📅 T-Th 3:05 – 4:20 PM
👨🏫 Instructor: Joaquin Carbonara, PhD
🏢 Office: SAMC 379
⏰ Office Hours: By appointment
This course explores foundational mathematical and data science thinking, focusing on analytical reasoning, problem solving, and data-centric applications. Students will participate in weekly quizzes, assignments, a midterm project, presentations, and a final project. The main programming language used is python.
📘 How to Think Like a Data Scientist (HTTLADS) — free online
Access instructions will be provided in class and linked on the course LMS.
Students will:
| Assessment Component | Percentage |
|---|---|
| Weekly Quizzes | 20% |
| Assignments | 20% |
| Midterm Project | 20% |
| Presentations | 20% |
| Final Project | 20% |
⚠️ Important: Attendance is required to take quizzes and complete presentations.
We will cover approximately one module a week.
| Module | Topics / Readings | Activities |
|---|---|---|
| 1 | Chapter 4: Python & Jupyter Notebooks – Intro & Python Review (variables, data types, control flow, functions) |
Intro workshop, Python syntax exercises |
| 2 | Chapter 4: Python & Jupyter Notebooks – Jupyter, Colab, Markdown | Lab: Set up Jupyter/Colab + Markdown cells |
| 3 | Chapter 5: Learning Pandas with Movie Data – Intro to pandas & DataFrames | Lab: Load/inspect DataFrames |
| 4 | Chapter 5: Pandas – Filtering, indexing, multiple DataFrames | Assignment: Manipulating pandas DataFrames |
| 5 | Chapter 6: Exploratory Data Analysis (EDA) – Visualizations & summarizing data | Quiz + EDA lab (histograms, scatter plots) |
| 6 | Chapter 6 Continued: EDA case studies & deeper visualization | Midterm project preparation |
| 7 | Chapter 7: Ethical & Legal Considerations in Data Use | Discussion: Ethics case studies + short reflection |
| 8 | Chapter 8: Textual Analysis – Text mining & tidying data | Lab: Text tokenization and word frequency |
| 9 | Chapter 8 Continued: Textual Analysis practice & investigation questions | Quiz + small text analysis project |
| 10 | Chapter 9: Predictive Analytics – Intro & Bike Rental prediction | Intro to predictive modeling; SQL overview (if applicable) |
| 11 | Chapter 9 Continued: Bike data exploration & intro to SQL/querying | Lab: Build and evaluate simple predictive model |
| 12 | Chapter 9 / 10 Wrap-up: Advanced predictive topics + project check-ins | Final project presentations & review |
Regular attendance is required. You must be present to take quizzes and participate in presentations.
All students are expected to demonstrate honesty and integrity in completing course requirements and following university academic regulations. Acts of plagiarism, cheating, academic misconduct, or misrepresentation of work are inconsistent with the aims and goals of Buffalo State University and may result in disciplinary action.
Students should also consult resources on plagiarism avoidance, critical citation practices, and academic integrity posted by the university.
Students who need accommodations to complete course requirements due to a disability are invited to make their needs known to Student Accessibility Services (SAS) and to provide appropriate documentation.
Tutoring and academic support services (writing, math, study skills) are available through the Academic Center for Excellence.
Disruptive classroom behavior (cell phones, talking, noise, etc.) will not be tolerated. Instructors may take appropriate action, including removal from class, consistent with university policy.
Success in this course depends on consistent engagement — attending class sessions, keeping up with readings, participating in discussions, and careful preparation for presentations and projects. I'm looking forward to exploring data science thinking with you!