14.9K
DIComputer ka dimaag kaise kaam karta hai? 💻⚡
Ye hai data processing ka real game!”
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#ComputerBasics
#DataProcessing
#HowComputerWorks
#CPU
#digital_explore_v
@digital_explorer_v
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#Data Processing
Schauen Sie sich 125K Reels-Videos über Data Processing von Menschen aus aller Welt an.
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169.1K
JEComment “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!
@jessramosdata
132
VI⚙️ DATA PROCESSING – Turning Raw Data into Meaning
Raw data has no value until it’s processed.
This is where cleaning, transforming, validating, and enriching happen.
Batch jobs or real-time streams—data processing ensures: ✔ Errors are removed
✔ Formats are standardized
✔ Business logic is applied
✔ Data becomes analytics-ready
From Spark jobs to streaming pipelines, this step decides the quality of insights you’ll ever get.
📊 No processing → No intelligence
⚡ Strong processing → Powerful decisions
#DataProcessing
#DataEngineering
#BigData
#ApacheSpark
#DataPipeline
ETL
ELT
@vishwatechtalk
334.1K
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
390.0K
CHThe best projects serve a real use case
Comment “data” for all the links and project descriptions
#tech #data #datascience #ml #explore
@chrisoh.zip
837.7K
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
1.1M
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
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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
288.0K
PRPreprocessing pipeline for llm
#datascience #machinelearning #womeninstem #learningtogether #progresseveryday
@priyal.py
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
155.5K
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
267.9K
EMComment “SQL” and I’ll send it.
If you’re serious about working with data, you can’t avoid SQL.
This guide shows what actually matters, plus hands-on resources to practice it right away.
@emrcodes
114.8K
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
✨ #Data Processing Entdeckungsleitfaden
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