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DBMI Course Prerequisites

This is no longer offered.

You’ll be given a small exam to see how much statistics and programming you’ll need to learn to take other informatics courses.

Programming​

Basic UNIX Stuff​

  • awk
  • grep
  • sed
  • find

Regular Expressions (#)​

This is your friend. Play with it, learn from it.

Python​

Inhale this book.

  • Conditionals and Ternaries
  • Loops: break and continue
  • Data Structures (and Big-O for each!)
    • List Comprehensions
    • Lists: map, filter, and reduce
    • Tuples
    • Sets
    • Hash Maps/Dictionaries
  • Functions
    • Recursion
    • Decorators/Closures
  • I/O
    • Reading and Writing Files
    • De/Serialization of JSON files
  • Vectorized Operations and Efficiency Considerations
  • Scientific Python
    • Draw a line plot
    • Draw a histogram
    • Dataframes: Sorting, Filtering. Get a Pandas cheatsheet.
    • Review a NumPy cheatsheet.
  • Dataclasses

Object-Oriented Programming 🤢​

Relational Databases​

This site is your friend. Do everything there and you’ll know most of what you’ll need. Making friends with a DBA is (I’m assuming) outside the scope of this course.

  • Language (SELECT/WHERE/LIKE GROUP BY ORDER BY ASC DESC)
  • Various Joins (INNER OUTER LEFT RIGHT)
  • Functions (MIN MAX SUM COUNT CONCATENATE)
  • Indexes: single and multi-column. Types of Indexes (e.g. inverted B-trees)
  • Keys: Primary, Secondary, Foreign
  • Schemas, Fields/Columns, lingo
  • Fuzziness
  • Subqueries

Git​

  • Know that you’re fundamentally dealing with a content-addressible system.
  • 80% of porcelain commands.
  • Refs, HEAD
  • Rebasing versus Merging

Other​

  • Intro to the Unified Medical Language System (UMLS)
  • File formats and their strengths and weaknesses (JSON, CSV, XML). De/Serialization. Columnar compression.

Linear Algebra​

See this page.

  • Scalars, Vectors, Matrices, Tensors
  • Basis Space and Basis Vectors
  • Dot and Cross Products
  • Matrix Rank (look at Row Echelon Forms)
  • Matrix Transpose and Operations
  • Matrix Determinants and what they mean
  • Matrix Invertibility
  • Matrix Adjoint
  • Matrix Conjugate
  • Matrix Orthogonality
  • Eigenvalues and Eigenvectors
  • Cramer’s Rule

Probability​

See this.

  • Counting
  • Philosophy/Interpretation
  • Axioms
  • Sample Spaces
  • Sample Events
  • Expected Value
  • Law of Total Probability
  • Conditional Probabilities
  • Independent and Dependent Probability
  • Chain Rule
  • Density Functions (PDFs)
  • Mass Functions (PMFs)
  • Cumulative Distribution Functions
  • Bayesian Analysis
  • PDFs, CDFs: Support, means, variances of common ones
  • Distribution “Arithmetic” (e.g. U(0,1)+U(0,1)=?U(0,1) + U(0,1) = ?)
  • The Gaussian/Normal Distribution and derivation
  • The Multivariate Gaussian Distribution
  • Moment Generating Functions and “Fingerprints”

Statistics​

See this.

Concepts​

  • Measures of Central Tendency: Mean, Median, Mode
  • Variance
  • The Central Limit Theorem
  • Estimators
    • Method of Least Squares
    • Maximum Likelihood Estimation
    • Maximum A Posteriori Estimation

Randomness​

  • Random Variables
  • Covariance
  • Sampling
  • Estimation
  • Bootstrapping

Hypothesis Testing​

Theory​

  • p-value
  • Null hypothesis
  • Confidence Intervals
  • Credible Intervals

Practice​

  • X2\Chi^2-squared Test
  • t-Test
  • ANOVA
  • Pearson Correlation
  • Non-parametric Tests

Prediction​

  • Regression
    • Linear
    • Multivariate
    • Logistic
    • Ridge
    • LASSO
  • Convex Optimization
  • Machine Learning