Accelerate Python Learning with Real-World Projects

🐍 Getting started with Jupyter Notebook As part of my Python learning for Data Science, I got introduced to Jupyter Notebook. What I understood: • It allows you to write and run code step by step • You can see outputs immediately, which makes learning easier • It’s widely used for data analysis, experiments, and visualization Setting up the environment properly gave me more confidence to start coding and practicing. Small steps, but moving forward 🚀 #PythonForDataScience #LearningInPublic #BeginnerProgrammer #JupyterNotebook

Let's talk about something practical that saved me hours. Ever spent ages writing complex data transformations? Meet Pandas 🐼→ Polars 🚀. The transition wasn't smooth at first—new syntax, different mindset. But the performance gain? 5-10x faster on large datasets. My realization: Sometimes we stick with tools because they're comfortable. But that 2-day learning curve for Polars gave me back weeks of compute time over the year. Try this: Take one of your slower pandas scripts this week. Rewrite it in Polars. Share your speedup in the comments! What's one tool switch that significantly improved your workflow? #Python #DataScience #Programming #TechTips #CareerDevelopment #DataEngineering

🚀 New Video Alert — Pandas Intermediate Series Continues! In this video, we dive deeper into boolean logic in Pandas — one of the most powerful tools for filtering and analyzing data. You’ll learn: ✅ How to use and & or conditions ✅ How to filter with .between() ✅ How to filter using .isin() ✅ Why filtering correctly matters in real analytics If you’re learning Python and Pandas and want to level up your data analytics skills, this video is for you. 📺 Watch here: 👉 https://lnkd.in/g6t2BbmR Stay tuned for more Pandas Intermediate videos as we continue building real skills, step by step. #pyaihub #pandas #python #dataanalytics #datascience #programming #learntocode

Why Data Structures Matter More Than You Think 🚀 Data Structures are not just a topic you study to pass exams — they are the backbone of efficient software. Choosing the right data structure can: Improve performance ⚡ Reduce memory usage 💾 Make your code cleaner and easier to maintain 🧠 For example, imagine you want to store a list of tasks and frequently add new tasks and remove completed ones. Using a Stack makes perfect sense because it follows LIFO (Last In, First Out) 📌 Master the fundamentals — they scale with you #DataStructures #ComputerScience #SoftwareEngineering #Programming #LearningJourney Here's a Simple Example (Python):

🐍 𝐃𝐚𝐲 𝟕 (𝐌𝐨𝐫𝐧𝐢𝐧𝐠) 𝐨𝐟 𝐌𝐲 𝟏𝟓-𝐃𝐚𝐲 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 — 𝐌𝐨𝐝𝐮𝐥𝐞𝐬 & 𝐏𝐚𝐜𝐤𝐚𝐠𝐞𝐬 Today’s morning session was about modules and packages — how Python helps organize code and reuse functionality efficiently. As projects grow, structuring code properly becomes essential. 🔹 𝐖𝐡𝐚𝐭 𝐈 𝐂𝐨𝐯𝐞𝐫𝐞𝐝 𝐓𝐨𝐝𝐚𝐲 ✅ What Is a Module? A module is a Python file containing functions, classes, or variables. import math print(math.sqrt(16)) ✅ Importing Specific Functions from math import sqrt, pi print(sqrt(25)) print(pi) ✅ Creating Your Own Module # my_module.py def greet(name): return f"Hello, {name}" import my_module print(my_module.greet("Ankush")) ✅ What Is a Package? A package is a collection of modules organized in folders. Example structure: project/ └── utils/ ├── __init__.py └── helper.py 🎯 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 Modules and packages help keep code organized, reusable, and scalable. Good structure today saves debugging time tomorrow. 🌆 𝐄𝐯𝐞𝐧𝐢𝐧𝐠 𝐒𝐞𝐬𝐬𝐢𝐨𝐧 (𝐃𝐚𝐲 𝟕): 𝐄𝐫𝐫𝐨𝐫 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 (𝐭𝐫𝐲, 𝐞𝐱𝐜𝐞𝐩𝐭) Let’s keep learning #Python #Modules #Packages #15DaysOfPython #LearningInPublic #Programming

💡 LeetCode Learning Journey: Contains Duplicate Problem Recently, I worked on the “Contains Duplicate” problem on LeetCode. Initially, I approached it with the classic nested loop method, which checks every element against all others. While this solution works logically, I realized it fails to pass large test cases due to O(n²) time complexity. After researching, I discovered a much cleaner and highly optimized solution using Python’s set(), which ensures O(n) time complexity by storing already seen elements and checking for duplicates instantly: return len(nums) != len(set(nums)) I was truly impressed by the simplicity and efficiency of this approach! 🚀 ✅ Key Learning Points: Using set in Python for uniqueness and fast membership checks How time complexity impacts large inputs in competitive programming Importance of optimizing code beyond just correctness I’ve included a screenshot of my longer initial solution as well as the optimized one-line solution for comparison. It’s amazing how a single line can outperform nested loops for large datasets! #Python #LeetCode #ProblemSolving #DataStructures #CodingJourney #OOP #Set #Programming #Optimization

📊 Data Analytics with Python – Day 4 (PDF) Day 4 was all about understanding the Levels of Data Measurement, a key foundation for data analysis. This PDF covers how different data types impact the choice of analysis methods and visualizations, explained with clear examples for better understanding. Building concepts step by step 📈 More learning ahead 🚀 #DataAnalysis #PythonLearning #Upskilling #ContinuousLearning #CareerInData #Day4

🚀 New Video Dropped — Pandas Intermediate Series Continues! In today’s video, we continue building real analytical skills with Pandas Intermediate. We focus on how to aggregate and compare data using department salary information — exactly how analysts work with data in real projects. 👉 Watch now: https://lnkd.in/gNe7fXC2 📂 Code available here: https://lnkd.in/gaQfTadf Whether you’re preparing for a data analytics role or want to level up your Python skills, this video gives you insightful, practical knowledge you can use immediately. 💬 Let me know what part you want next! #pyaihub #pandas #python #dataanalytics #datascience #analytics #programming

Visualizing Data Structures: The "Cat" Method Mastering Python list manipulation is fundamental for any developer or data scientist. While documentation is necessary, visual mental models are often faster for retention. Here is a clean, visual breakdown of the most common Python List Methods (append, pop, insert, etc.) represented by—you guessed it—cats. 🐱 This is a perfect cheat sheet for beginners cementing the logic or seniors needing a quick refresher on mutability without digging into the docs. Which method do you find yourself using the most in production? #Python #DataScience #SoftwareEngineering #Coding #Programming

Today, I taught three different classes..... and they reminded me why fundamentals matter so much in data. VLOOKUP in Excel : helping students understand how Excel thinks when searching for data. Tuples in Python : introducing immutable data structures and why they matter. Scenario Analysis in Excel : teaching how to ask better “what-if” questions before making decisions. Different tools, Different levels., Same principle: clarity before complexity. When learners truly understand the basics, switching between Excel and Python becomes much easier and confidence grows naturally. Teaching continues to be one of the best ways to deepen understanding. #DataAnalytics #ExcelTips #PythonForBeginners #LearningInPublic #WomenInData