetna
724.1K
WOIf you can build these 4 from scratch, you’re not a beginner anymore:
• Autoencoder
• Variational Autoencoder (VAE)
• Attention Mechanism
• GAN
Each one teaches you something different: compression, probabilistic modeling, transformers, adversarial training.
Autoencoder: https://github.com/nathanhubens/Autoencoders
VAE : https://github.com/bvezilic/Variational-autoencoder
Attention mechanism : https://github.com/uzaymacar/attention-mechanisms
GAN : https://github.com/eriklindernoren/PyTorch-GAN
#machinelearning #datascience #deeplearning #ai
@workiniterations
✨ #Datascience Discovery Guide
Instagram hosts 3 million posts under #Datascience, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.
#Datascience is one of the most engaging trends on Instagram right now. With over 3 million posts in this category, creators like @shailjamishra__, @she_explores_data and @ibrahimmanarbkh are leading the way with their viral content. Browse these popular videos anonymously on Pictame.
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💡 Top performing posts average 1.4M views (2.1x above average). Moderate competition - consistent posting builds momentum.
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🔥 #Datascience shows high engagement potential - post strategically at peak times
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✍️ Detailed captions with story work well - average caption length is 581 characters
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