lifelong learner
See Datacamp article.
Six different uses of underscore(_)
Database Clinic: Neo4j is a fine online course. The content is easy to follow but it doesn’t cover enough fundamental concepts and theories on Neo4j. The Neo4J used in the video is not the most current version due to the video is published in 2017. So some lines of the code didn’t work. The Neo4J Desktop made the browsing the database easier than using web browsers. This course is good resource for exploring what graph database can do with flat data.
https://www.linkedin.com/learning/database-clinic-neo4j/welcome?u=76278132
Epidemiology: The Basic Science of Public Health is a great fundamental course offered by the University of North Carolina at Chapel Hill. It is a 6-week course covering history of epidemiology, what and how to measure disease frequency, various study designs, measures of association, and causality.
The videos are short and concise. The quizzes are great for catching up with what I just learned from video lectures. The content is quite heavy, but the instruction is well-organized to cover all the fundamental concepts. The definitions of the measures of health outcome are still unclear on my end, but this course was very helpful in learning some basics.
Course information: https://www.coursera.org/learn/epidemiology
This content of this book is extremely useful resource for learning and understand statistical concepts and techniques. It is great to see how Python and R codes are implemented for each concept, but only the snippet of codes are provided on many examples in the book. Fortunately, the publisher provides the whole codes by chapter. The widely used Python packages (pandas, numpy, scipy, statsmodels, sklearn, matplotlib, seaborn, and more) and R libraries can be easily located in each chapter and index.
R libraries used:
library(boot) #Bootstrap Functions
library(ca) #Simple, Multiple and Joint Correspondence Analysis
library(cluster) #”Finding Groups in Data”: Cluster Analysis
library(corrplot) #Visualization of a Correlation Matrix
library(dplyr) #A Grammar of Data Manipulation
library(ellipse) #Functions for Drawing Ellipses and Ellipse-Like Confidence Regions
library(FNN) #Fast Nearest Neighbor Search Algorithms and Applications
library(ggplot2) #Create Elegant Data Visualisations Using the Grammar of Graphics
library(gmodels) #Various R Programming Tools for Model Fitting
library(klaR) #Classification and Visualization
library(lmPerm) #Permutation Tests for Linear Models
library(lubridate) #Make Dealing with Dates a Little Easier
library(MASS) #Support Functions and Datasets for Venables and Ripley’s MASS
library(matrixStats) #Functions that Apply to Rows and Columns of Matrices (and to Vectors)
library(mclust) #Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
library(mgcv) #Mixed GAM Computation Vehicle with Automatic Smoothness Estimation
library(pwr) #Basic Functions for Power Analysis
library(randomForest) #Breiman and Cutler’s Random Forests for Classification and Regression
library(rpart) #Recursive Partitioning and Regression Trees
library(tidyr) #Tidy Messy Data
library(vioplot) #Violin Plot
library(xgboost) #Extreme Gradient Boosting
This book is published in 2017. The fundamentals of Python language is covered in this book. O’Reilly offers companion videos.
This book is a little outdated, but great for the beginners for grasping fundamentals. The companion video uses a text editor and Python on command line environment. It is great for learning how to use command-line arguments, but not much useful anymore since Jupyter notebook became dominant in the market.
