Day 9 - 30 June 2024 Statistics - Finished the math prerequisites on Udemy, learned key notations, formulas, functions, and data basics. System Design - Studied TinyURL case and watched a mock interview for insights. Understand Thinking Process. #statistics #systemdesign

For Mathematics I selected some udemy course 1. Master statistic and ML :intubation, math, code by MIKE x COHEN 2. Complete linear algebra : theory and implementation of code by MIKE X COHEN #machinelearning #Statistics

Day 9 - 30 June 2024 Today's Plan: Starting ML and math alongside EDA. Focusing solely on EDA has been draining my interest, so I'll begin with statistics this week, then move to ML. Starting with statistics today. #machinelearning #datascience

Day 6, 7, 8 DSA - Finished all 5 types of sorting. Began Step 3 (Arrays) of Striver's DSA sheet and solved 5 out of 40 easy questions. System Design - Reading "Learning System Design" by Rohit Bhardwaj, focusing on "Design for Scale and Performance." #leetcode #dsa

Day 5 - 26 June 2024 ML - Completed EDA on the Heart Failure dataset from Faisalabad Institute of Cardiology, available on Kaggle. DSA - Learned hashing, solved 2 easy-level questions, and completed Step 1 (Learn Basics) of Striver's A2Z DSA sheet. #dsa #leetcode

Day 4 - 25 June 2024 ML - Dedicated the whole day to Matplotlib and Seaborn. Conducted EDA on the Kidney Chronic Disease dataset, using material from my 1-year PW Master Data Science course. DSA - Practiced two easy-level LeetCode problems. #machinelearning #eda

Day 3 - 24/06/2024 DSA - Started recursion, solved 5-7 basic GfG problems from Striver's A2Z DSA. Also began a System Design Udemy course, covering CAP theorem and designs for chat applications and Facebook's newsfeed. ML - Nothing today #leetcode #machinelearning

Here are the LeetCode problems I solved: 1. Two Sum 2. Contain Duplicate 2. Valid Anangram #Leetcode #DSA

Day 2 - 23 June 2024 Didn't sleep last night, spent the whole night on C++ containers. DSA - Solved 2 easy, 1 medium LeetCode problem, watched Striver's basic math video, and practiced all 7 problems. ML - Working on aggregating and reshaping DataFrames. #machinelearning #dsa

It's been 6 months since I started my job. Now, I'm focusing on DSA, learning C++ STL from Striver's YouTube channel, and also diving into Pandas and NumPy. I'll be posting daily updates again. Learning is a treasure that will follow its owner everywhere #machinelearning

🚀 Update: Last 2 Days 🚀 got into: LAMBDA functions MAP FILTER REDUCE RECURSION (basic) LIST COMPREHENSION Making progress in my Python skills! #Python #DataScience

What am I? I am a collection of items, Unordered and free, Enclosed in square brackets, you see. I can hold numbers, strings, or more, Each item with its place, Accessed by index, never a bore. I can be sliced and diced, Reversed or sorted, with ease, A versatile tool #Python

🔍 Python Riddles Time! 🤔💻 I'm a sequence, but not a list. You can't change me. What am I? I loop through numbers effortlessly, making tasks a breeze. What am I? Ready to crack these Python riddles? Comment your answers! 🕵️‍♂️🐍 #PythonRiddles #CodingChallenge

🚀 Update: Last 2 Days 🚀 Nailed 40 for loop questions, aced 40 control flow challenges, and revisited SQL, cracking 10 problems. 💻 Making progress in my coding journey! Solve Below Thread Riddle👇👇 #CodingLife #DataScience

Discovered some new functions I wasn't aware of until yesterday: ord() zip() all() and any() discard() union() frozenset() index()

Simple hack to banish duplicates: Convert your list to a set and then back to a list.

Confusion Cleared! 🌟 Both discard() and remove() serve to eliminate elements from a set. If the element isn't in the set, discard() does nothing without causing an error. In contrast, if the element is absent, remove() triggers a KeyError.

Why you can't nest sets: Trying to create a nested set leads to a TypeError. Sets aren't hashable, so they can't be used as elements in another set. Since sets are changeable, Python doesn't let them be hashed. 🚫🐍

🚀 Update: Last 3 Days 🚀 Been mastering Python by solving 400+ basic problems, using all sorts of built-in functions for lists, sets, and tuples. 🐍 Check out what new and interesting stuff I've learned in the thread below! 👇 #PythonLearning #DataScience

🧠Today's Insights Z-score: It tells us how far a data point is from the average of a dataset. Positive means above average, negative below. Standardization: Rescaling data to have an average of 0 and a standard deviation of 1. Simplifying comparisons across different scales.