Python Beyond Basics: Building Indispensable Skills

If you're building AI apps with Python, this one's worth your time. MongoDB is running a free 5-part masterclass series covering everything from the document model basics through to vector search, RAG, and autonomous agents using PyMongo, LangChain, and LangGraph. Parts 1–4 are available on-demand right now. Part 5 (Agents with LangGraph) is live on April 1st. Complete the series and you'll earn an official MongoDB Skill Badge too. https://lnkd.in/g6ZT5V_q? #AI #Python #MongoDB #RAG #VectorSearch #Agents #LLM

Your Python skills don’t suck. You just need a structured, learning roadmap. If you want to be a Data Scientist, you MUST know Python. This is the #1 skill required for Data Scientists. 86% of Data Science jobs require Python. ——— 𝗠𝘆 𝘀𝘁𝗼𝗿𝘆: I got a Data Science job at Meta after learning Python. No expensive bootcamp. No random tutorial videos. I simply used a combination of 3 things: #1  This tiered learning roadmap #2  DataCamp for learning: ↳ Python fundamentals: https://lnkd.in/eDMeCrq8 ↳ Python for Data Science: https://lnkd.in/e3AMtb2n #3 Jupyter Notebooks to build projects ↳ Start with guided projects: https://lnkd.in/eM7zNNvv ↳ Advance to self-projects: https://lnkd.in/gdRh-Gzq ——— Here’s how to go from D-tier to S-tier in Python: 𝗗 𝘁𝗶𝗲𝗿: 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 → Variables and data types → Control structures → Functions & list comprehensions 𝗖 𝘁𝗶𝗲𝗿: 𝗣𝗮𝗻𝗱𝗮𝘀 → Data cleaning → Merging & reshaping data → Grouping & aggregation 𝗕 𝘁𝗶𝗲𝗿: 𝗗𝗮𝘁𝗮 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 → Basic plotting → Advanced plots → Customizing plots 𝗔 𝘁𝗶𝗲𝗿: 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗼𝗿𝘆 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 → Descriptive statistics → Correlation analysis → Outlier & anomaly detection 𝗦 𝘁𝗶𝗲𝗿: 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 → Model training & evaluation → Regression → Classification & clustering ——— ♻️ Found this useful? Repost it so others can see it too.

This is great! I mainly utilize Tiers F-C in my workplace(nothing wrong with some AI help). I am eager to explore use cases for the remaining tiers. 🐍

Day 12 of My Data Science Journey — Python Lists: Methods, Comprehension & Shallow vs Deep Copy Today’s focus was on one of the most essential data structures in Python — Lists. From data storage to manipulation, lists are used everywhere in real-world applications and data science workflows. 𝐖𝐡𝐚𝐭 𝐈 𝐋𝐞𝐚𝐫𝐧𝐞𝐝: List Properties – Ordered, mutable, allows duplicates, and supports mixed data types Accessing Elements – Used indexing, negative indexing, slicing, and stride for flexible data access List Methods – append(), extend(), insert() for adding elements – remove(), pop() for deletion – sort(), reverse() for ordering – count(), index() for searching and analysis Shallow vs Deep Copy – Understood that direct assignment does not create a new copy – Used copy(), list(), slicing for safe duplication – Learned the importance of copying, especially with nested data List Comprehension – Wrote concise and efficient code using list comprehension – Combined loops and conditions in a single readable line Built-in Functions – Used sum(), len(), min(), max() for quick data insights Additional Useful Methods – clear(), sorted(), zip(), filter(), map(), any(), all() 𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Understanding how lists work — especially copying and comprehension — is critical for writing efficient and bug-free Python code. Lists are not just a data structure; they are a core tool for solving real-world problems. Read the full breakdown with examples on Medium 👇 https://lnkd.in/gFp-nHzd #DataScienceJourney #Python #Lists #Programming

Learning Python for Data Analytics 📊 Recently explored the difference between NumPy and Pandas, two powerful libraries used in data analysis. 🔹 NumPy – efficient numerical computations using arrays 🔹 Pandas – powerful tools for working with structured/tabular data Understanding how these tools work together is an important step in my data analytics learning journey. #Python #NumPy #Pandas #DataAnalytics #LearningJourney #entri #josephdelmon https://lnkd.in/dz-aG9yq

Most people learn Python for data and immediately jump into complex machine learning models and fancy algorithms. But the real magic? It happens in the basics. The analysts and engineers who move the fastest are not the ones who know the most libraries. They are the ones who deeply understand a few simple tools and use them really, really well. Here's what actually matters when using Python for data work. Readability beats cleverness. Code you wrote 6 months ago should make sense to you today. If it doesn't, it's too clever. Simple, clean logic wins every time. Automate the boring stuff first. The biggest wins I've seen aren't from fancy models they're from automating repetitive data cleaning and reporting tasks that were eating up hours every week. Pandas is not just a library, it's a mindset. Once you truly understand how to think in dataframes, the way you approach every data problem completely changes. Your biggest skill is not syntax, it's knowing WHAT to ask. Python just executes your thinking. The better your questions, the better your analysis. Consistency beats intensity. 30 minutes of Python every day beats a weekend marathon once a month. Always. #Python #DataAnalytics #DataEngineering #PythonForData #DataScience #LearningEveryDay #GrowthMindset #DataCommunity #Pandas #Numpy #MachineLearning #DataAnalytics

Pandas Data Exploration Explained | head(), tail(), info(), describe() | Python Data Analysis EP 16 Explore Any Dataset in Seconds | Pandas head(), tail(), info(), describe() Tutorial | EP 16 In Episode 16 of the Python for Data Analysis series, we explore how to understand the structure of a dataset using essential Pandas data exploration functions. Before performing any serious analysis, it is important to first explore the dataset to understand its structure, identify missing values, and check data types. In this tutorial, you will learn how to use four powerful Pandas functions that every data analyst should know: head(), tail(), info(), and describe(). These functions help analysts quickly inspect datasets, verify data quality, and gain statistical insights before moving to deeper analysis or machine learning models. In this video you will learn: • How to preview the first rows of a dataset using head() • How to inspect the last rows using tail() • How to check data types and missing values using info() • How to generate statistical summaries with describe() • How to explore datasets efficiently before analysis This lesson is perfect for beginners in Python, data analysis, and data science who want to learn practical Pandas techniques used by professional analysts. Episode: 16 Topics Covered: Python Pandas Data Exploration Dataset Structure Data Analysis Basics If you are learning Python for Data Analysis, this series will help you build strong foundations step by step. Subscribe for more tutorials on Python, Pandas, NumPy, Data Visualization, and Machine Learning. 👍 If this video helps you, Like, Share and Subscribe for more data science tutorials. #Python #Pandas #DataAnalysis #DataScience #PythonTutorial #MachineLearning #DataAnalytics #LearnPython #Programming #AI

Just wrapped up a 12-minute presentation on how to smoothly use the Runcell AI assistant into JupyterLab! Streamlining Python and machine learning workflows is a huge part of my day-to-day , and having an AI assistant right inside the notebook environment is an absolute game-changer for data science projects. However, during setup, many Windows users hit a common roadblock: the dreaded 'jupyter' is not recognized error. This usually happens when Python is installed via the Microsoft Store, which hides the executable scripts in a folder that Windows doesn't automatically check. If you are looking to try Runcell, here is the code to install it, along with the permanent fix for that pesky Windows 11 PATH issue. 🛠️ [A} Install JupyterLab & Runcell Run these commands in your terminal: Bash or Command prompt python --version pip install --upgrade jupyterlab pip install runcell jupyter labextension enable runcell jupyter server extension enable runcell ⚙️ [B] The Permanent PATH Fix (Windows 11) If you get the yellow warning that your scripts are not on PATH, follow these steps: 1. Copy the path from the yellow warning in your command prompt. (Auxiliary Path: If you can't find the warning, the Microsoft Store path usually looks like this: C:\Users<YourUsername>\AppData\Local\Packages\PythonSoftwareFoundation...\LocalCache\local-packages\Python311\Scripts. For standard Python installers, it is usually C:\Users\<YourUsername>\AppData\Local\Programs\Python\Python311\Scripts) 2. Press your Windows Key, type Environment Variables, and hit Enter. 3. Click the Environment Variables... button at the bottom right of the System Properties window. 4. Under the top section ("User variables"), find the variable named Path, click to select it, and then click Edit.... 5. Click New and paste your copied folder path. 6. Click OK on all three windows to save and close them. 7. Close your Command Prompt and open a fresh one. Type jupyter lab and you are successfully up and running! Has anyone else experimented with Runcell or other Jupyter AI extensions yet? I would love to hear how you are fitting them into your workflow! #DataScience #Python #JupyterLab #MachineLearning #Runcell #AI #Productivity #TechTips

As a Full-stack Developer, I always thought if i made any library it would be published on npm. Funny enough, my first open source library ended up being published on PyPi 😂 😎 I am excited to share my first open source python library : **skwrapper**! While learning ML, I noticed that when we train the datasets with scikit-learn, we do not rely on just one algorithm. we usually try with multiple algorithms to see which performs best for dataset. and in that we often repeat the same steps again and again: - Import different algorithm - Train each model separately - Import and calculate metrics repeatedly. writing very similar code multiple times, which is realy time-consuming. To simplify this workflow, i build skwrapper With skwrapper you can: 1. Import the skwrapper library following its two main class (sc, sr). 2. Define short algorithm names for regression and classification 3. Run multiple models in one line of code and get evaluation metrics quickly ⚡️ Behind the scenes it still use scikit-learn, so it keeps the same reliability and performance The motive is to help developer & data scientist quickly experiment with multiple models without worrying about repetitive setup code This is my first open source project, and i would really appreciate feedback, suggestions, or contributions from community. You can find the README and Contribution guidelines in the link below for detailed usage and contribution instructions. https://lnkd.in/gF4cTNeW Install the library with pip install skwrapper **If you are experimenting with ML, feel free to give a try!** #ai, #wrapper, #scikitlearn, #ML, #newlibrary, #opensource, #skwrapper