431.9K
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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#Data Science
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EM3 data science projects you can do in a weekend.
If you’re learning data science, one of the best ways to improve is by working through real examples.
Here are three Kaggle notebooks you can explore:
• Loan Prediction – predicting whether a loan gets approved based on applicant data.
• Bank Churn – analyzing which customers are likely to leave a bank.
• House Price Prediction – estimating house prices from property features.
You can study the notebooks and also try solving the same problems yourself using the same datasets.
It’s a great way to practice and see different ways people approach the same problem.
Comment “DATA” and I’ll send you the notebooks.
#coding #datascience #university
@emrcodes
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
336.6K
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
39.1K
WO📍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
@woman.engineer
78.9K
THData visualisation book recommendation for anyone who wants to turn data into interactive stories, not just static charts 📊🌍💻
✨ Teaches you how to move from spreadsheets to web-based visualisations
✨ Covers tools like Google Sheets, Datawrapper, Tableau Public, Chart.js & Leaflet
✨ Perfect if you want to communicate data clearly — even without heavy coding
✨Open-source so freely available online
📌 Hands-On Data Visualization: Interactive Storytelling from Spreadsheets to Code — Jack Dougherty & Ilya Ilyankou
💭 Summary: This book shows you how to clean, analyse, and visualise data using practical tools — starting with spreadsheets and moving into customisable web-based charts and maps. It’s especially useful if you want to share your work online and make your data interactive, not just informative.
If you’re learning data science, bioinformatics, or just want to present your work better, this is a great place to start 🤍
📌 Save this for later — I’ll be sharing more recommendations soon.
#womeninstem #datavisualization #datascience #bioinformatics #tech
@thephdstudent
11.5K
VOComment "DATA" for the links.
You Will Never Struggle With Data Science Again
📌 Learn the most important foundations with these beginner-friendly resources:
1️⃣ Learn Python for Data Science – FreeCodeCamp’s full beginner course
2️⃣ Essence of Linear Algebra – 3Blue1Brown’s visual, intuitive playlist
3️⃣ Statistics – A Full Lecture (2025) – step-by-step breakdown of core stats concepts
Stop feeling overwhelmed by Python, statistics, or linear algebra. These tutorials simplify the fundamentals of Data Science with clear explanations, visuals, and real-world examples.
Whether you’re preparing for a career in Data Science, getting into machine learning, or just curious about data analysis, this is the fastest way to finally understand how it all fits together.
Save this post, share it, and turn confusion into clarity with Python, Stats, and Linear Algebra for Data Science 📊
@volkan.js
190.5K
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
1.5M
VOComment "Link" to get the links!
You Will Never Struggle With Data Structures & Algorithms Again
🔗 Explore these free visualization tools:
1️⃣ visualgo.net
2️⃣ cs.usfca.edu
3️⃣ csvistool.com
Stop memorizing code blindly. See every algorithm in action — arrays, linked lists, stacks, queues, trees, graphs, sorting, searching, and more. These interactive platforms show step-by-step exactly how data flows and how operations work.
Whether you’re preparing for coding interviews, studying computer science, or just starting with DSA, this is the fastest way to master the fundamentals.
Save this, share it, and turn complex algorithms into simple visuals you’ll never forget.
@volkan.js
425.5K
BRData Science 4 All is a free training program that makes data science shockingly uncomplicated
@bri_xu
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
-------
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
54.0K
PRDay 1 - Inferential and Descriptive Statistics
Hope this helpful, let me know if you want more such videos.
Follow for more
#datascience #machinelearning #womeninstem #learnintogether #progresseveryday #tech #consistency #statistics
@priyal.py
✨ #Data Science Discovery Guide
Instagram hosts 14 million posts under #Data Science, creating one of the platform's most vibrant visual ecosystems. This massive collection represents trending moments, creative expressions, and global conversations happening right now.
#Data Science is one of the most engaging trends on Instagram right now. With over 14 million posts in this category, creators like @priyal.py, @volkan.js and @the.datascience.gal are leading the way with their viral content. Browse these popular videos anonymously on Pictame.
What's trending in #Data Science? The most watched Reels videos and viral content are featured above. Explore the gallery to discover creative storytelling, popular moments, and content that's capturing millions of views worldwide.
Popular Categories
📹 Video Trends: Discover the latest Reels and viral videos
📈 Hashtag Strategy: Explore trending hashtag options for your content
🌟 Featured Creators: @priyal.py, @volkan.js, @the.datascience.gal and others leading the community
FAQs About #Data Science
With Pictame, you can browse all #Data Science reels and videos without logging into Instagram. No account required and your activity remains private.
Content Performance Insights
Analysis of 12 reels
💡 Top performing posts average 1.4M views (2.5x above average). High competition - quality and timing are critical.
Focus on peak engagement hours (typically 11 AM-1 PM, 7-9 PM) and trending formats
Content Creation Tips & Strategy
💡 Top performing content gets over 10K views - focus on engaging first 3 seconds
✨ Many verified creators are active (75%) - study their content style for inspiration
📹 High-quality vertical videos (9:16) perform best for #Data Science - use good lighting and clear audio
✍️ Detailed captions with story work well - average caption length is 919 characters
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