Lectures.Rmd

---
title: "Lectures"
output: 
  html_document:
    toc: false
    toc_float: true
    collapsed: false
    number_sections: false
    toc_depth: 3
    theme: lumen
    highlight: tango
---

Before you start browsing through the material consider the following strategies: There are (at least) two ways to progress through this material, depending upon how much time you have to devote to it each week.

1. If you are self-paced with at least 10 hours a week to devote to learning R, I’d recommend adopting the schedule below, which is designed for an intense but doable semester-long course, one module per week.  It is intended to take the average graduate student roughly 10 hours per week to complete all required tasks.  However, some students will find programming to be more challenging and may take up to 15 hours per week. Some will fly through the material in 5.

2. If you are self-paced with limited free time, you can take a more relaxed pace by alternating weeks: in the first week in each pair, complete the DataCamp materials, and in the second week, complete the project.  

I recommend that you work your way through the entire series. After working through each module, I strongly advise that you put your new skills to the test with the DataCamp courses and projects. These courses will significantly increase your learning curve since learning R is very much like learning a new language and you will need a lot of practising to master it!


```{r, message=FALSE, warning=FALSE, echo=FALSE}
#library(data.table)
library(readxl)
Lectures <- read_excel("LectureSpring2019.xlsx", sheet = 1, col_names = TRUE, col_types = NULL, na = "", skip = 0)
Lectures[is.na(Lectures)]<- " "
#Lectures <- fread("Lectures.csv")
knitr::kable(Lectures)

```

Resources:

1.	Irizarry, R. (2019). Introduction to Data Science. Data Analysis and Prediction Algorithms with R. eBook available at https://rafalab.github.io/dsbook/