Summer Courses

IAD is excited to offer the following courses for Summer 2022:

CDA 500-ZIA: Problem-Solving with Algorithms and Data Structures Using Python

Wednesdays 5:00PM - 7:40PM • 5/31/22 to 8/05/22

This course will cover data structures and algorithms that advanced Python programmers need to write code that runs faster and more efficiently. Topics will include Big O notation, linked-lists, searching, sorting, greedy algorithms, hashes, stacks, queues, and graphs. The course is designed to meet the demands of coding interviews.

(2 Credit Hours)

Instructor: Mohammed Zia
Prerequisite: EAS503 or Equivalent
Format: In-person or Online

CDA 500-RHB: Bayesian Networks in R

Tuesdays and Thursdays 9:30am-11:30am • 7/5/2022 to 7/14/202

This course gives an overview of Bayesian Networks with application in R. The focus will be Bayesian network modeling, from structural learning to parameter learning and inference. Classic discrete, Gaussian, and conditional Gaussian networks will be described. Applications will showcase the wealth of R packages dedicated to learning and inference.

(1 Credit Hour)

Instructor: Rachael Hageman Blair
Prerequisite: Basic R Programming, Basic Probability
Format: Online

CDA 500-PRO1 : Part I Foundations of Probability

Tuesdays and Thursdays 9:00am-11:45am • 5/31/22 - 6/10/22

This course covers:

  • A Definition of a Random Variable
  • Properties of Discrete Random Variables: The Distribution (pmf and CDF), Mean, Variance
  • Important Discrete Random Variables: Binomial, Poisson, Geometric, Negative Binomial
  • Properties of Discrete Random Variables: The Distribution (pdf and CDF), Mean, Variance
  • Important Continuous Random Variables: Uniform, Exponential, Normal
  • Advanced Topics: Sums of Random Variables, Covariance, Independence, Transformations

(1 Credit Hour)

Instructor: Dietrich Kuhlmann 
Prerequisite: Basic Calculus
Format: Online

CDA 500-PRO2: Part II Estimators and Properties of Estimators

Tuesdays and Thursdays 9:00am-11:45am • 6/27/22 - 7/8/22

This course covers:

  • A The Distribution of Estimators (Moment Generating Functions)
  • Properties of Estimators: Bias, Mean Square Error, Relative Efficiency and Consistency
  • Advance Topics of Estimators: Cramer-Rao Lower Bound, Maximum Likelihood Estimators and Method of Moments Estimators
  • Interval Estimators: Pivotal Quantities, Confidence Intervals
  • Introduction to Hypothesis Testing: Stating the Null and Alternative Hypothesis, Types of Errors, Power of a Test, Neyman-Pearson Lemma

(1 Credit Hour)

Instructor: Dietrich Kuhlmann 
Prerequisite: Part 1 or Prob and Stats
Format: Online

CDA 500-ASA1: Part III Applied Statistical Analysis

Tuesdays and Thursdays 9:00am-11:45am • 7/18/22 - 7/29/22

This course covers:

  • A One-Sample Analysis: Confidence Intervals and Hypothesis Testing for a Population Mean, Population Variance and a Population Proportion
  • Tests for Normality
  • Two-Sample Analysis: Two-Sample Means, Paired t-test, Two-Sample Variances (F-test, Bonett, and Levine)
  • Other Chi-Square Tests: Chi-Square Goodness of Fit, Chi-Square Test for Association

(1 Credit Hour)

Instructor: Dietrich Kuhlmann 
Prerequisite: Part 1 or Prob and Stats
Format: Online

CDA 500-ASA2: Advanced Statistical Analysis

Tuesdays and Thursdays 9:00am-11:45am • 8/1/22 - 8/12/22

Analysis of Variance: 1-way ANOVA, Multiple Range Tests, Kruskal-Wallis Test, Welch’s ANOVA, 2-Way ANOVA, ANOVA with Interaction, Model Adequacy Checking

Regression Analysis: Correlation, Simple Linear Regression, Quadratic Regression, Transformations in Regression, Multiple Regression

(1 Credit Hour)

Instructor: Dietrich Kuhlmann 
Prerequisite: Part 3
Format: Online

Graduate students who are interested in taking a CDA summer course can submit a force registration request in the SEAS Portal.