<The Fine Art of Small Talk> by Debra Fine A interesting book to help introvert and shy person to enjoy the fun of small talk in business. It's more of a skill that can be trained than a born gift. <今日简史 21 Lessons for the 21st Century> by Yuval Noah Harari Although this book [...]
In professional environment, there are a lot of tasks that either require a Excel file as source, or one as a delivery, and most of time we ourselves have minimized the workload to an extent that it has become a daily boring task. What if I tell you there is a way to potentially [...]
Prologue This blog only serves as keeping my thoughts and notes at one place so that they'd be easier to retrieve when I need them. As expected, key words and random thoughts can spread everywhere and I will try keeping them organized to the best I can. I have seen so many times when [...]
I just came across a very insightful article about the status quo of common understanding of the hype in Machine Learning. The author uses analogy of chef and kitchen appliance to compare with Machine Learning in business and in research. Nowadays people and companies are passionate about the development of 'kitchen appliance' while ignoring what they [...]
Introduction There are mainly 2 types of DS interview questions you'll meet with: 1. Technical and 2. Business. For technical questions, you'll be asked about Brainteasing (quickly solve a math problem), Machine Learning (how do you deal with overfitting?), or Database related case questions. For business questions, be prepared for Estimation, Data Strategic, or [...]
The CIRCLES for Product Design Comprehend the situation Identify the customer Report the customer's need Cut, through prioritization List solutions Evaluate Trade-offs Summarize your recommendation Mocked Case Examples and Feedback "How to improve Facebook Marketplace?" Structured Answer: C - FB Marketplace is a platform where people discover, buy and sell items with their community [...]
GGplot实操100练
#并行计算-parallel包 #parallel包的思路和lapply函数很相似，都是将输入数据分割、计算、整合结果。只不过并行计算是用到了不同的cpu来运算。下面的例子是解决欧拉问题的第14个问题。 # 并行计算euler14问题 # 自定义函数以返回原始数值和步数 library(parallel) func <- function(x) { n = 1 raw <- x while (x > 1) { x <- ifelse(x%%2==0,x/2,3*x+1) n = n + 1 } return(c(raw,n)) } library(parallel) # 用system.time来返回计算所需时间 system.time({ x <- 1:1e6 cl <- makeCluster(4) # 初始化四核心集群 results <- parLapply(cl,x,func) # lapply的并行版本 res.df <- do.call('rbind',results) # 整合结果 stopCluster(cl) [...]
#Multiplot in ggplot2 install.packages("gridExtra") library("gridExtra") install.packages("cowplot") library("cowplot") df <- ToothGrowth # Convert the variable dose from a numeric to a factor variable df$dose <- as.factor(df$dose) head(df) #----Cowplot---- # The cowplot package is an extension to ggplot2 #and it can be used to provide a publication-ready plots. library(cowplot) # Default plot bp <- ggplot(df, aes(x=dose, y=len, color=dose)) [...]
Here is an interesting package called 'profvis' that I recently get to know about. It is quite useful when you are trying to locate the place where takes the longest time when running the code. Below is an official example. #R system monitoring install.packages('profvis') library(profvis) # To run code with profiling, wrap the expression in profvis(). [...]