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SWIf I was a beginner learning to code, I would use this Python roadmap step by step for beginners 💪 #coding #codingforbeginners #learntocode #codingtips #cs #python #computerscience #usemassive
@swerikcodes
#Python Data Science
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SHData Analytics interviews does not require complex Python Programming knowledge. I have created a PDF which contains required Python syllabus and free resources to learn.
Please comment “Python” to get the PDF directly to your DM !!
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_P🐍Learning Python with AI
🔸️In this class, we're training students to learn Python faster with AI collaboration!
🔸️Here, Aidan uses ChatGPT to recreate a version of the classic arcade game Asteroids.
🔸️This is Aidan's 12th day of Python programming.
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HAInput and typecasting in python
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Offline batches are starting very soon in Jaipur!
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PYComment "Projects" & I will DM You all the projects!
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1️⃣ Medical Data Extraction
2️⃣ Sports Celebrity Image Classification
3️⃣ T20 World Cup Cricket Analytics
4️⃣ Grocery Store Management System
5️⃣ Sales Insights for a Consumer Goods Brand
Like this reel, comment “Projects”, and share it with your friends!
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https://drive.google.com/file/d/1a3fxX9EFHp8tjBIq75eRgKvbEfpNYSjF/view?usp=sharing
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MAComment PROJECT to access my step-by-step Python tutorial that anyone can follow to build your very first geospatial dashboard web app! 🌍📊
A good number of portfolio projects is 3–5, and the types of projects you choose should reflect the kind of data role you’re going after.
A data analyst portfolio should look very different from a machine learning engineer one. Even within data science, a product/decision data scientist portfolio should focus on A/B testing and metrics storytelling—while an algorithm data scientist portfolio might highlight modeling and experimentation.
✨ Especially if you’re building your very first project, prioritize:
🌱 Real-world messiness (not polished Kaggle sets)
🌱 Business context and decision-making
🌱 Clear documentation (what you did and why)
🌱Visuals to help your work stand out
No one’s asking for perfection—they want to see how you think.
#datascienceportfolio #dataanalyst #learnpython #codingjourney #techcareers
@maggieindata
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CO🐍 Top 15 Python Libraries Every Data Analyst Must Know 📊
If you are starting your Data Analytics journey, the right Python libraries can save you hours of effort and make your projects 10x more powerful. 🚀
Here’s a quick breakdown of the must-know libraries:
✅ Pandas → Data cleaning & manipulation
✅ NumPy → Fast numerical computing
✅ Matplotlib & Seaborn → Stunning visualizations
✅ Plotly → Interactive dashboards
✅ Scikit-learn → Easy machine learning
✅ Statsmodels & SciPy → Statistical analysis
✅ TensorFlow / PyTorch → Advanced AI & analytics
✅ OpenPyXL, Dask, BeautifulSoup, NLTK, SQLAlchemy → Excel automation, big data, web scraping, text analytics, and databases!
💡 Whether you’re preparing for a job, building projects, or just learning, these libraries are the backbone of Data Analytics.
👉 Save this reel for quick reference 🔖
👉 Share it with your data friends 🔄
👉 Follow @codeandcrush for more daily Data Analytics tips, tricks & career hacks 🚀
#python #dataanalytics #pythonlibraries #datascience #machinelearning #sql #powerbi #dataanalyst #learnpython #learnandgrow #careergoals #instagram #pythonprogramming #reelsi̇nstagram #trendings
@codeandcrush
✨ #Python Data Science Entdeckungsleitfaden
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