224.3K
PRrag evaluation
#datascience #machinelearning #learningtogether #womeninstem #progresseveryday #tech #generativeai #ai #consistency
@priyal.py
#Dag Data Processing Techniques
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SURepost 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
@sundaskhalidd
155.7K
DA🚨 Want to become a Data Analyst but don’t know where to start? 👀
I’ve got you covered — Microsoft has launched a dedicated learning path with free resources to help you master Data Analytics step by step! 📊
💬 Comment “DATA” and I’ll DM you the complete roadmap + official Microsoft resources.
✅ Beginner to advanced topics covered
✅ 100% FREE learning materials
✅ Certificate-ready path to build your career
🔥 This is your sign to start learning data analytics the right way — straight from Microsoft! 🚀
@data_with_anurag
2.5M
PR1. Netflix Show Clustering
Group similar shows using K-Means based on genre, rating, and duration.
Tech Stack: Python, Pandas, Scikit-learn, Seaborn
2. Spotify Audio Feature Analyzer
Analyze songs by tempo, energy and danceability using Spotify API.
Tech Stack: Python, Spotipy, Matplotlib, Plotly
3. YouTube Trending Video Analyzer
Discover what makes a video go viral.
Tech Stack: Python, Pandas, BeautifulSoup, Seaborn
4. Resume Scanner using NLP
Parse and rank resumes based on job description matching.
Tech Stack: Python, SpaCy, NLTK, Streamlit
5. Crypto Price Predictor
Predict BTC/ETH prices using historical data.
Tech Stack: Python, LSTM (Keras), Pandas, Matplotlib
6. Instagram Hashtag Recommender
Suggest hashtags based on image captions or niche.
Tech Stack: Python, NLP, TF-IDF, Cosine Similarity
7. Reddit Sentiment Tracker
Analyze community sentiment on hot topics using Reddit API.
Tech Stack: Python, PRAW, VADER, Plotly
8. AI Job Postings Dashboard
Scrape and visualize job trends by tech stack and location.
Tech Stack: Python, Selenium/BeautifulSoup, Streamlit
9. Airbnb Price Estimator
Predict listing prices based on location and amenities.
Tech Stack: Python, Scikit-learn, Pandas, XGBoost
10. Food Calorie Image Classifier
Estimate calories from food images using CNNs.
Tech Stack: Python, TensorFlow/Keras, OpenCV
Each project can be completed in 1-2 weekends.
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #projects
@priyal.py
332.6K
TOUse the Instant Data Scraper to quickly pull data from websites!
#scrape #data
@topclickmediasa
36.5K
NA🔥 I spent 2 weeks simplifying RAG.
Here’s the clearest breakdown you’ll ever see ..
RAG turns a general LLM into a domain expert using your knowledge.
💡 What is RAG ?
Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response.
RAG has 3 major steps:
1️⃣ Retrieve: Find relevant information from your own data using embeddings + vector search.
2️⃣ Augment: Inject the retrieved chunks into the LLM prompt as context. This step ensures the model has the exact information it must use. This step prevents hallucination.
3️⃣ Generate: The LLM produces the final answer using both, The user’s question and the retrieved context
🌟 Why RAG Is a Big Thing Right Now
1. No fine-tuning needed - You can use your custom data instantly
2. Reduces hallucination - AI answers are grounded in facts
3. Super scalable - Works with millions of documents
4. Cheaper than training models - Retrieval minimizes token usage
5. Perfect for enterprise - Safe, controllable, auditable
6. Best way to build AI apps - Chatbots, copilots, search engines, document bots
Follow @navokitech for more posts
#explorepage #foryoupage #trendingreels #fyp #infographics #agenticai #rag #explore
#navoki #softwareengineer #mobileappdevelopment #peoplewhocode #coding #computerscience #100daysofcode #programming #programmerslife💻
#webdeveloper #aicreator #generativeai #artificialintelligence #datascience
@navokitech
1.2M
THHere’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]
@the.datascience.gal
458.9K
PAA new approach to RAG called PageIndex has been getting alot of attention recently, where it uses a tree instead of storing chunks in vector databases #ai #chatgpt #education #viral #tech
@parthknowsai
1.8M
MAI won’t be mad if you copy this entire roadmap…
#dataanalyst #dataanalysis #dataanalytics #data #analyst #techjobs #breakintotech #wfh #workfromhome #wfhjobs #remotejobs #remotework #excel #sql #tableau #python
@marytheanalyst
370.5K
LI1. QUALIFY + ROW_NUMBER()
Lets you rank rows and filter results in the same query — perfect for grabbing the most recent or top record without subqueries.
2. LAG / LEAD
Used to look at the previous or next row — great for comparing changes over time (day-over-day, month-over-month).
3. CTE (WITH clause)
Creates a temporary, named query so you can break complex SQL into clean, readable steps.
#data #analyst #dayinthelife #dadlife #sql
@life.by.elliot
349.3K
SUComment ‘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
@sundaskhalidd
119.0K
VEIf 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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#datascience #data #interview
@vee_daily19
✨ #Dag Data Processing Techniques Discovery Guide
Instagram hosts thousands of posts under #Dag Data Processing Techniques, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.
#Dag Data Processing Techniques is one of the most engaging trends on Instagram right now. With over thousands of posts in this category, creators like @priyal.py, @marytheanalyst and @the.datascience.gal are leading the way with their viral content. Browse these popular videos anonymously on Pictame.
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📹 High-quality vertical videos (9:16) perform best for #Dag Data Processing Techniques - use good lighting and clear audio
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