This Specialization is intended for students and professionals in computer science and data science seeking to develop advanced skills in probability and statistical modeling. Through three comprehensive courses, you will cover essential topics such as joint probability distributions, expectation, simulation techniques, exponential random graph models, and probabilistic graphical models. These courses will prepare you to analyze complex data structures, conduct hypothesis testing, and implement statistical methods in real-world scenarios. By the end of the Specialization, you will be equipped with the practical tools and theoretical knowledge needed to make informed decisions based on data analysis, enhancing your capabilities in both academic and industry settings. Additionally, you will gain hands-on experience with programming tools like R, which is widely used in the industry for statistical computing and graphics, making you a competitive candidate for roles that require data analysis, modeling, and interpretation skills in technology-driven environments.
Applied Learning Project
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.
