Demystifying PCA: 3 Steps to Simplify High-Dimensional Data

Imagine trying to explain a complex 3D object using just one photo. Pick the wrong angle, and you might lose critical detail. The same thing happens when you try to simplify high-dimensional data. If you just drop a random axis to make it 2D, you could throw away crucial information and end up with a tangled mess. Enter Principal Component Analysis (PCA)! Instead of randomly dropping data, PCA rotates your entire coordinate system to find the absolute "best camera angle". Watch our quick 60-second visual breakdown below! 👇 if you want to dive deeper into the math behind the magic and get the Python code, Watch the full tutorial here: https://lnkd.in/gdGkEw8r #PCA #MachineLearning #DataScience #DataVisualization #Schovia #Shorts

📊 Data Science Foundations Series – Part 1: NumPy Basics I’ve started strengthening my fundamentals in data science, beginning with NumPy. Here are some key takeaways: ✅ NumPy is faster than Python lists due to contiguous memory storage ✅ Supports vectorized operations (no need for loops) ✅ Efficient for handling large numerical datasets Some concepts I explored: 🔹 Array creation using np.array() and np.arange() 🔹 Reshaping data with .reshape() 🔹 Indexing and slicing (including negative indexing) 🤯 One interesting learning: m1[-5:-1:-1] returns an empty array. Reason: When stepping backwards, the start index must be greater than the stop index. ✔️ Correct approaches: m1[-1:-5:-1] m1[-5::-1] This small detail helped me better understand how slicing actually works under the hood. 📌 Next: Vectorization & Broadcasting #DataScience #Python #NumPy #LearningInPublic #CareerGrowth

📅 Day 9/30 — NumPy Indexing & Slicing Continuing my 30-day journey into data science, today I explored how to efficiently access and manipulate data using NumPy arrays. What I worked on today: 🔢 Accessing elements using indexing (including negative indexing) ✂️ Extracting data using array slicing 🔁 Selecting elements using step slicing 🎯 Using index arrays to pick specific elements 🧠 Applying boolean masking to filter data based on conditions It was interesting to see how NumPy provides powerful ways to quickly access, modify, and filter data, which is very useful when working with large datasets. ➡️ Next step: exploring more advanced NumPy operations and applying them to real-world data. #LearningInPublic #Python #DataScience #NumPy #30DaysOfLearning #ProgrammingJourney

🚀 Quick NumPy Revision + Assignment Completed While learning Data Science, I created these quick notes for NumPy to revise important concepts like: ✔ Creating NumPy arrays ✔ Understanding array dimensions (ndim) ✔ Reshaping arrays ✔ Random number generation ✔ Functions like zeros, eye, and linspace ✔ Array operations & indexing ✔ Mathematical operations on array ✔ Searching array These small notes help me revise NumPy faster while practicing Python for Data Science and Machine Learning. 📂 Assignment available on GitHub: https://lnkd.in/dX66epMw #Python #NumPy #DataScience #MachineLearning #LearningInPublic #100DaysOfCode

𝐓𝐨𝐩 𝐒𝐞𝐚𝐛𝐨𝐫𝐧 𝐏𝐥𝐨𝐭𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐌𝐮𝐬𝐭 𝐊𝐧𝐨𝐰 𝐢𝐧 𝟐𝟎𝟐𝟔 Data analysts rely heavily on visualizations to understand patterns hidden inside datasets. Python’s Seaborn library simplifies statistical visualization and helps analysts create clear, attractive charts with minimal code. This guide explains the most important Seaborn plots every data analyst should know in 2026. From scatter plots to heatmaps, these visualizations help uncover trends, correlations, and patterns quickly. #DataAnalytics #PythonVisualization #SeabornPlots #DataScience #PythonProgramming #analyticsinsight #analyticsinsightmagazine Read More 👇 https://zurl.co/mvmNa

𝗪𝗲𝗲𝗸 𝟱 of my 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 & 𝘔𝘓 journey with ParoCyber. Here's what I learned: ☑️ Pandas Series: creating a one-dimensional data structure from a Python list. ☑️ DataFrames – organizing data into rows and columns, similar to a spreadsheet or table. ☑️ Creating DataFrames from dictionaries with columns like Name, Age, and City. ☑️ NumPy Operations: performing mathematical operations on arrays and exploring indexing. I have learnt that NumPy helps with fast numerical calculations, while Pandas makes it easier to organize and explore datasets. Also, dataFrames make data much easier to understand because everything is structured in rows and columns. It almost feels like working with Excel, but using Python. Seeing how simple lists and dictionaries can be turned into structured datasets made me realize how Python is slowly preparing us to work with real-world data. #DataScience #MachineLearning #Python #ParoCyber

🚀 Day 48 of My 90-Day Data Science Challenge Today I worked on Feature Selection Techniques. 📊 Business Question: How can we select the most important features to improve model performance? Feature selection helps remove irrelevant or redundant features and improves efficiency. Using Python & scikit-learn: • Applied SelectKBest • Used Correlation Analysis • Understood Feature Importance • Reduced dimensionality • Improved model performance 📈 Key Understanding: Not all features are useful — selecting the right ones improves accuracy and speed. 💡 Insight: Removing unnecessary features helps reduce overfitting. 🎯 Takeaway: Better features lead to better models. Day 48 complete ✅ Improving data quality 🚀 #DataScience #MachineLearning #FeatureSelection #Python #LearningInPublic #90DaysChallenge

🚢 Titanic Survival Analysis Project Analyzed the Titanic dataset to explore patterns influencing passenger survival using Python and exploratory data analysis. 🔎 Identified key factors such as gender, passenger class, and age that significantly impacted survival rates. 📊 Performed data cleaning, preprocessing, and visualization to uncover meaningful insights. 📌 Compared survival patterns across different passenger groups to better understand historical outcomes. 🛠 Tools: Python, Pandas, Matplotlib, Seaborn, Jupyter Notebook 🔗 GitHub Repository: https://lnkd.in/gXBzREJ7 #DataAnalytics #DataScience #Python #EDA #MachineLearning

Insert Interval (LeetCode 57) - Medium I explored a more optimized way to handle intervals when the input is already sorted. Instead of re-sorting everything, I learned how to process the intervals in a single linear pass. Key Learnings: * Linear Scan: Since the input is sorted, we can divide the problem into three logical parts: Before overlap, During overlap (merge), and After overlap. * In-place Merging: For the overlapping part, we simply update the start to the min and the end to the max of the conflicting intervals. * Efficiency: No sorting means we save time! This approach is much faster for pre-sorted data. Complexity: ⏱️ Time Complexity: O(N) — because we only iterate through the list once. 📂 Space Complexity: O(N) — to store the result list. Consistency is the key #LeetCode #CodingJourney #Blind75 #SDEPrep #DataStructures #Python #ProblemSolving #TechCommunity

𝗧𝗵𝗲 𝗴𝗿𝗮𝗽𝗵 𝗹𝗼𝗼𝗸𝗲𝗱 𝘀𝗶𝗺𝗽𝗹𝗲. 𝗧𝗵𝗲 𝗰𝗼𝗱𝗲 𝘁𝗵𝗮𝘁 𝗯𝘂𝗶𝗹𝘁 𝗶𝘁 𝗱𝗶𝗱𝗻'𝘁. Day 22 of #1000DaysOfLearning 🗓️ Today I plotted my first graph in matplotlib — a 𝘀𝗰𝗮𝘁𝘁𝗲𝗿 𝗽𝗹𝗼𝘁. 📊 What I worked through: → plt.scatter() vs plt.plot() — and what each communicates → Controlling 𝗺𝗮𝗿𝗸𝗲𝗿 𝘀𝗶𝘇𝗲, 𝗰𝗼𝗹𝗼𝗿, 𝗹𝗮𝗯𝗲𝗹𝘀, 𝘁𝗶𝘁𝗹𝗲𝘀, 𝗮𝗻𝗱 𝗹𝗲𝗴𝗲𝗻𝗱𝘀 → Grouping data points using slicing and color lists The code gets long for what looks like a simple output. But 𝘁𝗵𝗮𝘁 𝗹𝗲𝗻𝗴𝘁𝗵 𝗶𝘀 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 — every label, every color, every legend entry is a deliberate line. Matplotlib assumes nothing. 🎯 Also noticed that 𝘇𝗶𝗽 𝗮𝗻𝗱 𝘁𝘂𝗽𝗹𝗲 𝘂𝗻𝗽𝗮𝗰𝗸𝗶𝗻𝗴, which felt less useful in regular Python, come up naturally when working with coordinate data. Made more sense here than any time I saw them before. 💡 #Python #DataScience #Matplotlib #DataVisualization #LearningInPublic