I love assigning project code names. However, after a certain point the increase in projects decrease the amount of time I am able to commit to assigning a meaningful code name to each one. I needed something more scalable that I could quickly use and revisit later if the idea or prototype had any legs to stand on. Why spend all that time on something if it would never see the light of day? ...
July 2020 marked the 10 year anniversary for my blog. If you asked me a decade ago what I would be blogging about now my answer would be SEO. I never would have guessed it would shift to data science and data visualization topics. To end this chaotic year on a high note I want to share the top 10 things I learned over the course of my career in search engine marketing. I hope someone will find this useful regardless of industry and tenure in their field. ...
The 2020 US presidential election is coming down to the wire and I thought it would be fun to share how I calculate the answer to the following question: What is the distribution of the remaining ballots that Biden/Trump needs to win the electoral college votes for a given state? If we join this data point with Biden vs Trump mail-in ballot rates, or any other information for that matter, then it returns a fairly decent estimate on how thin/wide the margins will be for the US presidential race. ...
Ask my wife anything about me and she’ll be the first to testify that I have more books than clothing (lol). One book that dramatically changed my life and highly recommend reading is “Deep Work” by Cal Newport. Stuck at home, bored of Netflix/Hulu/Disney+ and need a reprieve from the onslaught of #coronavirus news? Here are my top 20 favorite non-business books (out of 250+) I recommend to get your mind off things pic.twitter.com/cC93iEaOFQ ...
The purpose of this article is to elaborate and visualize (surprise!) my comment I left over on Reddit: Professional data scientists: did you overcome the feeling of never knowing enough? If so, how? I think this concept can be applied to any field - not just data science. My personal advice that has worked for me to quell any “insecurities” is frame your mindset in terms of business value vs technical knowledge. ...
The coronavirus (a.k.a. COVID-19) is taking the world by storm with the World Health Organization officially characterizing the situation as a pandemic. I’m not an infectious disease expert but I couldn’t resist and write a quick #rstats script to visualize the total number of cases by country. Feel free to use and modify for your own needs: # LOAD PACKAGES library(tidyverse) library(scales) library(gghighlight) # DOWNLOAD DATA df <- read_csv("https://covid.ourworldindata.org/data/ecdc/full_data.csv") # PARSE DATA df_parsed <- df %>% filter(total_cases >= 100) %>% # MINIMUM 100 CASES group_by(location) %>% mutate(n = n(), day_index = row_number()) %>% ungroup() %>% filter(n >= 25, # MINIMUM 25 DAYS !location %in% c('World', 'International')) # EXCLUDE # GRAPH df_parsed %>% ggplot(aes(day_index, total_cases, color = location, fill = location)) + geom_point() + geom_smooth() + gghighlight() + scale_y_log10(labels = comma_format()) + labs(title = "COVID-19: cumulative daily new cases by country (log scale)", x = "Days since 100th reported case", y = NULL, fill = NULL, color = NULL, caption = "by: @eeysirhc\nSource: Our World in Data") + facet_wrap(~location, ncol = 4) + expand_limits(x = 70) + theme_minimal() + theme(legend.position = 'none') ...
Pretty fun murder mystery from @knightlab - can you find the killer using #SQL?https://t.co/vXcMtY2b1c — Christopher Yee (@Eeysirhc) December 20, 2019 CLUE #1 There is a murder in SQL City on 2018-01-15. select * from crime_scene_report where type = 'murder' and city = 'SQL City' and date = '20180115' CLUE #2 Witness 1 lives in the last house on Northwestern Dr. Witness 2 is named Annabel and lives somehwere on Franklin Ave. ...
Do you know who your favorite person on Twitter is? Probably! Did you ever want to quantify that statement? Probably not! Are you curious to find out who someone else’s favorite Twitter user is? Now you can with R! The code below is brought to you by Namita and her hilarious tweet: face some possibly uncomfortable truths about yourself and others with 4 easy lines of code using #rtweet and the #tidyverse pic.twitter.com/JtRnzk0xu7 ...