Lecture 2 - Welcome to DATA 530!#

Platforms for Data Science#

Lecture Outline#

Introduction (10 mins)

Demo of DATA 530 Lab and GitHub (30 mins)

Demo of Gradescope (10 minutes)

Introduction (10 mins)#

The Essence of the course#

The overall goal of this course is for you to:

Install, configure, and use a variety of data analysis tools and software packages

This course covers how to configure data analysis environments, select appropriate tools for particular tasks, read documentation and get help, and use a variety of software packages for data analysis.

Other courses will build on this course by going deeper into the application of these systems and techniques.

Course Objectives#

Install and setup a variety of software tools and programs used by data analysts

Perform basic and advanced data analysis and visualization in Excel

Able to setup IDEs and write small programs in Python and R

Understand the pros and cons of each tool/software package and criteria to select the best tool for the job

Course Goals (for me)#

Provide the information in a simple, concise, and effective way for learning.

Strive for all students to understand the material and excel at the course.

Be available for questions during class time, office hours, and at other times as needed.

Provide an introduction to a variety of data analysis software and systems.

Emphasize the use of Excel as a easy-to-use, general tool for data analysis.

Academic Integrity#

Cheating is strictly prohibited and is taken very seriously by UBC. A guideline to what constitutes cheating:

Labs
- Submitting code produced by others.
- Working in groups to solve questions and/or comparing answers to questions once they have been solved (except for group assignments).
- Discussing HOW to solve a particular question instead of WHAT the question involves.

Exams (Quizzes)
- DATA 530 and 531 Quizzes are open book
- No communication about course content is permitted (with classmates, or others)

How to Excel in this course#

Attend class
- Participate in class activities and questions (clickers)

Attend and complete all labs
- Labs practice the fundamental employable skills as well as being for marks.

Practice on your own. Practice makes perfect.
- Do more questions than in the labs.
- Read the additional reference material and perform practice questions.

Systems and tools#

Course Material:
- Canvas, this site, and Github (all are identical)

Marks:
- Gradescope (copied to Canvas)

Feedback
- Gradescope

Hardware
- Your laptop (mostly)
- Cloud computing (when needed): https://ubc.syzygy.ca

Lab Assignments#

Weekly lab assignments are worth 50% of your overall grade.
Lab assignments will likely take more than the 1.5 hours of lab time.
In addition to labs, there will be a small weekly Learning Log to check-in on you, this is your opportunity to have 1:1 contact with me, and self-evaluate your performance.
Labs are due on Tuesdays at 6PM
- Each lab has a no-penalty 48 hour grace period.
- Don’t fall behind!
Install stack is due this week, Lab 2 will be due next week
Lab assignments are done individually but can be worked on collaboratively.
The lab assignments are critical to learning the material and are designed both to prepare you for the exams and build up your skills!

Labs during COVID-19#

Lab sections are 1+2 hours long, split into two to ensure physical distancing
- Section 1: 8:30 - 9:30
- Section 2: 13:30 - 15:30
All Lab sections will be on Zoom, so come to one or the other, or both if you like

Demo of DATA 530 Lab and GitHub (30 mins)#

Overview of Git and GitHub#

Terminology#

Term	Definition
Repository (repo)
Organization (org)
Commit
Add
Commit
Pull
Push
Personal access token (PAT)

Accepting a Lab assignment#

Step 1: Accept assignment
- Visit course page that has the lab links
- Click link to accept assignment

Step 2: Clone the repo on your local computer
- git clone <https://...>

Step 3: Navigate to that directory

Step 4: Launch a jupyter lab server

Step 5: Make changes and edit .ipynb file

Step 6: Making changes to the README.md
- code README.md and then make changes

Step 7: Commit your changes to the repository
- git add -A or git add .
- git commit -m "This is commit message of some sample code"

Step 8: Pushing the changes above
- git push

Demo of Gradescope (10 mins)#

Link to Canvas.

Q&A#

You have been exposed to a LOT already.

What questions do you have?

RISE Template#

from IPython.display import IFrame
from IPython.display import Markdown
# Additional styling ; should be moved into helpers
from IPython.core.display import display, HTML
HTML('<style>{}</style>'.format(open('styler.css').read()))

/tmp/ipykernel_1776/411737310.py:4: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display
  from IPython.core.display import display, HTML

from traitlets.config.manager import BaseJSONConfigManager
from pathlib import Path
path = Path.home() / ".jupyter" / "nbconfig"
cm = BaseJSONConfigManager(config_dir=str(path))
tmp = cm.update(
        "rise",
        {
            "theme": "sky", # https://revealjs.com/themes/
            "transition": "fade",
            "start_slideshow_at": "selected",
            "autolaunch": False,
            "width": "100%",
            "height": "100%",
            "header": "",
            "footer":"",
            "scroll": True,
            "enable_chalkboard": True,
            "slideNumber": True,
            "center": False,
            "controlsLayout": "edges",
            "slideNumber": True,
            "hash": True,
        }
    )

Data 530

Lecture 2 - Welcome to DATA 530!

Contents