Announcements

Dear DS 1 students,

If you are struggling with anything in particular, feel free to start a follow-up discussion for this post on Piazza (remember that you can also post anonymously, if you feel more comfortable not sharing your name). You can also review the material during the office hours or schedule an appointment with Prof. K.

• Project 1 has been posted. Start on it right away!
• As usual for labs, which are always due before the next lab, Lab08 will be due on the same day as the Project 1 draft. Lab08 should help you with the project, so I recommend working on them in tandem, especially, since Lab08 largely echoes Lab07.
• After you submit your Project 1 draft on Wednesday, you have until Friday to finalize your submission. Use the office hours throughout the week to get feedback from the mentors on your project.

We wish you a Happy Thanksgiving and a wonderful long weekend!

Dear DS 1 students,

As we discussed in class, these past two weeks flew by very quickly! Since the quarter is only going to be picking up its speed, it would be useful for us to pause and review where we are and where we are going.

The end of the week is usually a good time to look back and assess what’s working and what can be tweaked or improved. If you are wondering how to allocate your time in this course, we have a dedicated page on the website to help you: https://ucsb-ds.github.io/ds1-f20/success.

If you are struggling with anything in particular, feel free to start a follow-up discussion for this post on Piazza (remember that you can also post anonymously, if you feel more comfortable not sharing your name).

Keep in mind that the index page of our course website also has all important links that we need in this class.

Due Dates

• So far, the only assignment that was due is Lab01.
• This coming Tuesday (Oct 20) the participation activity (PA 1) that’s listed on Piazza (post @15, Logical Fallacies) will be due.
• As usual for labs, which are always due before the next lab, Lab02 will be due Oct 21.

For due dates, keep an eye on the Schedule page. We will also be adding them to the Google calendar that’s linked on our Calendar page.

The Calendar page also has a list of dedicated office hours. You can learn more about the mentors who are holding the office hours and are helping with the course by checking our Staff page. You can also schedule a meeting with me using a link that’s posted there.

Lectures

So far, we’ve had 5 lectures.

Week 0 - Lecture 0: Welcome to the course

During our very first meeting, you got to meet our mentoring team, learn about the course and its structure, worked with each other on an in-class activity to figure out what type of problems you are interested in and what kind of data you’d need, and dipped your toes into what data science means with a mention of John Snow that you got to read about in Chapter 2 of the book.

During Week 1, we had two lectures:

• Lecture 1: Data Science Mindset / Biases
• Lecture 2: Variables, expressions, Jupyter notebooks

We continued building the community in this class by working with each other on a fun “guess the sequence” group activity. We discussed how it ties with the data science process and mindset, and how our biases might affect the approaches and/or hypotheses. We also looked at a research on vultures and their habitat, which illustrated how understanding data and explaining patterns might require visualizations as well as expertise in different domains.

After Lab01, we wrapped up the week by discussing the concepts that we covered in lab and saw a demo of additional Jupyter notebook features.

Lab 01: Expressions

First lab using Jupyter Notebook. The goals were to practice how to:

1. navigate Jupyter notebooks;
2. write and evaluate some basic expressions in Python, the computer language of the course;
3. call functions to use code other people have written; and
4. break down Python code into smaller parts to understand it.

This lab covered parts of [Chapter 3 and 4] of the online textbook.

During Week 2, we had two lectures:

• Lecture 3: Functions, strings, arrays, ranges
• Lecture 4: Arrays and Ranges; Critical Thinking and Logical Fallacies

This week we dove more into the computing tools. During lectures we introduced and reviewed the concepts from Lab02. During Lecture 4, we also worked in breakout rooms to go through an example of how to assess an advertisement claim that was using FBI statistics. We wrapped it up by starting on the participation activity (PA), which will be due next week.

Lab 02: Data Types

In this lab, you practiced working with text, importing code/modules (and displaying pictures!), manipulating arrays, and using ranges.

This brings us to the end of Week 2. Feel free to share your highlights in the follow-up discussion on Piazza.

In the following weeks, we are going to be aligning with the schedule listed on the course roadmap.

The due times in this class are always at 9AM, so each week, we will usually have

• PAs (Participation Activities) due on Tuesdays at 9AM
• LAs (Lab Activities) due on Wednesdays at 9AM and every other week, we will have
• HW (Homework) due on Saturdays at 9AM.

There will be exceptions due to holidays and course projects, but that’s going to be our general structure for the next few weeks.

With that, I’m wishing you a very good weekend, and I look forward to our next class!

Prof. K

Nothing will be due this week! We are currently continuing to enroll students and are issuing add codes on an individual basis.

Our first lab will be this Wednesday (Oct 7) via the same Zoom link as the lecture. The link to Lab01 will be posted on the Schedule page.

The labs will always be due the following Wednesday, so you’ll have a week to finish the lab. Lab attendance is highly encouraged; if you have a conflict with the section that you are enrolled in, you can join the other section instead.

Everyone will be added to Piazza and Gradescope on Friday (Oct 9). Thank you for your patience and understanding.

In the meantime,

• read Sections 1.2 and 1.3 of our online textbook
• read Chapter 2
• follow the steps in the Getting Started section.

Hello world! Welcome to Data Science Foundations!