Spring 2022 Syllabus

A course unlike any other data science course...?

Institution: UC Berkeley Sutardja Center for Entrepreneurship & Technology (SCET)

Course Name: Applied Data Science with Venture Applications: Data-X

Course Number: 

  • INDENG 135 (undergraduate students)
  • INDENG 235 (graduate students)

Units: 3

Semester: Spring 2022

Faculty and Graduate Student Instructors (GSIs):

Role Name and Email Office Hours
Faculty Ikhaq Sidhu, sidhu@berkeley.edu By appointment, and Fridays after class ends
Faculty Derek S. Chan, derekschan@berkeley.edu  By appointment, and Fridays after class ends
GSI Mahan Tajrobehkar, mahan_tajrobehkar@berkeley.edu Wednesday 4-5 PM PST Via Zoom

Link: https://berkeley.zoom.us/j/93911930495

GSI Tung-Ling (Tony) Li,

tungling_li@berkeley.edu

Thursday 1 - 2PM PST Via Zoom

Link: https://berkeley.zoom.us/j/5866993284

*At office hours and on Slack Data-X (INDENG 135 / 235), for specific questions on details about algorithms, coding, and math, please ask the GSIs, Mahan and Tony. As students, please also ask and help each other.

Meeting Day/Time: Fridays, 2:10-5:00pm from 1/21 to 4/29/2022. No class Friday 3/25 due to the spring recess.

Meeting Location: Evans Hall 10

Course Website: You are at the course website, https://datax.berkeley.edu/syllabus-spring-2022/ 🙂

Course Prerequisites: Students from all majors and countries are welcome since diversity adds value in co-building applications and systems to help society. However, the course has technical application, so students should have completed a probability or statistics course, and have the ability to write Python code (or be committed to complete foundational Python material by week 3 end):

  • Code Academy free beginner course and a corresponding assignment, and
  • Data-X introductions to Pandas and Numpy and corresponding assignments.

GSIs will hold Python workshops during office hours in weeks 2-3.

  • If you struggle on Python homework due weeks 2-3 but show commitment (e.g., attend GSI workshops and submit work), you are advised to remain enrolled. 
  • If you struggle on Python homework due weeks 2-3 but didn’t attend GSI workshops, you are advised to drop for your team and yourself.
  • Commitment is the most important — grit rather than expertise is more important since working toward expertise can be developed over time through grit. Students who were less technical but committed did well in the Fall 2021 course. Plus additional support has been added to the Spring 2022 course, based on prior student feedback.

Course Description: 

Today, the world is literally reinventing itself with Data and AI. However, learning a set of ‘related theories’ vs. ‘making it work’ is not the same, especially for innovating competitive advantage, economic strength or social good, and even national/global security.

Thus, Data-X is a mixture of half Data Science and Data Systems and the other half on Innovation, which includes behaviors and processes required to create new data-related applications.  At its core, Data-X is an advanced project course which is highly applied.  The course surveys a variety of key theoretical concepts and software tools. Data-X has a focus on helping students validate, design, and build data science, AI, and Machine Learning applications and systems (not only algorithms) for real-world user impact.

The course covers 3 tracks aimed to guide students’ project work and future industry work: 

  • code and theory (e.g., high- and low-code tools),
  • broader insight from instructors, industry guests, and inductive learning games, and
  • teams and projects (e.g., live team demos and feedback).

To further enrich student learning experience, project teams will be assigned an external industry mentor for a few hours per month. 

The course helps you with the Innovation Engineering framework below, which includes story development, execution while learning, innovation behaviors, and leadership.

Course Objectives

You will learn

  • To define and execute what, why, and how to build real world AI, data, and systems applications for users – working on a project in a team, including with an industry mentor
  • Computer science tools for data science
  • Relevant theory, critical thinking, and insights on AI, data, and systems

Textbook/Resources

Course Communication

Announcements will be made via Slack Data-X (INDENG 135 / 235). As students, much of your learning will be from each other. Slack can facilitate class conversations and team building, and is used in industry. 

Attendance/Participation Policy

A student shouldn't attend class if sick. In class, students must adhere to current campus directives related to COVID-19 and refusal to do so may result in the student being asked to leave.

  • Excused absence request form by 12pm PST before class: For exception cases (e.g. sickness, COVID exposure) you provide, you have the option to join class via Zoom or to skip class and submit in-class assignments later for credit. Based on excused absence requests weeks 3-4, we anticipate roughly 5 students per week will join via Zoom.
  • Starting week 5 (2/18/2022), either 1) attending class or 2) submitting the Google Form before class and receiving approval later are necessary to be eligible for credit on in-class assignments.
  • All classes are scheduled to be automatically recorded (e.g., to support exception cases such as sickness, COVID exposure) and can be found at bCourses -> Spring 2022 Data-X (INDENG 135 / 235) -> Media Gallery.

Because the course is an applied and project team course, attendance and participation are important.

Weekly Schedule and Assignments (subject to change)

The weekly schedule and assignments are meant to provide an outline of the course material and structure. However, it is not set in stone and may be modified as the semester unfolds. If substantive updates occur on the syllabus, instructors will communicate via Slack Data-X (INDENG 135 / 235) and in class so you are aware the course syllabus webpage is updated.

Classes include one or more 5-10 minute break(s) between topics.

Acronyms below: IS=Ikhlaq Sidhu, DSC=Derek S. Chan

Week Date Broader insight (mostly) Code and theory Teams and projects Due by subsequent class
Notes:

* The GSIs—Mahan Tajrobehkar and Tony Li—will hold Python foundation workshops on homework during office hours in weeks 2-3.

* If you are new to Python, please start the Code Academy free beginner course ASAP and reach out to the GSIs, as needed, in addition to the workshops.

* You can access a Google Drive folder, Spring-2022-Data-X-Students via your CalCentral berkeley.edu email, a folder where some content will be populated during the semester.

1 1/21 (55 mins) IS & DSC: Why the Data X course is important for students, set course and project expectations and grading, and cover the history of Data-X. (45 mins) IS & DSC Lecture: Intro to ML insights (55 mins) IS: Review of Python Data Handling Tools Numpy and Pandas (plus in-class submission)

(5 mins) Assignments due via Gradescope during semester (entry code is at Slack Data-X general channel)

* Python HW1 (Code Academy notebook) with reference: Walk-through video of Google Colab and Gradescope
* Read Innovation Engineering, Chapter 4: A Step-by-Step Guide for Innovative Projects (file encryption key is in that same folder). Submit a reflection — assignment instructions and scoring rubric are on Gradescope.
* Sign up for Anvil, AWS, H2O.ai, etc. accounts (10 minutes)
2 1/28 (20 mins) DSC Lecture: Intro to ML insights (skipped prior week)

(75 mins) DSC: Dataset diversity, quality, quantity (plus in-class submission)

(25 mins) IS: Demonstrate NABC (Need, Approach, Benefit, Competition) and Dataset slide from https://www.innovationengineering.space/framework-templates

(25 mins) DSC: How to work with mentors

Enrolled students who attend weeks 1-2 and complete all homework to date are matched with teams/mentors by 2/1. Remaining waitlisted students who later are enrolled will be matched with teams/mentors.

* Sunday 1/30, 8pm PST: Complete individual survey for team and mentor matching (Student-Mentor resources folder is accessible via your CalCentral berkeley.edu email)* Friday 2/4, 2pm PST: Python HW2 (Numpy) and HW3 (Pandas)
3 2/4 (15 mins) DSC: Customer discovery interviews (5 questions) (65 mins) IS: A System's View of Data Science with Prediction (in-class submission)

(60 mins) DSC: Data APIs and Web Scraping (in-class submission)

(15 mins) Team time Due 2/7/2022

* Reach out to mentor to request a first meeting

Due 2/11/2022

* Python HW4 (dataset evaluation)

Please see what is due 2/18/2022, with notice 2 weeks in advance

4 2/11 * (50 mins) DSC: Mission Impossible Game, with learning objective of fast customer validation (60 mins) IS low-code and high-code tools - including Anvil and licenses.
* Introduce tools, and demo application code more complicated than Hello World!
* (20 mins) IS review Low Tech Demo slides due in two weeks

* (20 mins) Team time

Due 2/18/2022

* Meet mentor, share agenda in advance, and document 3 learnings

* NABC (Need, Approach, Benefit, Competition) and Dataset slides 3-4 and 8  — instructions and grading rubric

* Each person conduct 3 customer discovery interviews for your project (e.g., 15 interviews per team)

* Python HW (Data API or web scraping) for your project

5 2/18 (10 minutes) Q&A

* Attendance/Participation Policy updates to further accommodate exceptions due to sickness and COVID exposure

* Brief slide on upcoming ML technical approaches not typically in data science courses but important in industry and therefore in Data-X later in the semester

 

Anvil license and H2O extended account

(110 minutes, plus 1-2 breaks)

DSC: Titanic Notebook Historical (Modules 160A-160D walkthrough of pros, cons, and insights beyond)

* H2O AI Cloud with optional tutorial

* H2O + Anvil with tutorial (in-class submission)

(20 mins) 1 team presents initial NABC + dataset slides, and customer discovery

(Remaining students submit in-class reflection on presenter team)

* Team video on each other's individual learning goal and team role, your's group definition of success

* Low Tech Demo slides

* Anvil HW for your project

6 2/25 (30 mins) IS: Review tech strategy tools on the Innovation Engineering site and slide decks you will create, and review technology strategy template due future week AWS administrative steps before class

(80 mins) DSC: Build cloud system different ways

Anvil + Hugging Face API

* Anvil + AWS SageMaker, with a 3rd-party model

(35 mins) 2 teams present low-tech demo slides

(Remaining students submit in-class reflection on presenter teams)

* Anvil + model HW for your project

* Meet mentor, share agenda in advance, and document 3 learnings from second meeting

7 3/4 (30 mins) Optional after class: Student focus group provides feedback on what hasn't vs. has helped their learning in the course to inform improvements (50 mins) IS: System's view of correlation (in-class submission)

(50 mins) DSC: Colab + build your own model

(45 mins) At start of class, 3 teams present low-tech demo slides

(Remaining students submit in-class reflection on presenter teams)

* Tech strategy template
* Read Innovation Engineering, Chapter 6, and submit individual slide reflection on "Common Strategic Errors and Story Narrative Mistakes" for your project (file encryption key is in that same folder).
* Between 3/7 and 3/18: Every student team schedules time and meets with their assigned instructor group for 30 minutes. Mentor attendance is optional.
8 3/11 (80 mins) Guest Session: "Neural Network workshop" by Mario Filho (Machine Learning Expert | Kaggle Grandmaster | Data Scientist) with in-class submission

(45 mins) DSC: Life or Death Game, with learning objective of active learning ML given limited resources (Kaggle submission)

(15 mins) Midterm exam review, team projects * Team app development in team folder
* Between 3/7 and 3/18: Every student team schedules time and meets with their assigned instructor group for 30 minutes. Industry mentor attendance is optional.
9 3/18 (50 mins) IS: Measurement for Decisions (in-class submission)

(90 mins) Individual exam: Build cloud system similar to assignments weeks 5-7

(15 mins) Final project grading rubric * Team app development and 4-minute maximum video on progress

* Submit confidential mid-semester reviews on peers and mentors (Google Form)

10 3/25 Spring Recess
11 4/1 (30 mins) Guest Lecture: "Production ML Systems, Infrastructure, Scalability, and Hidden Costs in Industry" by Michael Mui (Technical Lead at Uber AI) (50 mins) DSC: Systematic ML Optimization Game, with learning objective to build intuition and apply practices for your project and/or company (Kaggle submission) (60 mins) Teams present projects, including app development so far

(Remaining students submit in-class reflection on presenter teams)

* Team app development and 4-minute maximum video on progress
12 4/8 (65 mins) DSC: Each team sets up a station in class and conducts user tests of their app on individuals from other teams

Learning objective: Submit individual user testing template by class end, as practice to conduct team user texting of external users for the next homework

(25 mins) Team time

(45 mins) Teams present projects, including app development so far

(Remaining students submit in-class reflection on their group's presenter teams)

* Team app development and 4-minute maximum video on progress

* Team submission 5 user tests

* Between 4/11 and 4/22: Every student team schedules time and meets with their assigned instructor group for 30 minutes to understand what to improve to avoid points lost on final project. Industry mentor attendance is optional.

13 4/15 (40 mins) Guest Lecture: "Common pitfalls of entrepreneurship" by Shuo Chen (General Partner at IOVC | Faculty at UC Berkeley | Board Director)

(30 mins) IS: Common mistakes with ventures and business models, including Airbnb example (interactive exercise)

(Async material, with instructors available at class to help on projects)

(25 mins) Team time

(45 mins) Teams present projects, including app development so far

(Remaining students submit in-class reflection on their group's presenter teams)

* Between 4/11 and 4/22: Every student team schedules time and meets with their assigned instructor group for 30 minutes to understand what to improve to avoid points lost on final project. Industry mentor attendance is optional.

Recommended reading: Appendix D -> Seven common business models

 

* Due Thursday, 4/21 11:59pm:

~5-minute, 2-point assignment for "Week 14" — submit at least 1 career question that interests you for Career Guest Panelists to answer live

 

14 4/22 (105 mins) Guest Career Panel, then Guest Breakout Groups (15 mins) AWS option, shifting from prototype to beta

(5 mins) Midterm exam hidden secret

Preparation for final project presentation and submission of required items below
* App
* Slide deck
* Code repository
* News article
* Provide final project submission via Week 15 folder, write anonymous peer reviews, and co-present final project live 4/29
15 4/29 Location: Evans Hall 10

You can attend session 1 or 2 (and 4:00-4:15pm) and aren't required to attend more than that. Teams selected their respective presentation times in advance.

Session 1

* 2:10 - 4:00pm: 10 teams

Final gathering (all students)

* 4:00 - 4:15pm

Session 2

* 4:20 - 6:00pm: 9 teams

Each team has a 7-minute timed presentation, followed by 3-minute timed Q&A.

*At end the of the semester, one Data-X project team can qualify to participate in the Collider Cup, SCET's all-star showcase.

Grading

Grade Range
90-100%
B 80-89%
C 70-79%
D* 60-69%

*Graduate students need 70%+ or higher to pass the course, whereas undergraduate students need 60%+ to pass the course.

Area Percent of Grade*
Homework 31.27%
Final Project + Peer Reviews 25.09%
Class participation 21.82%
Mentor-student interaction 10.91%
Exam 10.91%

*Peer anonymous reviews: Peers are asked to score each other's contribution 0-3 (none, low, medium, high) for each of 3 areas and justify briefly in writing: Innovation / Entrepreneurship Story, Design, and Technical. Example illustration of feedback from Team Members about Team Member 1 at end of semester.

Area Team Member 2 Team Member 3 Team Member 4 Team Member 5 Average Rating
Innovation / Entrepreneurship Story 1 2 2 1 1.5
Design 0 1 1 0 0.5
Technical 2 2 3 3 2.5
% of total possible points from peers -- -- -- -- 4.5 / 9.0 = 50%
  • 51%+ of total possible points from peers (final project score stays the same)
  • 25-50% of total possible points from peers (final project score drops -4 points)
  • Less than 25% of total possible points from peers (final project score drops -8 points)

Example adjustment: If a team scores 60/65 on the final project, but a student is scored 25-50% of total possible points by peers, the student's final project score will be adjusted -4 points to 56/65.

Week Assignment Points
1 Pandas In-Class 2
2 Dataset Evaluation In-Class 4
2 Project Reflection Chapter 4 Homework 2
2 Python Homework Notebook 1 (Basics) 4
3 Data API & Web-scraping In-Class 4
3 Prediction Game In-Class 4
3 Python Homework Notebook 2 (NumPy) 2
3 Python Homework Notebook 3 (Pandas) 4
4 Anvil (individual) 4
4 Dataset Evaluation Homework 4
4 Mission Impossible (team) 5
5 Anvil + H2O In-class Submission 4
5 Data API & Web-scraping Homework 4
5 Customer Discovery Interviews 4
5 NABC + Dataset Slides 4
5 Student-Mentor Interaction (First Email & Meeting) 3
5-7 Reflections on the presenter team(s) 6
6 Model + Anvil In-class Submission 3
6 Anvil Homework 4
6 Low-Tech Demo Slides 4
6 Team Video (hyperlink submission) 4
7 Correlation In-Class 4
7 Model & Anvil Homework 4
7 Model In-Class 4
7-15 Student-Mentor Interaction (2nd Meeting) 3
7-15 Student-Mentor Interaction (3rd Meeting) 3
7-15 Student-Mentor Interaction (4th Meeting) 3
7-15 Student-Mentor Interaction (5th Meeting) 3
8 Active Learning Kaggle Competition 4
8 Reflection on Common Strategic Errors. . . 4
8 Tech Strategy Template 4
9 In-Class Measures Game 2
9 Midterm Exam 30
9 App development (place in folder) 2
11 Kaggle Systematic ML in-class 2
11-14 Reflections on the presenter team(s) 8
12 In-Class User Testing (Individual) 2
13 User Testing Homework (Team) 6
11 App development 6
12 App development 6
13 App development 6
13 Attendance in place of FFT 2
14 Questions for panelists (written before) 2
15 Mentor interaction 15
15 Final project 65
15 Peer reviews 4
15 Students' choice voting for best project team 1
Total 274
Bonus points: First team to present week 5 2
Bonus points: Neural Network notebook 2
Bonus points: Midterm exam extra question 1
Bonus points: Mid-semester peer review 2
Bonus points: Kaggle top-5 finish week 8 1
Bonus points: Kaggle top-10 finish week 11 1
Bonus points: Attend and support Collider Cup team 1

 

Late Policy

Each student has 7 late days total during the semester to submit homework after the deadlines, without points lost due to late submission. If a student submits 1 hour or 23 hours after the deadline, that counts as 1 late day used.

If a student misses class but notifies GSIs and copies Faculty with an excuse before the class (e.g., sickness), and the student’s reason is accepted before/after, the student still can submit in-class work.

The final project should be submitted on time due to live presentations and grading timeline at the end of the semester.

Students who have DSP accommodations can receive additional time with deadlines. Please contact instructors as soon as possible about your need for accommodations.

Course Evaluations

At the end of the term, students will be asked to fill out an evaluation to give feedback about the course on what hasn’t and/or has helped their learning experiences. SCET values and appreciates student responses, which are used to better understand and improve our courses. Students are strongly encouraged to submit the evaluations. 

Mid-Semester Feedback

A student focus group (optional attendance) will be held mid-semester right after class hours for student feedback aloud on the course.

Scheduling Conflicts

Please notify GSIs and cc Faculty in writing as soon as possible about any known or potential extracurricular conflicts. We will try our best to help you with making accommodations, but cannot guarantee them in all cases.

Student Code of Conduct & Academic Integrity

Berkeley honor code: Everyone in this class is expected to adhere to this code: “As a member of the UC Berkeley community, I act with honesty, integrity, and respect for others.”

Student Conduct: Ethical conduct is of utmost importance in your education and career. The instructors, the College of Engineering, and U.C. Berkeley are responsible for supporting you by enforcing all students’ compliance with the Code of Student Conduct and the policies listed in the CoE Student Guide. The Center for Student Conduct is set up to support you when you have been affected by actions that may violate these community rules. This includes an organized and transparent process, student participation in the process, mechanisms for appeals, and other mechanisms to protect fairness (https://sa.berkeley.edu/conduct).

Academic Integrity: Any assignment submitted by you and that bears your name is presumed to be your own original work that has not previously been submitted for credit in another course unless you obtain prior written approval to do so from your instructor. In all of your assignments, you may use words or ideas written by other individuals, but only with proper attribution. To copy text or ideas from another source without appropriate reference is plagiarism and will result in a failing grade for your assignment and usually further disciplinary action. For additional information on plagiarism, self-plagiarism, and how to avoid it, see the Berkeley Library website.

If you are not clear about the expectations for completing an assignment or taking a test or examination, be sure to seek clarification from your instructor beforehand. Anyone caught committing academic misconduct will be reported to the University Office of Student Conduct. Potential consequences of cheating and academic dishonesty may include a formal discipline file, probation, dismissal from the University, or other disciplinary actions. 

Inclusion: We are committed to creating a learning environment welcoming of all students. To do so, we intend to support a diversity of perspectives and experiences and respect each others’ identities and backgrounds (including race/ethnicity, nationality, gender identity, socioeconomic class, sexual orientation, language, religion, ability, etc.). To help accomplish this:

  • If you have a name and/or set of pronouns that differ from your legal name, designate a preferred name for use in the classroom at: https://registrar.berkeley.edu/academic-records/your-name-records-rosters.
  • If you feel like your performance in the class is being impacted by your experiences outside of class (e.g., family matters, current events), please don’t hesitate to come and talk with the instructor(s).  We want to be resources for you.
  • We are all in the process of learning how to respect and include diverse perspectives and identities. Please take care of yourself and those around you as we work through the challenging but important learning process.
  • As a participant in this class, recognize that you can be proactive about making other students feel included and respected.  

Student Accommodations

We honor and respect the different learning needs of our students, and are committed to ensuring you have the resources you need to succeed in our class.  If you need accommodations for any reason (e.g. religious observance, health concerns, insufficient resources, etc.) please discuss with your instructor or academic advisor how to best support you.  We will respect your privacy under state and Federal laws, and you will not be asked to share more than you are comfortable sharing.  The disabled student program is a related resource, listed below. UC Berkeley is committed to creating a learning environment that meets the needs of its diverse student body. If you anticipate or experience any barriers to learning in this course, please feel welcome to discuss your concerns with me.

If you have a disability, or think you may have a disability, you can work with the Disabled Students' Program (DSP) to request an official accommodation. The Disabled Students' Program (DSP) is the campus office responsible for authorizing disability-related academic accommodations, in cooperation with the students themselves and their instructors. You can find more information about DSP, including contact information and the application process here: dsp.berkeley.edu. If you have already been approved for accommodations through DSP, please meet with me so we can develop an implementation plan together.

Students who need academic accommodations or have questions about their accommodations should contact DSP, located at 260 César Chávez Student Center. Students may call 642-0518 (voice), 642-6376 (TTY), or e-mail dsp@berkelely.edu.

Prevention of Harassment and Discrimination

The University is committed to creating and maintaining a community dedicated to the advancement, application and transmission of knowledge and creative endeavors through academic excellence, where all individuals who participate in University programs and activities can work and learn together in an atmosphere free of discrimination, harassment, exploitation, or intimidation. For more information on related policies, resources and how to report an incident, see the Office for the Prevention of Harassment and Discrimination (OPHD) website

Safety and Emergency Preparedness/Evacuation Procedures

As class activities may keep you on campus at night, check out the Cal’s Night Safety Services website for details on the University’s comprehensive free night safety services. See the Office of Emergency Management website for details on Emergency Preparedness/Evacuation Procedures. The UC Berkeley Police Department website also has information regarding safety on campus. Dial 510-642-3333 or use a Blue Light emergency phone if you need help.

Grievances

If you have a problem with this class, you should seek to resolve the grievance concerning a grade or academic practice by speaking first with the instructor. Then, if necessary, take your case to the SCET Chief Learning Officer, SCET Faculty Director, IEOR Department Chair, and to the College of Engineering Dean, in that order. Additional resources can be found on the Student Advocate’s Office website and the Ombuds Office for Students website.

SCET Certificate in Entrepreneurship & Technology

This class can be used towards requirements to earn the SCET Certificate in Entrepreneurship & Technology. For details on the certificate requirements and other opportunities to engage with the Center, see the SCET website

Support during Remote Learning: 

We understand that your specific situation may present challenges to class participation. Please contact the instructors if you would like to discuss these and co-develop strategies for engaging with the course. 

The Student Technology Equity Program (STEP) is available to help access a laptop, Wi-Fi hotspot, and other peripherals (https://technology.berkeley.edu/STEP).

You will be alerted as to when synchronous sessions are about to be recorded. If you prefer not to be recorded, you may turn your video and microphone off.

Please set your Zoom name to be the name you would like the instructors to call you. You may optionally include your personal pronouns. 

Please set your Zoom picture to an appropriate profile picture of you to foster a sense of community and enhance interactions. If you are not comfortable using an image of yourself, you may use an appropriate picture of an avatar.

We encourage participating with your video on to foster a sense of community and enhance interactions. However, we understand that some students are not comfortable with video or may not be able to participate by video.

Additional Resources

See the Student Affairs website for more information on campus and community resources.

Center for Access to Engineering Excellence (CAEE)                           

The Center for Access to Engineering Excellence (227 Bechtel Engineering Center;

https://engineering.berkeley.edu/student-services/academic-support) is an inclusive center that offers study spaces, nutritious snacks, and tutoring in >50 courses for Berkeley engineers and other majors across campus.  The Center also offers a wide range of professional development, leadership, and wellness programs, and loans iclickers, laptops, and professional attire for interviews.  

Counseling and Psychological Services       

University Health Services Counseling and Psychological Services staff are available to you at the Tang Center (http://uhs.berkeley.edu; 2222 Bancroft Way; 510-642-9494) and in the College of Engineering (https://engineering.berkeley.edu/students/advising-counseling/counseling/; 241 Bechtel Engineering Center), and provide confidential assistance to students managing problems that can emerge from illness such as financial, academic, legal, family concerns, and more. Long wait times at the Tang Center in the past led to a significant expansion to include a 24/7 counseling line at (855) 817-5667.  This line will connect you with help in a very short time-frame.  Short-term help is also available from the Alameda County Crisis hotline: 800-309-2131.  If you or someone you know is experiencing an emergency that puts their health at risk, please call 911.  

The Care Line (PATH to Care Center)

The Care Line (510-643-2005; https://care.berkeley.edu/care-line/) is a 24/7, confidential, free, campus-based resource for urgent support around sexual assault, sexual harassment, interpersonal violence, stalking, and invasion of sexual privacy. The Care Line will connect you with a confidential advocate for trauma-informed crisis support including time-sensitive information, securing urgent safety resources, and accompaniment to medical care or reporting.

Ombudsperson for Students                                            

The Ombudsperson for Students (102 Sproul Hall; 642-5754; http://students.berkeley.edu/Ombuds)  provides a confidential service for students involved in a University-related problem (academic or administrative), acting as a neutral complaint resolver and not as an advocate for any of the parties involved in a dispute. The Ombudsman can provide information on policies and procedures affecting students, facilitate students' contact with services able to assist in resolving the problem, and assist students in complaints concerning improper application of University policies or procedures. All matters referred to this office are held in strict confidence. The only exceptions, at the sole discretion of the Ombudsman, are cases where there appears to be imminent threat of serious harm.

UC Berkeley Food Pantry

The UC Berkeley Food Pantry (#68 Martin Luther King Student Union; https://pantry.berkeley.edu) aims to reduce food insecurity among students and staff at UC Berkeley, especially the lack of nutritious food. Students and staff can visit the pantry as many times as they need and take as much as they need while being mindful that it is a shared resource. The pantry operates on a self-assessed need basis; there are no eligibility requirements.  The pantry is not for students and staff who need supplemental snacking food, but rather, core food support.

Disclaimer: Syllabus/Schedule are subject to change.

References

Innovation Engineering Textbook. Data-X students can access required sections for free. Encryption password is at Slack Data-X (INDENG 135 / 235), pinned in the #general channel.

Navigator Tool template at Innovation Engineering website -> Google Slides to reinforce inductive learning

Low Tech Demo template at Innovation Engineering website -> Google Slides

Directory of Advisors and Industry Experts for Data-X

The Data-X course and project brings together students, technical experts, start-up companies, and executives.  Each brings a different perspective to data, algorithms, and scale. Please refer to the People webpage.