Data Science Course Objectives

Data 1: Introduction to Data Science

• Exposure to different types of data sets (e.g., census, experimental)
• Identify questions that are answerable from the data available
• Calculate new variables from existing variables
• Exposure to importing and joining relational data
• Statistical Foundations
  • Formulate null and alternative hypotheses
  • Construct a sampling distribution
  • Calculate observed test statistics
  • Derive p-values and confidence intervals
• Connecting different variable types to types of visualization
• Basic exploratory data analysis and interpretation
• Learn about basic data structures (e.g., arrays, vectors, tables)
• Responsible data management (e.g., transparency, reproducibility)

Data 2: Intermediate Data Science

• Data Wrangling
  • Row operations: filtering, sorting, grouping, summarization
  • Column operations: selecting, calculations
  • Reshaping data
  • Joining relational data
• Programming
  • Custom functions
  • Iteration
  • File handling
• Visualization principles and techniques
• Communication principles and ethics
• Team-based collaboration and communication
• Creation of a data science portfolio

Data 3: Data Management

• Database design and management
• Structured querying (e.g., with SQL)
• Version control (e.g., git)
• String manipulation (regular expressions)
• Basics of web scraping
• Big data (larger than memory)
• Web services
• Software as a service
• Parallel computing

Data 4: Introduction to Machine and Statistical Learning

• Linear regression and classification
• Resampling methods (bootstrapping, cross-validation)
• Linear model selection and regularization (lasso, ridge)
• Nonlinear regression (polynomial, local, spline, step, additive)
• Tree-based methods (decision trees, random forests)
• Unsupervised learning (k-means, principal components)
• Data bias and ethical practices in modeling

Capstone / Community Data Labs

• Define a problem statement given a dataset and constraints
• Communicate with a community partner / real-world client
• Apply data science skills to a capstone project
• Contribute project work to student portfolio
• During the semester, we expect students to learn or improve their skills in:
  • Data management and wrangling
  • Data analysis and visualization
  • Statistical inference and prediction
  • Computer Programming
  • Data-driven decision making
  • Problem solving
  • Communication