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UBC-DSCI
GitHub Repository: UBC-DSCI/dsci-100-assets
 Path: blob/master/2019-spring/slides/01_intro_jupyter_r.ipynb
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Kernel: R

DSCI 100 - Introduction to Data Science

Lecture 1 - Getting started with Jupyter & R

2019-01-03

High-level goals of this course:

  1. Learn how to use reproducible tools (Jupyter + R) to do data analysis

  1. Learn how to solve 3 common problems in Data Science

Problems we will focus on:

  1. Predict a class/category for a new observation/measurement (e.g., cancerous or benign tumour)

  1. Find previously unknown/unlabelled subgroups in your data (e.g., products commonly bought together on Amazon)

  1. Predict a value for a new observation/measurement (e.g., 10 km race time for 30-35 year old males with a BMI > 25).

Course syllabus:

Read on your own time: https://github.com/UBC-DSCI/dsci-100/blob/master/README.md

TL;DR

Well, please do read the syllabus later... but for now...

Flipped classroom

  • read text/watch videos before class

  • I will kick off the lectures with a little intro (like today)

  • do lecture worksheets and activities in class (Thursdays), due Saturdays at 6pm

  • work on tutorial questions in class (Tuesdays), due Wednesdays at 10pm

  • you will need a laptop/chromebook/etc in every class! Don't have one? Borrow one from the library (see here).

Everything will be posted as links/buttons in Canvas

Collaborate

  • talk to each other (in class, on Piazza) as you work through the worksheets and tutorials

  • group project at middle-end of course

  • follow the DSCI 100 course code of conduct (TL;DR be respectful, inclusive and nice!)

Your teaching team

| Position | Name | | :------: | :---: | | Instructor | Tiffany Timbers | | Teaching Assistant | Madison Friesen | | Teaching Assistant | Harmeet Gill | | Teaching Assistant | Aaron Quinton | | Academic Assistant | Ian Flores |

First week learning goals:

  • use a Jupyter notebook to execute provided R code

  • edit code and markdown cells in a Jupyter notebook

  • create new code and markdown cells in a Jupyter notebook

  • load the tidyverse library into R

  • create new variables and objects in R using the assignment symbol

  • use the help and documentation tools in R

  • match the names of the following functions from the tidyverse library to their documentation descriptions: read_csv, select, mutate, filter, ggplot, aes

  • chain together two functions using the pipe operator, %>%

We've got a lot to do! Let's get started!

Jupyter notebook demo time!

Now it's your turn!

  • Use your neighbours, the TAs and me to help you get unstuck when needed!

  • I will interupt in about 20 minutes for a class activity.

5 + 2

I can write text