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.

Instructors: Ian McCulloh +1 more

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.

    • Skills you'll gain

  •   Combinatorics
  •   Statistical Methods
  •   Regression Analysis
  •   Bayesian Network
  •   R Programming
  •   Statistical Analysis
  •   Probability Distribution
  •   Network Analysis
  •   Statistical Hypothesis Testing
  •   Bayesian Statistics
  •   Applied Mathematics
  •   Statistical Modeling

    • Specialization - 3 course series

    In the "Statistical Methods for Computer Science" specialization, learners use R in Jupyter Notebooks to build foundational skills in data analysis, modeling, and statistical inference, applied to computer science problems. Through hands-on labs, learners progressively explore data cleaning, visualization, hypothesis testing, regression analysis, and classification, applying these methods to solve practical data challenges. Each assignment involves setting up a Jupyter Notebook, analyzing data, and documenting findings in both .ipynb and .pdf formats. This course equips learners with essential statistical skills, data-driven problem-solving abilities, and clear reporting practices, providing a solid foundation for advanced machine learning and data science applications in computer science.

    Advanced Probability and Statistical Methods

    Computational and Graphical Models in Probability

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