When we want to accomplish a goal, whether it is to land our dream job or to get promoted, most of us often do things that we think looks good on paper but we often fail to ask ourselves 2 very important questions: I) Does this thing bring us closer

Costly mistake A common mistake that I often saw junior data scientist made is writing token access and credentials directly into the Python script (see image below). This is especially true for those who seldom work in a production environment. It is a bad practice because when we push our

Growing up, I have noticed that when I become good at something, I am also becoming better at other different but related skills. Let me give an example. Back then in primary school I am not good at maths, however during one school break I decided to learn some Singapore

Tools Tool #1: Sum (minus / multiply / divide) 2 column for each row SELECT column1, column2, (column1 + column2) AS sum_of_columns FROM your_table; Example: product_id price quantity 1001 50 2 3001 20 4 SELECT product_id, quantity, price, (quantity * price) AS total FROM product_table; product_id price

Recently I have been working on designing end to end data pipelines on Azure for my company. In this post, I document all my thought process and reasons for all the design choices made when architecting data pipeline. I also compile them into a checklist which I will continue to

This post is part 2 of cloud data architecture series. I will write about the design choices made when designing a sales data pipeline on cloud. What data stored in each data lake tiers? We use ADLS as our data lake and we sperate it into 3 tiers: Bronze, Silver

This post is part 1 of cloud data architecture series. I will write about the design choices made when designing a sales data ingestion pipeline on Azure. What business use case will this data pipeline used for? i) Reporting and analytics: Management can monitor the sales trends of each outlets

How it works? If current index X but value Y (not same), we swap it with index Y Let's say we have the array below: [1,4,3,2,0] Our current index is 0 but value is 1, we swap it with index 1 [4,1,3,2,0]

How to study 1) Read this AWESOME article by Tim Park on BFS. Follow along the article to solve Leetcode #104 104. Maximum Depth of Binary Tree class Solution: def maxDepth(self, root: Optional[TreeNode]) -> int: queue = [(root, 1)] res = 0 if root is None: return 0 while queue:

Main Idea Iterate over all intervals -> If overlap, merge -> If non overlap, continue next interval How to study 1. Read pseudocode to get overall idea on how to solve def merge(intervals): # Sort intervals # Iterate over the input intervals # Check two intervals # If they overlap, merge them. # If

Main Ideas Most solutions use these 3 blocks: i) Dummy nodes: Help us deal with edge cases ii) Pointers ii) Reverse Linked List When solving a problem, think: how can I use these 3 blocks? How to study 1. Learn how to reverse linked list by solving Leetcode #206 (Make

TL;DR Create 2 pointers -> Move 2 pointers until not valid (using while loop) How to study 1. Familiarize all 4 patterns by solving a Leetcode question for each pattern. Pattern #1: Pointers from both end 167. Two Sum II - Input Array Is Sorted def twoSum(self, numbers:

TLDR Expand window -> Check window validity -> If not valid shrink window until valid -> (repeat process) How to study 1. Understand Neetcode's solution to Leetcode#3 2. Understand the framework below, compare between the framework and Leetcode#3 solution. Framework: A = {} l, r = 0, 0 result = 0 # increase

Propensity score helps us to reduce confounding bias when randomization is not possible anymore. Our Business Objective We are a team of data scientist in a milk bubble tea company with thousands of franchises. Our marketing team launched a voucher campaign where customers who receive vouchers can redeem them at

Using Decision Tree Decision tree is a machine learning algorithm used for classification and regression tasks. A decision tree look like this Lets say we want to predict whether a person is diabetic using decision tree. The person has the following features: Overweight Family Diabetic Age > 45 No Yes No

We are a team of data scientist in an ecommerce coffee company. Our company's marketing team launched an email marketing campaign sent to 1000 customers to promote the new Brazilian coffee. Our job is to measure how effective is the marketing campaign. Let's start the task. Treatment & Outcome We want

In the book "So Good They Can't Ignore You", Cal Newport presents the idea that we should develop a rare and valuable skill. But what skills should we choose to master? I will extend the book's idea and argue that we should choose skills based on inflection points that are

Table of content How database store Index and Table Structure of Index (B-Trees) Logarithmic scalability of B-Trees Create Index Implementation How database store Index and Table [pic 1] Row_id helps database to uniquely identified each rows. Some databases (like Postgresql) create their own system maintained id as row_id

Building Intuition Training Phase We have the dataset. We want to create 10 trees. Age Gender Exercise Weight (y) 36 Male Yes 83 41 Female Yes 56 39 Male No 92 STEP 0: We first find the average of all y values \(\frac{83+55+92}{3} = 77\) STEP 1:

Multinomial Naïve Bayes Let us see how naïve bayes works by performing a movie review sentimental analysis task. Let’s say we have a small movie review dataset which looks like this: Movie Review Sentiment Review 1 Good Review 2 Bad Review 3 Good Review 4 Good Review 5 Bad

useEffect Hook * Run first time and every renders import { useState, useEffect } from 'react'; const Home = () => { const [counter, setCounter] = useState(0) useEffect(() => { console.log("Run on every render") }) const handleClick = () => { setCounter(counter + 1) } return ( <>

This Is A Counter

Click

{counter}

) } export default Home; * Run first time only

Do not aim for feeling productive rather aim for actually being productive. You might wonder, what's the difference? An example of making myself feel more productive is reading tech news on tech crunch or watching educational YouTube videos. At first, it might seem that I have learn some new things

To be good at something, we always need to put in the reps. When we are just a beginner at something, putting too much emphasis on quality might stop us from making progress and get things done because we are afraid of creating something of low quality. By shifting our

I just realized today that a lot of activities that had high returns are often filled with uncertainties. Starting a business with no direct path to profit, training day and night to win the Olympic gold medal and going to interviews while working at a restaurant with the hope of

Navigating landscapes which do not have a clear outcome or path requires a different framework. When I want to create a product description copywriting app for e-commerce store owners, I am bombarded with thousands of questions and self-doubt. How can I monetize the app? Would people use my app since