Learning Python & SQL in 30 Days

Started learning Python for Data Analysis 🐍 Not going to lie — it feels confusing at times. But I’m focusing on: • Small steps • Practicing daily • Understanding concepts Progress may be slow, but it’s happening. #Python #DataAnalytics #LearningJourney #Consistency

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

Every expert was once a beginner 💡 Here’s my first step into data visualization using Matplotlib. Learning how to turn data into meaningful graphs! #LearningJourney #Python #Visualization

Built Linear Regression from scratch using Python (no libraries) Wanted to understand what’s happening under the hood before moving to sklearn. So I implemented a simple model to predict marks based on hours studied using Gradient Descent. 🔹 What I did: Implemented the prediction function (y = wx + b) Calculated Mean Squared Error (MSE) manually Computed gradients and updated parameters over 1000 epochs 🔹 What I learned: How gradient descent updates weights step by step Why learning rate plays a critical role How loss decreases as the model learns 🔹 Result: The model successfully learned the relationship. Example: If a student studies 9 hours → predicted marks ≈ 89.3 🔗 Code: https://lnkd.in/gPHCenhB Next step: implementing this using NumPy and then sklearn. #MachineLearning #Python #LearningInPublic

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

🚢 Completed Titanic Survival Analysis! Tools: Python | Pandas | Seaborn | Matplotlib Key Findings: ✅ Females had higher survival rate ✅ First class passengers survived more ✅ Higher fare = better survival chance GitHub: https://lnkd.in/gTsrns4y #DataAnalysis #Python #Pandas #DataScience

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

Python is powerful but libraries are what make it truly practical. From data analysis to machine learning, libraries help us build faster and smarter without starting from scratch. Still learning, still exploring. #Python #DataScience #MachineLearning

In Python, Pandas stands out as one of the most important libraries for data analysis. Why? Because of its efficiency in handling, cleaning, and analyzing data. From simple data manipulation to complex analytical tasks, Pandas makes the workflow smoother and more intuitive. Interestingly, in today’s data world, how well you know Pandas often reflects your strength in Python-based data analysis. For many, Pandas isn’t just a library—it’s almost synonymous with data analysis in Python. Mastering it can significantly boost your ability to extract insights and work with real-world datasets effectively. #DataAnalytics #Python #Pandas #DataScience #LearningJourney