代做MS764 · Fall 2024 Technology Labs代写留学生Python语言

日期：2024-09-12 05:39

Technology Labs

MS764 · Fall 2024

Tech Labs is a comprehensive journey that equips students with the skills and knowledge needed to create things from scratch.

During the Fall, Tech Labs is divided into three distinct phases – Ideation, Design, and Experimentation. In the Ideation phase, students learn to generate and refine ideas while mastering essential collaboration tools. The Design phase focuses on translating concepts into visual designs and crafting brand identities, leveraging design tools and techniques. The Experimentation phase uses LLMs to conduct advanced product experiments.

The course is entirely hands-on. Students are expected to bring their laptops and be fully engaged. Tech Labs follows a workshop format, where students will spend the majority of their time exploring tools and figuring out ways to integrate them into their workflows. This course also goes beyond software. By reading some of the most canonical texts in the technology space, students will begin to develop the “language” of technology needed to navigate the real world.

During the Spring, Tech Labs will progress to cover Development, Marketing and Pitching. This course aims to encompass the entire lifecycle of “ideas” and the tools and methods needed to bring them to life. AI will be heavily used to make us faster and more technical.

Please note that this syllabus is by no means definitive and is subject to change. Tech Labs follows the latest trends in technology and any tools that go to market will be added.

Prerequisites

A passion for learning and trying new things.

Learning Objectives

Part	Objectives	Tools
Part 1 — Ideation Where do good ideas come from? How do we create an “inspiration environment”?	Understand the importance of creative thinking and ideation in business and technology. Develop the ability to generate, refine, and validate innovative ideas both individually and in teams. Learn effective personal management and collaboration techniques to foster a productive and inspiring work environment.	Slack, FigJam, Microsoft Teams, Notion, JIRA, Trello, LinkedIn, ChatGPT, Perplexity, Claude
Part 2 — Design & User Experience How are solutions translated into effective visual designs? How do you craft compelling product stories?	Master design principles and apply them to create engaging and intuitive user experiences. Develop skills in crafting visual designs and brand identities. Learn advanced techniques for creating persuasive presentations that convey product stories effectively. Explore the use of tools for web and mobile app design, emphasizing user-centric approaches.	Figma, Canva, Adobe Creative Suite, Framer, Webflow, Beautiful.ai, PowerPoint
Part 3 — Experimentation How do we use data and experimentation to optimize products and validate ideas?	Learn to conduct advanced product experiments using data-driven approaches. Develop skills in exploratory data analysis (EDA), hypothesis testing, and regression analysis to make informed decisions. Understand clustering techniques and factor analysis to segment data meaningfully. Utilize statistical methods and machine learning techniques to enhance product development and business strategies.	Python, Qualtrics, Google Forms, Excel, Statsig, Maze, Sprig, ChatGPT, Perplexity, Claude

Evaluation and Course Policies

Grading

The assignment of grades is based on Preparation, Participation, Application and Contribution. Students must meet the standard

Grades for this course will be assigned based on performance in four areas:

Area	Fail	Pass	High Pass	Honors
Preparation	Meet standard in only one area	Meet standard in two areas but not all three	Meet standard in all three areas	Meet standard in all three areas
Participation
Application
Contribution	Not considered	Not considered	Does not contribute significantly	Make significant contribution

Assignment	Details	Grade
Exams – 45%
Midterm Project	Product presentation	20%
Final Exam	All materials covered in the course up to and including the final session.	25%
Format	Exams follow a three section structure. The first section is matching quotes to the relevant readings. The second is defining software elements and technology lingo. And the third is short answer questions explaining technical concepts
Class Application Assignments and Participation – 55%
Readings	Complete the assigned readings and post forum responses.	5%
Software Demo Presentation	Complete and present your software demo presentation	10%
Participation	Thoughtful participation in class discussions and exercises	40%
Bonuses	Bonus points are distributed for exceptional participation
Total			100%

Attendance

Attendance Policy: 

Please see the MiM Student Handbook in the Blackboard MiM Dashboard regarding attendance. Satisfactory class contributions require attendance at every session of the course; preparation of all materials for every session; and active, quality participation in class discussions. Simply attending class, however, does not constitute a positive contribution to class and will not yield high class contribution scores. Students who miss one (1) or more sessions risk failing the course. Assignments are always due at the beginning of class on their due date, even if students are unable to attend class that day.

MiM students are allowed one unexcused absence per course per module. Students must request approval in advance from the instructor for excused absence from any class, workshop, or career session. For absences for which this is not possible (e.g., medical or family emergency), students must provide documentation of the reason for the absence (e.g., a physician’s note). Regardless of whether an absence is excused, each student must submit makeup work for the class, workshop, or career seminar, according to the recommendation of the instructor. Exceptions can be made in the case of emergencies, at the discretion of the instructor.  

Classes, workshops, guest lectures, client visits, and other program activities may be scheduled between the hours of 8:00am and 5:00pm, Monday-Friday. Internships and part-time jobs should not be scheduled during this time. Students are expected to attend all required program activities.

Attendance will be taken at all classes and program activities. Attendance records will be reviewed in periodic meetings of the PDC. If the PDC determines that multiple excused or informed absences are negatively affecting a student’s learning, they will consider suitable action, which may include remedial work, academic probation or withdrawal from the program.

Tardiness: Students are expected to be on time for classes, workshops, and meetings. Arriving late to classes, workshops, and lectures constitutes one-half of an absence. Please plan ahead: plan to arrive at class 5-10 minutes early to avoid unexpected incidents.

Attendance Impact on Grades: For each module, a student receives one excused absence in each course. If a student does not provide ample notification of an absence and misses two classes in each course, then the students will be marked one grade lower. For example, if the student was normally showing “High Pass” level work but missed two classes, the grade would revert to a “Pass.” If a student does not attend three or more classes in a course, the student’s grade would go down two levels (i.e. “High Pass” to “Low Pass”) and be at risk of failing the course.

In all cases, students who miss a class must make up all classwork and homework and submit the materials to the professor of the course within two weeks of the student’s absence.

Scheduling Job/Internship Interviews: Generally, MiM classes are scheduled in the mornings. Thus, you should schedule interviews for jobs and internships in the afternoons. Hiring managers recognize that you are students and will generally be willing to arrange interviews around classes at your request (which you should!). It is understood that sometimes an interview may conflict with class time (for example, with international companies).  When there is no alternative to scheduling an interview during class time, students should notify the instructor in advance and arrange for any makeup work.

Course Policies

Academic Integrity: Students are expected to abide by the Questrom School’s Academic Conduct Code (https://www.bu.edu/academics/policies/academic-conduct-code/).

All graded assignments, both written and verbal (except those otherwise specified), are strictly individual efforts. While we collaborate on work in class, graded assignments outside of class are to be completed entirely independently. Please seek assistance directly from your instructor or from a Teaching Assistant if you would like to discuss your assignment outside of class.

Academic Accommodations: In keeping with University policy, any student with a disability who needs or thinks they need academic accommodations must contact the Office of Disability Services (ODS). Students can call ODS at 617-353-3658, or visit their website (http://www.bu.edu/disability/) to arrange a confidential appointment with a Disability Services staff member. Accommodation letters must be delivered to your instructor in a timely fashion (not later than two weeks before any major examination). Please note that accommodations will not be delivered absent an official letter of accommodation.

Professional Conduct: Laptops will be used in class for casework and exercises. They may not be used for other purposes, and should be closed during lecture. Texting, emailing, or web surfing are prohibited during class. Phones may not be used in class at any time. You are expected to arrive on time and stay for the duration of class, leaving only during scheduled breaks.

Sexual Misconduct/Title IX Policy: The Questrom School of Business is committed to fostering a safe learning environment for all members of the community and preventing sexual misconduct. All forms of sexual misconduct, including rape, acquaintance rape, sexual assault, domestic and dating violence, stalking, and sexual harassment are violations of Boston University’s policies, whether they happen on campus or off campus.

Title IX of the Education Amendments of 1972 is a federal civil rights law that prohibits sex-based discrimination in federally funded education programs and activities. This law makes it clear that violence and harassment based on sex and gender is a Civil Rights offense subject to the same kinds of accountability and the same kinds of support applied to offenses against other protected categories such as race, national origin, etc. If you or someone you know has been harassed or assaulted, you can find the appropriate resources at http://www.bu.edu/safety/sexual-misconduct/.

Diversity and Inclusion: In developing the materials and assignments for this course, we have aimed to be thoughtful about how identity and culture impact the course content. During the semester we may discuss content that will inspire debate, different opinions, and shared experiences.  Learning can only happen in a community that is respectful and inclusive. All members of class will conduct themselves in a professional manner. Remember, you can disagree with the idea and still respect the person.

We invite you to share your personal experiences and perspective related to the course content; we can learn from each other. If there are topics or conversations that you feel would benefit from incorporation of social context, a differing perspective, or Questrom’s Center for Diversity, Equity, and Inclusion, please inform. your instructor and we will explore resources and opportunities for us to engage a wide variety of perspectives in our classroom.

Artificial Intelligence: AI is an unprecedented opportunity for students to become more effective and productive. We believe in:

● Embracing AI broadly and responsibly

● Treating AI as a tool for work not a replacement for it

● Adapting and adjusting as AI evolves

Please refer to the MiM AI Student Guide in the MiM Dashboard for an outline of encouraged and responsible use.

Norms: The class will be conducted by the norms of a well-run professional environment. Failure to comply with these norms on an ongoing basis is a reason for dismissal from the program. Our norms:

● Be Professional – Be respectful to everyone.  Be on time. Don’t leave class unless it is absolutely necessary. Listen and seek to understand those around you.

● Prepare – Come to class ready to contribute and engage with other students. Be prepared to share the insights you’ve generated from your assignments with the class.

● Participate – Actively engage in class and team discussions. Consistently make substantial and relevant contributions to your project teams.

● Be Present – Show respect to each other by being attentive. Distractions such as cell phones and computers should be shut off and put away unless your professor asks for their use.

● Be Principled – Act with integrity, recognizing the thinking of others without ever allowing their thinking to be misinterpreted as your own.

Participate:

Attendance - Please see the MiM Student Handbook in the Blackboard MIM Dashboard regarding attendance. Satisfactory class participation requires attendance at every session of the course; preparation of all materials for every session; and active, quality participation in class discussions. Simply attending class is essential.  However, it does not constitute a positive contribution to class and will not yield high class contribution scores. Students who miss one (1) or more sessions risk failing the course.

Be Present:

Students are expected to be on time for class and present the entire time. Arriving late, departing early, leaving during class (even for an interview), or taking personal breaks when your team is working together  shows disrespect to your fellow students and faculty. Please plan ahead: arrive at class early and avoid scheduling interviews that overlap with the course.

Be Principled:

Our course can only succeed if every student represents honestly their own knowledge and ideas. Academic misconduct is conduct by which a student misrepresents their academic accomplishments or impedes other students’ opportunities of being judged fairly for their academic work. Knowingly allowing others to represent your work as their own is as serious an offense as submitting another’s work as your own. The penalties for violating the code are severe.

It is the responsibility of every student to be aware of the Academic Conduct Code’s contents and to abide by its provisions. Please review the Academic integrity section of the Student Handbook for a description of the Academic code.

Detailed Class Schedule

*Please see Blackboard for a list of readings for the week

Class 1: Intros, Syllabus and Setting the Stage
Objective:
Introduce the course, establish expectations, and generate excitement about the next phase of business and technology. The session will also focus on setting up effective workflows and tools that will be used throughout the course.

● Introduction to the course and its objectives

● Overview of the syllabus and expectations

● Setting expectations for participation, assignments, and project work

● Teaching and learning style.

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Class 2: Personal Management and Ideation
Objective:
Dive into the tools and best practices for ideation, research effective project management, and time management.

● Importance of technology in business education.

● Setting the stage for an effective career and learning trajectory.

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Class 3: Design Principles, History & Brands - "The Foundation of Great Design"
Objective:
Introduce students to the fundamental principles of design and explore the history of design movements that have shaped the way we approach product and user experience design today.

Topics:

● Design principles such as balance, contrast, alignment, repetition, proximity

● History of design movements such as Bauhaus, Minimalism, Neobrutalism

● Typographic principles such as serifs, kerning, hierarchy

● Color theory principles such as complementarity and analogous colors

● The importance of responsive design

● Logo, types, messaging, and brand books

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Class 4: Design for Web - "Crafting Product Experiences"
Objective:
Focus on designing for the web and how to create interfaces that are intuitive and appealing.

Topics:

● Web design principles

● Mobile-first design

● Touch targets

● Navigation patterns

● Variables, components

● Native UI

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Class 5: Design for iOS - "Crafting Product Experiences for Mobile"
Objective:
Focus on the specifics of designing for iOS and how to create interfaces that are intuitive and appealing for mobile apps.

Topics:

● iOS human interface

● Mobile-first design

● Touch targets

● Navigation patterns

● Variables, components

● Native UI

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Class 6: Crafting Award-Winning Presentations - "The Art of Persuasion Through Design"
Objective:
Teach students how to create compelling, visually stunning presentations that not only convey information but also persuade and engage audiences.

Topics:

● Visual hierarchy

● Storytelling in design

● Minimalism

● Icons and imagery

● Font pairing

● Color psychology

● Transitions and Animations

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Class 7: Midterm Project Session - "Product Presentation"
Objective:
Teams will present product ideas they have designed in Figma.

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Class 8: Understanding and Mastering Interactions with LLMs - "Communicating with AI"
Objective:
Understand the transformative power of AI. This class serves as the foundation for effectively interacting with LLMs, emphasizing prompt design, best practices, and real-world applications.

Topics:

● AI, ML, LLMs, and Tokenization

● Transformers, Neural Networks, Training Data, Parameters

● Inference, Evaluation, Few-Shot Learning

● Ethics in AI

● Prompt engineering, iterative prompting

● Few-shot vs zero-shot

● Temperature, Token, Context window, Stop sequence, Ambiguity

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Class 9: EDA and Hypothesis Testing
Objective:
Combine the skills of data analysis, cleaning, exploratory data analysis (EDA), and hypothesis testing to uncover patterns, make predictions, and test hypotheses.

Topics:

● Data cleaning, null values

● Methods for handling missing data

● Outliers and skewing

● EDA

● Various visualization approaches

● Hypothesis testing, null hypothesis

● P-value

● A/B testing

● ANOVA

● Pandas, NumPy, Matplotlib, Seaborn

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Class 10: Regression Analysis
Objective:
Learn how to apply regression analysis to predict outcomes and identify relationships between variables.

Topics:

● Simple regression

● Multiple regression

● R-squared

● Interpreting coefficient

● P-value

● statsmodels, scikit-learn, stargazer

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Class 11: Factor Analysis and Clustering
Objective:
Introduce students to factor analysis and clustering techniques, enabling them to group variables and data points into meaningful segments.

Topics:

● Factor analysis

● Eigenvalues

● Clustering

● K-means

● Interpretation of clustering regression

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Class 12 and 13: Go-To-Market (GTM) Strategy - "Bringing Products to Market"
Objective:
Develop and execute a Go-To-Market (GTM) strategy, covering the essential tools and techniques needed to successfully launch a product.

Topics:

● GTM, segmentation, positioning

● Value proposition, MVP

● North star metrics like CAC, LTV

● Marketing channels

● User journeys, funnels

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Class 14: Final Exam Day