Tuesday's CTPs Fall Data Science Home Repo

Class Dates, Times, and Zooms

• Tuesday's 6:30-9:00pm - Zoom Link
• Wednesdays's 6:30-9:00pm - Zoom Link
• Friday's 12:30-3:00pm - Zoom Link
• Friday's 6:30-9:00pm - Zoom Link

Link to Lecture Recordings

Links to Lecture Recordings Document If you dont have permission to view doc you are using the wrong email

Student Suggested Resources / Fav Sites

Contribute and share your favorite resources here.

HW Submission Sheets

All HWs are due at 12:01pm (noon) the day before the next class

• Tuesday's HW Submission Sheet HW due by 12:01pm (noon) on Mondays.
• Wed HW Submission Sheet HW due by 12:01pm (noon) on Tuesdays.
• Friday's 12:30 HW Submission Sheet HW due by 12:01pm (noon) on Thursdays.
• Friday's 6:30 HW Submission Sheet HW due by 12:01pm (noon) on Thursdays.

Instructor / TA Info and Office Hours

To attend office hours, DM them Slack during their time slot and they will DM you back a private meeting link.

Tuesdays Section

• Lead Instructor: Zack DeSario
  • Office Hours: Thursday's 1-2pm
• TA: Hussam Marzooq
  • Office Hours: Monday 12-1pp

Wed Section

• Lead Instructor: Chris Glosser
  • Office Hours: Thursdays 6:00 PM-7:00 PM
• TA: Kevin Li
  • TA Office: Fridays 5:00 PM-6:00 PM

Friday 1230 Section

• Lead Instructor: Zack DeSario
  • Office Hours: Thursday's 1-2pm
• TA: Georgios Ioannou
  • Office Hours: Fridays 3-4pm

Friday 630 Section

• Lead Instructor: Harpreet Gaur
  • Office Hours: Mondays 11am-12
• TA Georgios Ioannou
  • Office Hours: Fridays 3-4pm

Current Syllabus

Subject to change

• Week 01 [Aug 26-30]: Welcome + Github + Start Pandas
• Week 02 [Sept 02-06]: How to use Data Science [~1hr] // Collecting and Cleaning Data [~1hr]
• Week 03 [Sept 09-13]: Data Analytics // Data Viz // BI tools
• Week 04 [Sept 16-20]: Career Counselor talk [1hr], Project Ideation / Team Matchmaking [1hr], More Data Cleaning [~30min]
• Week 05 [Sept 23-27]: Unsupervised / Clustering
• Week XX [Sept 30 - Oct 4] : NO CLASS THIS WEEK
• Week 06 [Oct 07-11]: Classifiers via Logistic Regression
• Week 07 [Oct 14-18]: Regressors via Linear Regression
• Week 08 [Oct 21-25]: Decision Trees / Random Forest
• Week 09 [Oct 28 - Nov 1]: Thinking Like a Data Scientist
• Week 10 [Nov 04-08]: Neural Networks 1 - Intro // HuggingFace // Leveraging PreTrained Models
• Week 11 [Nov 11-15]: Career Counselor talk [1hr] // Neural Networks 2 - RAGS [1hr] // Project Time [~30min]
• Week 12 [Nov 18-22]: Neural Networks Cont... [1.5hr] 2.5 - RAGS // 1hr Project Time
• Week XX [Nov 25-29]: NO CLASS THIS WEEK
• Week 13 [Dec 02-06]: PROJECTS DUE!!! Final Project Demos // Most Import Curriculum Review // What’s Next

Homework Instructions: How to hand in your HWs.

HW assignments can be found in that each weeks README.md file. Open that weeks folder to find assignment

All HWs are due at 12:01pm (noon) the day before the next class

• Tue class: HW due 12:01pm on Mon
• Wed class: HW due 12:01pm on Tue
• Fridays (both): HW due 12:01pm on Thr

Submit your HW assignemts next to your name in your sections sheet below

• Tuesday's HW Submission Sheet
• Wed HW Submission Sheet
• Friday's 12:30 HW Submission Sheet
• Friday's 6:30 HW Submission Sheet

There are usually 3 sections of HW every week.

#1 Pre-Class HW [~1hr]

This covers the topic we are about to teach. This is HW that will help you come to class better prepared to learn the material that week.

• Watch / read / do the tutorial listed above.
• Go to your class slack channel.
• Find the usually most recent message from your TA instructor that says "Week X: Pre-Class learnings".
• Respond in-thread to that message with least one thing you learned from the videos/reading/or tutorial.
  • Your response can be It can be as short as one sentence, or as long as a book.
• Still in Slack, copy the link to your response.
• Pasted that to your response in HW Submission sheets Pre-Class column for that week.

Submit by pasting the link to your message under the "Pre-Class Slack Link" column.

#2 Exercise HW [~1hr]

This is a coding assignment that you usually start in class. It is located in the Exercise-DONT-EDIT-MAKE-COPY.ipynb file. See detaild instructions below. (Paste link in HW Submission sheet.)

0. Make a copy of Exercise-DONT-EDIT-MAKE-COPY.ipynb
1. Name the new copy as Exercise-[YOUR-INITIALS].ipynb. Zack DeSario's would be Exercise-ZD.ipynb.
2. Complete all the questions in YOUR COPY of the exercise file.
3. Push that file to your fork.

   ## NEVER DO --> git add *
   git add YOUR-EXERCISE-FILE.ipynb
   
   git commit -m 'YOUR COMMIT MESSAGE'
   
   git push
   

4. Open your github fork on the internet, click on your HW file you just pushed. Copy that exact link.
5. Copy that exact link, and paste it into the HW submission sheet in the Exercise column for that week.

Submit by pasting the link in the HW Submission sheet under the "Exercise.ipynb" column.

#3 LinkedIn Post [~10min]

Every week you have to post on LinkedIn. It can be anthing data science related unless instructed otherwise.

Publish the post (make sure its a public post.)

If no specific post topic is given that week, here are some topic ideas you can use.

• It can be about starting your CTP journey.
• Asking for advice on most important things to learn for entry level roles.
• Something you leanred in the pre-class videos.
• Why you love or hate pandas.
• Your favorite part about the class.
• A tip or trick that your learned in class.
• Anything related to data science or your journey.

Submit by putting the link to your LI post under the "LinkedIn Post" column.