Lecture 1: Welcome to DSCI 100!

DSCI 100

Your Teaching Team

Instructors

  • Section 003 (R) Kenny Chiu
  • Section 004 (R) Lasantha Premarathna
  • Section 008 (R) Sky Sheng
  • Section 009 (R) Lasantha Premarathna
  • Section 101 (Python) Payman Nickchi

Coordinator

  • Julia Peng (courses@stat.ubc.ca)

If you have administrative questions (e.g. are sick and going to miss a quiz), please read the FAQ and contact the course coordinator

Your turn!

  • We encourage you to work collaboratively throughout DSCI 100
  • Let’s start by getting to know the person next to you
    • Introduce yourself
    • Which program are you from?
    • Why are you interested in Data Science?
    • Anything else you want to mention!

Course Guidelines

Visit and abide by the course Code of Conduct on Canvas.

Basically: be good to each other.

  • Help other students if they’re struggling
  • Respect others while they speak; don’t interrupt
  • Come to class prepared
  • Value diversity, respect everyone’s ideas
  • Be respectful and helfpul on Piazza

Remember: students in this class come from a wide variety of backgrounds - be good to each other!

What is data science exactly?

Data science is the use of reproducible and auditable processes to obtain value (i.e., insight) from data.

High-level goals of this course:

  1. Learn how to use reproducible tools (Jupyter + R) to do data analysis
  2. Learn how to identify and solve 4 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)

  2. Predict a value for a new observation/measurement (e.g., 10 km race time for 20 year old females with a BMI of 25)

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

  4. Estimate an average or a proportion from a representative sample (group of people or units) and use that estimate to generalize to the broader population (e.g., the proportion of undergraduate students that own an iphone)

Brief course walkthrough:

Read the Syllabus, Frequently Asked Questions, and JupyterGuide pages carefully!

Lecture/Tutorial Structure

  • The first class meeting of the week is for your “lectures”, and the second is for your “tutorials”.

  • We will post a reading before each lecture

    • e.g. the first chapter is reading_01

  • The readings often provide example answers for the questions in worksheets and tutorials!

  • We will kick off each lecture with a little intro

    • the slides should be up before each lecture, e.g. slides_01

Weekly Assignments

  • You will typically have one graded assignment per lecture / tutorial

    • Lecture worksheets start in the first class each week, due Saturday 11:59pm
    • Tutorial worksheets start in the second class each week, due Saturday 11:59pm
    • No “submit button” - our server takes a snapshot at the due date/time
    • No extensions for weekly assignments. We will drop the lowest grade for each worksheet and tutorial at the end of the semester.

Main Evaluations

  • One midterm covering ~6 weeks of material (summer ~3 weeks)

    • Same time & location of week 7’s tutorial (summer week 4). Invigilated in-person.

  • One final covering all the material in the course

    • To be scheduled by Classroom Services. Invigilated in-person.

  • A project (more on this later!)

    • You can collaborate with other people in class, but the work should be yours

First week learning goals:

  • Edit and execute code and markdown in Jupyter notebooks

  • Do the basics of data manipulation and visualization in R

  • Use the help and documentation tools in R

I’ll begin with a quick Jupyter notebook demo using your first assignment, Worksheet 1.

Demo

  • What is a notebook?
  • Cells with either code or text
  • Run with play button
  • Saving notebook
  • Restart kernel and run all before submitting
  • Shut down the server when you are done

Demo

  • If you run into a technical issue you can’t solve, first try to restart the kernel
    • Click the menu in the top left: Kernel -> Restart Kernel and Run All
  • If the problem persists, restart your Jupyter server instance.
    • Save the notebook (avoid losing work). Download it to be extra safe.
    • Click File -> Hub Control Panel
    • Click “Stop my server”
    • Click the Log Out button in the top right corner
    • Close all JupyterLab tabs
    • Refresh Canvas page, click worksheet link again

Now it’s your turn!

  • Navigate to Canvas, open worksheet_intro
  • Use Firefox or Chrome, not Safari

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