Restarting My Data Science Journey with Python

👋 Welcome back! 📅 Python Learning – Day 63 Today we move to a faster and more efficient sorting method: Quick Sort. Quick sort works by selecting a pivot element and dividing the list into two parts — smaller values on one side and larger on the other. This divide-and-conquer approach makes it much faster than basic sorting methods. 📘 In this lesson, I’ve explained: ⚡ How quick sort works using partitioning 🔀 How recursion helps sort the divided parts ⚠️ Common beginner mistakes when choosing pivot and handling recursion Quick sort is widely used because of its speed and efficiency. Once you understand how it breaks problems into smaller parts, your approach to problem-solving improves. 🔗 Tutorial link is in the comments. 💬 If you're following this learning journey and want to stay connected with more tutorials and discussions, you can join our LinkedIn community here: 👉👉 CodePractice Group: (https://lnkd.in/g3xbN4GJ) ⏭️ Tomorrow: Python Counting Sort #QuickSort #DivideAndConquer #LearnPythonDSA #SortingAlgorithms #CodingPractice #PythonProgramming #TechStudents #DeveloperSkills #python #learnpython #codepractice #softwaredevelopment #computerscience #pythondevelopment #pythonlearning

When I first started using Pandas, I wrote code the same way I wrote normal Python. Lots of loops. Lots of step-by-step logic. And it worked… at first. But then datasets got bigger. And things slowed down quickly. That’s when I learned something important: 👉 Pandas works best when you think in vectorized operations. Instead of: looping through rows You start thinking in columns. Example mindset shift: Instead of processing each row individually, you transform entire columns at once. This small change made my code: ✔ faster ✔ simpler ✔ easier to read Still learning, but it's one of those small mental shifts that really changes how you work with data. #DataEngineering #Python #Pandas

👋 Welcome back! 📅 Python Learning – Day 60 Today we look at one of the simplest sorting techniques: Bubble Sort. Bubble sort works by repeatedly comparing adjacent elements and swapping them if they are in the wrong order. With each pass, the largest element “bubbles up” to its correct position. 📘 In this lesson, I’ve explained: 🔁 How bubble sort works step by step 🔄 How swapping helps arrange elements ⚠️ Common beginner mistakes like unnecessary iterations Bubble sort is not the fastest algorithm, but it is very useful for understanding how sorting actually works. Once this concept is clear, learning advanced sorting becomes easier. 🔗 Tutorial link is in the comments. 💬 If you're following this learning journey and want to stay connected with more tutorials and discussions, you can join our LinkedIn community here: 👉👉 CodePractice Group: (https://lnkd.in/g3xbN4GJ) ⏭️ Tomorrow: Python Selection Sort #BubbleSort #SortingAlgorithms #LearnPythonDSA #CodingPractice #AlgorithmBasics #PythonForStudents #TechLearning #DeveloperJourney #python #learnpython #codepractice #softwaredevelopment #computerscience #pythondevelopment #pythonlearning

👋 Welcome back! 📅 Python Learning – Day 64 Today we explore a different kind of sorting technique: Counting Sort. Unlike comparison-based algorithms, counting sort works by counting how many times each value appears and then placing them in order. This makes it very fast for specific types of data. 📘 In this lesson, I’ve explained: 🔢 How counting sort works step by step ⚡ Why it can be faster than other sorting algorithms ⚠️ Common beginner mistakes when handling ranges and counts Counting sort is powerful, but it works best when the range of values is limited. Understanding when to use it is just as important as knowing how it works. 🔗 Tutorial link is in the comments. 💬 If you're following this learning journey and want to stay connected with more tutorials and discussions, you can join our LinkedIn community here: 👉👉CodePractice Group: (https://lnkd.in/g3xbN4GJ) ⏭️ Tomorrow: Python Radix Sort #CountingSort #NonComparisonSort #LearnPythonDSA #AlgorithmLearning #CodingPractice #PythonForStudents #TechLearning #DeveloperGrowth #python #learnpython #codepractice #softwaredevelopment #computerscience #pythondevelopment #pythonlearning

🚀 Learning Something New Every Day! Today I learned an important Python concept — *args and **kwargs. 🔹 *args allows a function to take multiple positional arguments (stored as a tuple) 🔹 **kwargs allows a function to take multiple keyword arguments (stored as a dictionary) 💡 This makes functions more flexible and reusable in real-world scenarios. Here’s a simple example: def demo(*args, **kwargs): print(args) print(kwargs) Step by step, I’m strengthening my Python fundamentals and building a strong base for data analytics. #Python #LearningJourney #Coding #DataAnalytics

Most people learn Python by staring at output. I tried something different. 👇 This is what actually happens when Python executes a basic function — step by step, visually. No theory. No slides. Just execution in real time. Still early in my journey — but this is how I'm learning. If you're learning Python too, drop a 👋 below. 🔧 Tool: pythontutor / staying.fun 🐍 Concept: Functions — how they're called, executed & returned #Python #LearningInPublic #DataAnalytics #BBA #100DaysOfCode #PythonBeginners

Let’s get back to the Python series Today, let’s talk about something powerful in NumPy that beginners often ignore 💡 Vectorization in NumPy Instead of using loops in Python, NumPy allows you to perform operations on entire arrays at once. 🔹 Traditional Python (loop-based) = slower 🔹 NumPy (vectorized operations) = faster + cleaner Example: Instead of writing loops to add two lists, NumPy does it in one line. 🔷 Why this matters? Because in real-world data analysis, performance and efficiency are everything. This is one of the reasons why NumPy is widely used in data science and machine learning. My learning: Writing less code but getting faster results is a game changer. #Python #NumPy #DataAnalytics #MachineLearning #LearningInPublic #CodingJourney

My Data Science journey One thing I’m focusing on now: consistency over intensity. You don’t need 10 hours a day to improve — you need 1–2 hours done regularly. Today’s focus: • Revisiting core statistics • Practicing Python basics • Solving small problems daily Small steps, every day. #DataScience #Consistency #Python #LearningJourney

Today I focused on strengthening my Python fundamentals, and here’s what I learned: 🔹 Tuple Ordered & immutable collection Allows duplicate values Useful for fixed data 🔹 Set Unordered collection of unique elements Automatically removes duplicates Helpful in data cleaning 🔹 Set Operations Union → Combine all elements Intersection → Find common elements Difference → Unique elements from one set Symmetric Difference → Non-common elements 💡 Key Learning: Data cleaning and comparison become much easier using sets in Python. 👉 Small step every day = Big growth in future 👉 Open to feedback and suggestions 👏 #Python #DataAnalytics #LearningJourney #100DaysOfCode #BeginnerToPro