Learning Python for Data Science Essentials

What is learning Python like in 2026? What's the best path to follow? Egor Howell shares an up-to-date streamlined roadmap for aspiring data professionals.

Discover the top 10 Python machine learning libraries for data science, including scikit-learn, TensorFlow, and Keras, and learn how to choose the best one for your project https://lnkd.in/gShWVMvJ #PythonMachineLearningLibraries Read the full article https://lnkd.in/gShWVMvJ

✅ *Must-Know Python Libraries for Data Science 🐍📊* *1️⃣ NumPy (Numerical Python)* ➤ Used for: Fast numerical computation & handling arrays ✔️ Core Features: - N-dimensional arrays (`ndarray`) - Mathematical functions (mean, std, dot, etc.) - Broadcasting for element-wise operations - Works 10x faster than native Python lists 📌 Foundation for almost every other data science library. *2️⃣ Pandas* ➤ Used for: Data cleaning, manipulation, and analysis ✔️ Core Features: - DataFrame & Series objects - Handling missing data - Merging, grouping, filtering, reshaping - Time series analysis 📌 Ideal for working with CSV, Excel, SQL, or JSON datasets. *3️⃣ Matplotlib* ➤ Used for: Basic data visualization ✔️ Core Features: - Line, bar, pie, scatter, histogram charts - Customizable axes, labels, titles - Save plots as images (PNG, PDF, SVG) 📌 Great for quick visual reports or graphs. *4️⃣ Seaborn* ➤ Used for: Advanced & beautiful visualizations ✔️ Core Features: - Heatmaps, pair plots, violin plots - Works seamlessly with Pandas - Built-in themes & color palettes 📌 Easier and prettier than Matplotlib for many plots. *5️⃣ Scikit-learn* ➤ Used for: Machine learning (ML) ✔️ Core Features: - Algorithms: Linear regression, decision trees, SVM, KNN, etc. - Model training, testing & evaluation - Preprocessing: scaling, encoding, splitting - Pipelines for cleaner code 📌 Beginner-friendly for ML tasks. *6️⃣ SciPy* ➤ Used for: Scientific computing ✔️ Core Features: - Linear algebra, integration, interpolation - Signal/image processing - Statistical distributions & optimization 📌 More advanced math than NumPy. *7️⃣ Statsmodels* ➤ Used for: Statistical analysis ✔️ Core Features: - Linear regression with statistical output - ANOVA, t-tests, ARIMA (time series) - Hypothesis testing 📌 Excellent for academic research and econometrics. *8️⃣ TensorFlow / PyTorch* ➤ Used for: Deep learning & neural networks ✔️ Core Features: - Build and train neural networks - GPU acceleration - Support for image, NLP, and tabular data - TensorBoard (in TensorFlow) for visual training insights 📌 TensorFlow is more production-ready; PyTorch is more flexible and beginner-friendly. *9️⃣ Plotly* ➤ Used for: Interactive visualizations ✔️ Core Features: - Zoomable, clickable charts - Dashboards with dropdowns, sliders - Export to HTML or use in Jupyter 📌 Best for presenting insights to non-technical users. *🔟 Jupyter Notebook* ➤ Used for: Writing, running, and documenting code ✔️ Core Features: - Markdown + Python in same notebook - Visual output (charts, tables, images) - Share notebooks easily (.ipynb) - Widely used in data science interviews and portfolios 📌 Your coding notebook + presentation tool. Data Science Resources: https://lnkd.in/g6Kgerxr Learn Python: https://lnkd.in/gsMtMnp8 💬

Python is the silent backbone of AI 🐍 Everyone talks about AI models. Few talk about what actually runs them. It’s Python. Behind almost every major AI breakthrough: • Data is processed using Python 📊 • Models are trained using Python libraries 🧠 • APIs are built and deployed using Python 🔗 • Automation pipelines run on Python ⚙️ From research labs to startups, Python is everywhere 🌍 Libraries like: • TensorFlow • PyTorch • Scikit-learn • Pandas …have turned complex AI into something developers can actually build with. Why Python? Because it’s: • Simple to learn • Extremely flexible • Backed by a massive ecosystem 🌐 • Built for fast development 🚀 AI didn’t just grow because of ideas 💡 It scaled because of tools 🛠️ And Python became that tool. That’s why I’m not just learning AI. I’m learning to build with Python 💻 Because in an AI-first world, understanding the backbone matters. If you want to start, here are 5 great Python courses 📚 • Python for Everybody – University of Michigan (Coursera) • CS50’s Introduction to Programming with Python – Harvard University • Complete Python Course – CodeWithHarry • Python for Data Science & AI – IBM

Tell your AI that you are beginner when learning Python! I recently asked AI to generate some simple code to reverse two virtual spreadsheet columns called ‘Name’ and ‘Type’. Instead of simply suggesting code in which the column list [‘Name’, ‘Type] is replaced by [‘Type’, ‘Name’], AI suggested a complex indexing trick that looks like [::-1]. Succinct coding, to be sure, but I could not decipher it without additional prompts! In another case, my AI provided some sample code in which a variable was defined after that variable was used! The Horror! When I asked why it made such a fundamental mistake, the AI complimented me on my “good eye” for catching the error and that it did not necessarily provide code in execution order. So, if you are just learning Python, always start your session with a good prompt to set the stage such as the following: "I am a total beginner learning Python. Please follow these rules for ALL Python code you write for me: 1. Write code in execution order. 2. Break every task into small, discrete steps. 3. Use simple, obvious variable names that describe what they contain 4. Add plenty of comments explaining what it happening in plain English 5. Avoid shortcuts, clever one-liners, or condensed syntax that experienced coders use 6. If there are multiple ways to do something, choose the most readable one, not the most efficient one 7. Before showing me code, double-check it runs in the correct order from top to bottom" For more tips, consider my new book "Automate Excel with Python". My publisher @No Starch Press is offering a free review chapter in case you were interested: https://lnkd.in/eS-WAVyV Good luck and good coding! #Excel, #Python, #pandas, #dataframes,#Productivity, #DataAnalysis, #Automation

New article: Sample Paths for Uncertainty Quantification in Time Series Forecasting In this article, we explore the difference between marginal and joint distributions, and how they answer different questions when quantifying uncertainty in time series forecasting. Plus, we get a hands-on experiment with the latest release of #neuralforecast which now supports sample paths across all models. Enjoy the read! #timeseries #forecasting #deeplearning #machinelearning #python #artificialintelligence https://lnkd.in/ekrZPn8S

⭐️ An insightful article on uncertainty quantification in time series forecasting, featuring the latest #neuralforecast 🧠 release. Check it out 👇

Python is the native language of AI. And yet most Python developers are still not using it for AI work. They are writing scripts, automating tasks, building APIs. All good. But the gap between a Python developer and an AI engineer is smaller than most people think. Here is what I mean. If you already know Python, you are one library away from building your first machine learning model. Scikit-learn. Done. You are two libraries away from building a chatbot. LangChain plus an LLM API. Done. You are three steps away from deploying it. Docker, a cloud platform, and a basic CI/CD pipeline. Python has stayed the number one in-demand AI skill for two straight years now. The demand is not slowing down. The developers who will win the next five years are not the ones who know the most. They are the ones who stayed curious and kept building. What was the first AI thing you ever built with Python? Drop it below. #Python #AIEngineering #GenerativeAI #MachineLearning #LangChain #GenAI #PythonDeveloper #ArtificialIntelligence #MLOps #TechCareers

📘 Python for PySpark Series – Day 15 🎭 Polymorphism in Python ✨ What is Polymorphism? Polymorphism means “many forms”. It allows the same method or function to behave differently based on the object. ➡️ Promotes flexibility and dynamic behavior in code 🔹 Why Polymorphism? ✔ Improves code flexibility ✔ Reduces complexity ✔ Enhances readability ✔ Supports method reusability 🔹 Types of Polymorphism ✔ Method Overriding (Runtime) ✔ Method Overloading (Compile-time – limited in Python) ✔ Operator Overloading 🔹 Syntax (Method Overriding) class Parent: def show(self): print("Parent method") class Child(Parent): def show(self): print("Child method") 🔹 Example class Animal: def sound(self): print("Some sound") class Dog(Animal): def sound(self): print("Bark") class Cat(Animal): def sound(self): print("Meow") animals = [Dog(), Cat()] for a in animals: a.sound() ➡️ Same method sound() → different outputs 🔗 Why Polymorphism in PySpark? ✔ Same operations work on different data types (RDD, DataFrame) ✔ Functions behave differently based on input ✔ Helps in writing generic and reusable code 🏫 Real-Life Analogy (Remote Control 📺) One remote → multiple devices ➡️ Same button (action) → different behavior (TV, AC, etc.) 🧠 Interview Key Points ✔ Polymorphism = one interface, multiple implementations ✔ Achieved using method overriding ✔ Supports dynamic method dispatch ✔ Increases flexibility and scalability 🧠 Key Takeaway Polymorphism allows writing flexible and reusable code where the same operation behaves differently for different objects. 🔖 Hashtags #python #pyspark #dataengineering #oop #polymorphism #pythonbasics #learningjourney #coding

Just Published: Mastering Python for Machine Learning: A Practical, No-Nonsense Roadmap If you're someone who feels confused about where to start in Machine Learning, this guide is for you. I’ve broken down the journey into simple, practical steps 💡 No unnecessary theory. No confusion. Just a clear roadmap you can actually follow. Whether you're a beginner or someone restarting your ML journey, this will help you build a strong, real-world foundation. 👉 Read here: https://lnkd.in/gBKzWiUK I’d love to hear your thoughts and feedback! 🙌 #Python #MachineLearning #DataScience #AI #Learning #CareerGrowth