DS-100 Syllabus

Course Description

In this course we will introduce you to three fundamental perspectives for reasoning with data: critical thinking, inferential thinking, and computational thinking. All three of these perspectives are integral to the data-driven research processes that are common in data science, thus allowing you to learn and practice how you can make and test hypotheses, and construct or deconstruct arguments that are rooted in data.

We will first use public data sets (both curated or scraped) focused on socially-relevant themes (e.g., public health, education, and environment) to model and understand real-world phenomena. We will focus on using model summarization, data visualization, and model-based simulations to interpret and communicate our understanding of these real-world phenomena as well as the potential for bringing these derived models to bear on real-world questions and applications (e.g., comparing different policies).

Particular emphasis will be placed on exposing you to and developing your appreciation for the principles underlying data mining and machine learning methods, including regression, classification and clustering, and the statistical concepts of measurement error and prediction. We will teach you critical concepts and skills in computer programming (Python), linear regression, and statistical inference. We will also delve into dilemmas surrounding data analysis such as balancing individual privacy and social utility.

This course uses a learning model where students use large language models (LLMs), commonly referred to as AI or GenAI, as learning partners for concept exploration while developing authentic data science competency through guided practice and individual verification.

Course Structure & Expectations

This course follows a flipped model. You explore concepts before class using GenAI tools, then class time is used for verification, application, and deeper expert engagement.

• Lectures (T/W/R 1:00–3:30 PM): Expert sessions that build on your pre-class exploration. Each 2.5-hour block includes practice and activities.
• Assessments (T 4:00–5:00 PM): Individual verification of understanding. Format varies by week. Default is no-AI or limited-AI.
• Q&A sessions (R 4:00–5:00 PM, optional): Open attendance. Good for questions, starting the week’s GAIE, or getting a head start before the weekend.

GenAI Exploration (GAIE) assignments are released weekly and serve as your preparation for the following week’s lectures.

Hub Learning Outcomes

Social Inquiry I (SO1)

Learning Outcome #1: Students will identify and apply major concepts used in the social sciences to explain individual and collective human behavior including, for example, the workings of social groups, institutions, networks, and the role of the individual in them.

We will employ hands-on analysis of real-world datasets, including curated economic data, data scraped from digital collections, social networks, and more. In this context, the course will expose you to social and legal issues surrounding data analysis, including issues of privacy and data ownership, and will highlight the many ways in which data could be used (or misused).

In this course we will be looking at data from multiple vantage points. For example, by looking at data characterizing COVID-19 infections, hospitalizations, deaths, vaccinations, we will be able to differentiate between phenomena (e.g., correlations) identified at the macro scale (federal and state) versus those identified at the micro scale (cities and communities) and draw conclusions or make statements supported with evidence from data (e.g., impact of socioeconomic background).

We will encourage you to apply what you learn on societally-relevant case studies of your choice (e.g., case studies similar to those presented at callingbullshit.org) by applying the tools and techniques covered in class to analyze data sets in order to support or debunk hypotheses.

Digital/Multimedia Expression (DME)

Learning Outcome #1: Students will be able to craft and deliver responsible, considered, and well-structured arguments using media and modes of expression appropriate to the situation.

We will use real data to understand relationships and patterns while also introducing critical concepts and skills in computer programming and statistical inference. In order to build your arguments, you will use multimodal data analysis and visualization in ways that are appropriate to the task at hand. This will include:

• Generation and interpretation of scatter plots, histograms, bar charts, and box plots
• Making predictions using simple regression
• Characterizing data quality and communicating associated uncertainties
• Establishing confidence in reproducible predictions
• Reaching defensible conclusions about real-world questions

These skills will be taught and evaluated through learning logs and in-class activities, as well as demonstrations and projects.

Learning Outcome #2: Students will be able to demonstrate an understanding of the capabilities of various communication technologies and be able to use these technologies ethically and effectively.

As part of DS-100, we will introduce you to multiple forms of data visualization and presentation, including histograms, scatterplots, word clouds, heat maps, infographics, etc. Each one of these forms of communication can be particularly effective (or even misleading) in certain settings. For example, the choice of different scales (e.g., absolute vs relative change) on an axis could over or under-emphasize particular conclusions from the data.

Given the multitude of sources from which the data is collected, you will be exposed to proper ways of handling the data. For example, to preserve the privacy of individuals or communities in a large data set, and be introduced to the use of randomization techniques (blurring the data). As another example, to deal with the scale of data it may be necessary to only consider/analyze a subset of all observations. In that context, we will introduce you to various ways in which selection bias may influence conclusions you may be able to reach with implications on reproducibility.

Learning Outcome #3: Students will be able to demonstrate an understanding of the fundamentals of visual communication, such as principles governing design, time-based and interactive media, and the audio-visual representation of qualitative and quantitative data.

We will teach you how to use Python to organize and manipulate data in tables, and to visualize data effectively. Furthermore, you will be able to use computation to help your data tell a story through fundamental principles and methods of data visualization. The data used throughout this course will include longitudinal data (time series over long-time scales), geospatial data (data overlaid on apps), or both. These modalities will offer you different ways to interact with the data.

In all of the above learning outcomes, we note that some of your work products will be in the form of multimedia reports, in which data visualization is coupled with narratives or video clips.

Research and Information Literacy (RIL)

We will teach you critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. In class, you will work in small teams, framing a question or testing a hypothesis using a set of potential data sources. The key phases of that process are the exploration and identification of relevant data sets, the formulation and reformulation of the questions based on the identified data, the development of a set of data processing/analytics steps leading to an answer, and the interpretation and/or validation of the answer. To a large extent, going through these phases mirrors the six steps of the data science research process.

Books & Tools

• Programming Environment: Python with industry-standard libraries (numpy, pandas). We will help set up your environment so everyone can access the same tools.

Course Platforms

• Blackboard Ultra: Authoritative source for the weekly schedule, office hours, policies, and course resources. When in doubt, Blackboard is correct. Check it at the start of each week.
• Gradescope: Submit and receive feedback on GAIEs, in-class work, and projects; also for regrade requests. However, the grades on Blackboard are authoritative.
• Piazza: Real-time communication for urgent notices, canceled office hours, interview sign-ups, and quick questions. Watch Piazza during the week. Do not email the teaching team; post on Piazza instead.
• TerrierGPT: Our GenAI exploration platform (terriergpt.bu.edu) with access to OpenAI, Anthropic, Amazon, and open-source models. Provided credits should be sufficient for the six-week term. If not, budget up to $40 for a commercial subscription. If this would be a hardship, contact the instructor and alternatives will be arranged. No student is penalized for inability to pay.

Assignments & Grading

Grade Distribution

How Grading Works

• GAIEs (completion-only): Autograded for completeness as Complete or Missing. No drops apply. Submit each GAIE by its Tuesday 8:00 AM deadline. That is when the following lecture uses it. Late GAIEs submitted before the last day of classes still count toward completion, but the preparation value for that week is gone. All 7 GAIEs must be submitted by the last day of classes to receive a final grade; missing GAIEs after that date may result in an Incomplete.
• Assessments & in-class activities (rubric/points): Graded with points/rubrics. This category includes both the weekly assessment blocks and any graded in-lecture activities. The lowest 10% of scores in this category are dropped automatically.
• Projects (rubric/points): Graded on clarity, accuracy, reproducibility, ethics, and communication. Spelling, grammar, and adherence to the course style guide are part of the communication score.
• Practice vs. verification: GAIEs are practice and preparation; assessments are individual verification of understanding. Accordingly, GenAI use is encouraged for GAIEs but prohibited during assessments and oral verification.

Projects

Mini-project (10% of course grade)

• Pairs formed by the instructor.
• Released Jun 4; due Jun 11 8:00 AM.
• Oral interviews: Jun 11 4:00–5:00 PM.
• No late submissions accepted.

Final project (30% of course grade)

• Team-based in small groups formed by course staff.
• Team deliverables (percent of final project grade):
  • Proposal: 15%
  • Final report: 40%
  • Final code: 45%
• Individual multipliers apply to the team project score:
  • Oral interview multiplier: below expectations, meets expectations, or exceeds expectations.
    • Meets: contributed meaningfully to the work and understands all aspects of the code and report.
    • Exceeds: demonstrates deep understanding.
    • Below: uncomfortable with their own work, GenAI-assisted work, or teammate work.
  • Peer review multiplier: below expectations or meets expectations.
    • Peer review uses a 1–5 scale; 4+ = meets expectations.
  • Final project score = team score × oral multiplier × peer multiplier.
• Released Jun 17; proposal due Jun 23 8:00 AM.
• Delivery due: Jun 25 2:00 PM.
• Oral interviews: Jun 25 4:00–5:00 PM.
• No late submissions accepted.

Timing & Late Work

• GAIEs: Due Tuesday 8:00 AM each week. Late submissions before the last day of classes still count for completion; the week’s preparation value is lost. No drops apply.
• Assessments & in-class activities: Due at the stated deadline. Lowest 10% dropped automatically.
• Projects (mini + final): Due at the stated deadline. No late submissions accepted. The compressed term does not allow for extensions.
• Oral interviews: Conducted during the course 4–5 PM slots. Missing your oral interview results in an automatic incomplete or failing grade on that project, regardless of the written work.
• Excused absences: Submit work within 3 days of the original deadline; grade is held until you complete an oral verification at the next Thursday Q&A session. Contact the instructor promptly.
• Late adds: Auto 5-day extension from enrollment date. Contact the instructor.
• Completion requirement: To receive a final grade, all GAIEs must be submitted by the last day of classes, all graded work must be submitted by the end of the instructional period, and both oral interviews must be completed. Missing any of these components may result in an Incomplete, regardless of performance on other work.

How to Succeed

1. Do your GAIEs before class. The lecture assumes you’ve explored the material; it will move fast if you haven’t.
2. Engage actively in class. Our model depends on in-class verification. Attendance isn’t graded separately; evidence comes from submitted artifacts.
3. Come prepared for Tuesday assessments. You’ll demonstrate your understanding individually. Work through your GAIE carefully beforehand.
4. Show up to Thursday Q&A when you have questions. The session is optional but the best place to get unstuck, ask about the current GAIE, or get a head start before the weekend. Piazza is for quick written questions; Thursday Q&A is for anything that benefits from a real conversation.
5. Watch Piazza. Urgent notices, office hour changes, and time-sensitive information go there first.
6. Build community. Use ERC tutoring, writing support, and peer study groups (outside individual assessments).

Integrity & GenAI Policy

We follow BU’s Academic Conduct Code and the CDS GAIA Policy, adapted for this course. Discussing ideas is encouraged; copying is not. Always cite collaborators, data sources, and libraries. Always attribute AI involvement using the course AI Attribution model (see the style guide).

GenAI Zones

• Green (Encouraged): GAIEs, concept exploration, debugging help, project brainstorming.
• Yellow (Allowed with care): Project execution (analysis must be student-driven), writing drafts, study groups.
• Red (Prohibited): In-class assessments, oral verification interviews, or any work explicitly marked “individual verification.”

Violations (especially GenAI use in the Red Zone) will be treated as academic misconduct.

Tone of conduct: Disagreement is welcome; disrespect is not. We aim to model the community we want for our University and industry.

Support & Accessibility

• Disability accommodations: Contact Disability & Access Services (617-353-3658, access@bu.edu). Share your letter privately with the instructor.
• Academic & well-being support: ERC tutoring, writing support, and BU Student Wellbeing resources (see Blackboard).

Regrades

Submit requests via Gradescope within 7 days of score release, identifying the specific error. Scores may go up, down, or remain unchanged; decisions are final.