Introducing Advanced Distribution Calculator in Python

#Week3 | Sorting Algorithms: The Foundation of Efficient Data Handling This week, I dove deep into the world of sorting algorithms, exploring how different approaches tackle the fundamental problem of arranging data. Here’s what I covered: * Implemented Merge Sort, a classic divide-and-conquer algorithm. * Implemented Quick Sort, discovering the importance of pivot selection. * Implemented Heap Sort, leveraging the power of heap data structures. Tech Stack / Tools Used: Python, Jupyter Notebook Key Insights / Learnings: Understanding the trade-offs between these algorithms in terms of time complexity, space complexity, and stability was the key takeaway. Merge sort is stable but needs extra space, Quick sort is fast but unstable, and Heap sort is in-place. This Week’s Plan: Next, I'll be exploring another fundamental concept: search algorithms. Reflections / Takeaway: Mastering these sorting algorithms is a crucial step in writing efficient and scalable code. Project / Repo Link: https://lnkd.in/gTaqbQKc #AIJourney #MachineLearning #Python #DataStructures #Algorithms #SortingAlgorithms #LearningInPublic #12WeeksAIReset

💡 Day 96 of My #100DaysOfCode Challenge Today’s problem: LeetCode 3432 – Count Partitions with Even Sum Difference ⚙️ 📘 Problem Statement: Given an integer array nums, we need to count the number of ways to partition it into two non-empty parts such that the difference between their sums is even. ✅ Approach: Calculate the total sum of the array. Traverse the array while maintaining a running left sum. At each partition index, compute the right sum = totalSum - leftSum. Check if (leftSum - rightSum) is even → if yes, increase the count. 💻 Time Complexity: O(n) 🧠 Space Complexity: O(1) 🔥 Key Learnings: Strengthened understanding of prefix sums Practiced modular arithmetic in array partitions Improved analytical thinking on even-odd relationships #LeetCode #Python #CodingChallenge #100DaysOfCode #ProblemSolving #DataStructures #Algorithms #LearningEveryday

💯 Day 46 / 100 – 100 Days of #LeetCode Challenge 🔁 Problem: 326. Power of Three Goal: Given an integer n, return true if it is a power of three. Otherwise, return false. An integer n is a power of three if there exists an integer x such that n == 3ˣ. Approach: ✅ Initialize a list with [1] (since 3⁰ = 1). ✅ Iterate from 1 to n, compute powers of 3 using temp = 3 ** i. ✅ Break the loop if temp > n. ✅ Store valid powers in a list and check if n exists in it. Complexity: ⏱ Time: O(log₃n) – since powers of 3 grow exponentially. 🗂 Space: O(log₃n) – storing powers of 3 up to n. 💡 Key Takeaway: Patterns like “power of a number” can be generalized using loops and exponentiation. Recognizing exponential growth helps optimize logic for mathematical problems. #100DaysOfLeetCode #Day46 #Python #Math #ProblemSolving #LeetCode #CodingChallenge #PowerOfThree #LogicalThinking

🚀 Day 18: Stats Behind Multivariate Plots & Advanced Charts Today I moved beyond single-variable and two-variable plots, diving into multivariate analysis — where multiple variables are studied together to find deeper insights. 📊 Lesson 1: Multivariate Plots Learned how to visualize relationships among 3+ variables. Used techniques like pair plots, heatmaps, and 3D plots to uncover complex interactions. 📊 Lesson 2: Advanced Charts (Bonus) Explored advanced visualizations such as violin plots, swarm plots, and joint plots. Understood how they give a clearer picture of data distributions and relationships. 👉 Multivariate plots are especially powerful for feature analysis in machine learning and for drawing insights from real-world datasets. #Day18 #Statistics #Python #DataVisualization #DataScience #100DaysOfCode

🚀 Day 16: Measures of Dispersion – Variance & Standard Deviation After learning about averages, I explored how data points spread out around the center. This is key to making accurate conclusions. 📊 Lesson 1: Variance Measures how far each data point is from the mean. A higher variance means more spread, while lower variance means values are closer together. 📊 Lesson 2: Standard Deviation The square root of variance — more intuitive to understand. Commonly used in real-world analytics to measure risk, volatility, or consistency. 👉 Dispersion tells us not just what the average looks like, but also how consistent or varied the data is. This is crucial in finance, research, and machine learning. #Day16 #Statistics #Python #DataScience #LearningInPublic #100DaysOfCode

#Week2 | Mastering Trees: From Traversal to Validation This week, I dove deep into the world of tree data structures, specifically Binary Search Trees (BSTs). It was a fantastic exercise in understanding recursion and level-order logic. Here’s what I covered: * Implemented a BST from scratch, including node creation and value insertion. * Wrote functions for various traversal methods: inorder and level-order (BFS). * Built utility functions to search for values, find the minimum value, and calculate the tree's height. * Capped it off by creating a function to validate if a tree is a proper BST. Tech Stack: Python, Jupyter Notebook A key takeaway for me was seeing how an inorder traversal of a BST naturally produces a sorted list of its nodes. It’s a simple but powerful property! Next up: I’ll be moving on to another important data structure: Heaps! You can follow my progress and check out the code here: https://lnkd.in/gnJw38QM #AIJourney #DataStructures #Python #Trees #BST #LearningInPublic #12WeeksAIReset #CodingChallenge

I am sharing here the full materials for the workshop on Explanatory Statistical Modeling which I delivered at the SIOP conference last Thursday. The online presentations, interactive assignments, solutions and take home project can be found at https://lnkd.in/eWvtHyb7. This workshop tried to condense a full day program into 3.5 hours, so we did not cover the fourth module on advanced methods. Ideally this content would be delivered over a full day, or over several sessions akin to a multi-week short lecture course.. It is free for you to use as long as you credit me as the source. #statistics #analytics #datascience #peopleanalytics #rstats #python #ai #technology

Developed an energy consumption forecasting model in Python, using regression analysis and real weather data. The core graph visualizes time series patterns—plotting energy use over hours, days, and months—while showing how temperature and humidity influence demand. By encoding seasonality, trends and anomalies stand out, helping stakeholders see when and why energy spikes occur. This approach empowers smarter, more sustainable planning through actionable data insights. GITHUB LINK : https://lnkd.in/gnJrugBQ #Python #DataScience #MachineLearning #Forecasting #TimeSeries #EnergyAnalytics #SmartCities #Sustainability #Visualizatio

Write cleaner visualization code with Altair 📈 Matplotlib requires manual data transformation and explicit configuration for every visual element. Altair uses declarative syntax based on Vega-Lite for intuitive, readable visualizations. With Altair, you can describe what you want, not how to create it: • Automatic formatting with type encoding (:T, :Q, :N, :O) • Skip groupby with built-in aggregations: mean(), sum(), count() • Easy chart composition and layering • Interactive features with minimal code See the first comment for the complete guide 📖 #DataScience #Python #Altair

I randomly came across this YouTube channel — Chai Aur Code by Hitesh Choudhary — and it’s truly a gem! 💎 I recently went through his NumPy Full Course as a part of my revision, and it was totally worth it. Hitesh’s way of explaining concepts — from array basics to advanced operations — makes even technical topics easy to grasp and apply. 📘 Key Takeaways : Strengthened my understanding of NumPy arrays, indexing & slicing Practiced reshaping, broadcasting, and mathematical operations Connected concepts with real-world Data Science use cases. If you’re new or brushing up your Python for Data Science or Data Analytics, this course is a absolutely perfect you! #DataScience #Python #NumPy #ChaiAurCode #HiteshChoudhary #Upskilling #ContinuousLearning #DataAnalytics