Vidaan Shankar’s Post

Week 3 check-in: This week, I completed several hands-on projects: ✅ I created a simple Python program to randomly recommend a song on Spotify. While not highly practical, it helped me apply various data wrangling methods and refresh my Python programming skills. ✅ I worked on data wrangling with a Netflix dataset. This project took several days as I learned how to slice data and create simple charts. It was both challenging and fun to learn data manipulation with Pandas. ✅ Even though having drawed countless charts using MS Office, I found Matplotlib more complex to use. At one point, I spent over an hour creating a stacked bar chart with simple data. The key challenge was understanding each component deeply and customizing everything. But it is, at the same time, giving more flexibility and granular approach to users on analyzing and visualizing. ✅ I also completed a set of LeetCode problems and learned new functions in SQL. Despite being familiar with SQL, delving into the interaction between the language and databases was still somewhat challenging. I created a GitHub repository to save these learning projects. You can find it here: #week3 #LeaningDataAnalysis — This is my logbook on self-studying data analysis, it contains summaries, some kind of documentation and some Python/SQL stuff. If you happen to read these, please note my purpose is not knowledge sharing, I am just bored so I write about whatever I learned to keep myself committed with studying. 👩💻 👩💻👩💻

🎨 Interactive Visualizations in Python: Bokeh is a powerful library that transforms complex data into compelling, interactive visualizations directly in your browser. Perfect for modern web needs! 🚀 High-Performance: It handles large or streaming datasets with ease, ensuring your data applications run smoothly and efficiently. 🛠️ User-Friendly: With Bokeh, creating dashboards and interactive plots is a breeze, even if you’re not a seasoned data scientist. 🌐 Community & Support: A vibrant community and extensive documentation mean you’re never alone in your development journey. 🔗 For more details and to dive into the world of interactive data visualization with Bokeh, check out their GitHub page: https://lnkd.in/eQFk-Y-c 🎁 Bonus addon! Already using Bokeh in your ML pipelines, but finding hard to share visualizations with team-mates? neptune.ai has got your back! Neptune integrates natively with Bokeh, letting you upload your awesome visualizations to a central dashboard with ease ✅ Check how you can use Bokeh with Neptune here: https://lnkd.in/evHCJPbT #DataVisualization #Bokeh #InteractivePlots #Python #OpenSource #MachineLearning #MLOps #neptuneai 📊🐍🌟

I came into IT industry using Python! .. .. As we dive deeper into the data-driven era, Python emerges as a star player in the realm of data analytics and data science. Here’s why: ✨ Simplicity in Syntax: Easy to learn, easier to use! Python’s straightforward syntax means you spend less time deciphering code and more time deriving insights. 🛠️ Rich Libraries: With tools like Pandas 🐼, NumPy 🔢, and Scikit-learn 🤖, Python equips data professionals with everything needed to turn data into decisions. 🌐 Community Support: Got a question? The global Python community is here to help, offering a treasure trove of resources and support. 🔗 Versatility and Integration: Python plays well with others, making it a flexible ally in any data project. 🎥 Real-World Impact: Ever wondered how Netflix 🍿 recommends shows you love? Yep, Python's data analytics magic at work! Now, let's hear from you! 🎤 How has Python empowered your data projects? Share your experiences or any cool projects you’ve worked on using Python! #Python #DataScience #DataAnalytics #TechCommunity #PythonProgramming

🚀 Why Your Business Needs Python 🚀 Python is a game-changer for businesses. Here’s why: 1. Versatility: Ideal for web development, data science, automation, and machine learning. 2. Efficiency: Simple syntax speeds up development, reducing time-to-market. 3. Scalability: Robust frameworks for building scalable applications. Embrace Python to drive innovation and growth. Let’s connect if you’re ready to harness its power! #Python #TechInnovation #WebDevelopment #DataScience #BusinessGrowth Feel free to adjust to better fit your style!

Navigating a language shift from #Python to #Rust as a #DataScientist? 🐍➡️🦀 It's all about mastering virtual environments! Think of Rust's Cargo 📦 as Python's venv, but with a twist. Ready to embrace the change and supercharge your data performance with Rust's safety and speed? #TechTransition #DataScience #ProgrammingLanguages #VirtualEnvironments #CareerGrowth.

Day 14 of #90daysofdevops : Unraveling Python's Data Types & Structures! 🐍💻 Delved deep into Python's data types and how they bolster our DevOps tasks. From mutable lists to immutable tuples and handy dictionaries, I'm all set to script my way to automation efficiency. Check out my latest blog post where I break it all down with examples and personal insights. #PythonForDevOps #DataTypes #DevOpsEngineer #PythonStructures #Automation #Day14 #CodingLife #DevOpsDiary 🚀🔧 #90daysofdevopschallenge #90dayschallenge #developercommunity

❗Attention Python enthusiasts and data pipeline architects! 🛠️ Want to continuously refine your LLM / RAG? Simplify the process with QuixStreams. We're empowering you to refine your LLM and RAG models with the finesse they need, minus the complexity 👇 Here's the info: 👉 Feature Pipeline: Ingests and transforms raw data into a refined state, ready for the big leagues of machine learning. 👉 Training Pipeline: Takes those polished features and uses them to enhance our models' intelligence. 👉 Inference Pipeline: The grand finale where all the training manifests into swift, accurate predictions. Setting it up is as simple as: `pip install quixstreams` Check the image for an idea of the code 👇 Why choose QuixStreams? It's Python at its purest, with stateful operations and scalability (Kubernetes). Plus, it's a natural fit within the Python ecosystem. Are you eager to elevate your data pipelines, or stuck in the batch world and don't know where to start with real-time data? Why not try QuixStreams? If you like what you see, consider giving us a star on GitHub. 🌟 For a deeper conversation or to get started, check out our documentation (https://lnkd.in/ed4CZdbX) or connect with us on Slack - https://lnkd.in/du5B8SpH #Python #DataStreaming #QuixStreams #MachineLearning #Kubernetes #DevOps

Today I earned my "Using GitHub Copilot with Python" badge! I’m so proud to be celebrating this achievement and hope this inspires you to start your own @MicrosoftLearn journey! Thanks to Ulster University #Copilot #GitHub #AI #MachineLearning #DeepLearning #DataAnalytic #DataScience