Johns Hopkins University
Statistical Methods for Computer Science Specialization
Johns Hopkins University

Statistical Methods for Computer Science Specialization

Master Statistical Methods for Data Analysis. Gain advanced skills in probability, statistical modeling, and computational techniques for effective data analysis and decision-making.

Ian McCulloh

Instructor: Ian McCulloh

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 5 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Gain proficiency in advanced statistical techniques and probability models to analyze complex data sets across various applications in computing.

  • Develop practical skills in simulation methods, network analysis, and probabilistic graphical models for effective data-driven decision-making.

  • Master hypothesis testing, regression analysis, and network modeling to derive meaningful insights and drive innovation in statistical methods.

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Taught in English
Recently updated!

October 2024

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Specialization - 3 course series

What you'll learn

  • Master combinatorial techniques, including permutations, combinations, and multinomial coefficients, to solve counting and probability problems.

  • Apply probability axioms, construct Venn diagrams, and calculate sample space sizes to evaluate probabilities in various scenarios.

  • Utilize Bayes' formula, the multiplication rule, and conditional probability to assess event relationships and solve real-world problems.

  • Analyze discrete and continuous random variables using probability density functions, cumulative distribution functions, and expected values.

Skills you'll gain

Category: Continuous Random Variables
Category: Discrete Random Variables
Category: Conditional Probability
Category: Combinatorial Analysis
Category: Probability Calculation

What you'll learn

  • Learn to analyze relationships between random variables through joint probability distributions and independence concepts.

  • Understand how to calculate and interpret expected values, variances, and correlations for random variables.

  • Acquire essential skills in conducting statistical tests, including T-tests and confidence intervals, for data analysis.

  • Explore the principles of Markov chains and their applications in modeling systems with memoryless properties and calculating entropy.

Skills you'll gain

Category: Joint Probability Analysis
Category: Statistical Inference
Category: Expectation Calculations
Category: Application of Limit Theorems
Category: Markov Chain Modeling

What you'll learn

  • Master techniques for simulating random variables, including the Inverse Transformation and Rejection Methods using R programming.

  • Analyze complex networks using Exponential Random Graph Models to model and interpret social structures and their dependencies.

  • Understand and apply probabilistic graphical models, including Bayesian networks, to reason about uncertainty and infer relationships in data.

Skills you'll gain

Category: Network Analysis
Category: Data Visualization
Category: Statistical Hypothesis Testing
Category: Statistical Modeling
Category: Random Variable Simulation

Instructor

Ian McCulloh
Johns Hopkins University
8 Courses186 learners

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