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.
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Spécialisation Statistical Methods for Computer Science
Master Statistical Methods for Data Analysis. Gain advanced skills in probability, statistical modeling, and computational techniques for effective data analysis and decision-making.
Instructeurs : Ian McCulloh
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Expérience recommandée
Expérience recommandée
Ce que vous apprendrez
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.
Vue d'ensemble
Compétences que vous acquerrez
- Probability & Statistics
- Statistics
- Statistical Methods
- Probability Distribution
- Bayesian Statistics
- Combinatorics
- Data Analysis
- Probability
- Data Science
- Network Analysis
- Bayesian Network
- Statistical Modeling
- Statistical Analysis
- Statistical Hypothesis Testing
- Social Network Analysis
- Simulations
- Regression Analysis
- Applied Mathematics
Outils que vous découvrirez
Ce qui est inclus
Ajouter à votre profil LinkedIn
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- Acquérez des compétences recherchées auprès d’universités et d’experts du secteur
- Maîtrisez un sujet ou un outil avec des projets pratiques
- Développez une compréhension approfondie de concepts clés
- Obtenez un certificat professionnel auprès de Johns Hopkins University
Spécialisation - série de 3 cours
Ce que vous apprendrez
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.
Compétences que vous acquerrez
Ce que vous apprendrez
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.
Compétences que vous acquerrez
Ce que vous apprendrez
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.
Compétences que vous acquerrez
Obtenez un certificat professionnel
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Instructeurs
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Foire Aux Questions
The specialization is designed to be completed at your own pace, but on average, it is expected to take approximately 3 months to finish if you dedicate around 5 hours per week. However, as it is self-paced, you have the flexibility to adjust your learning schedule based on your availability and progress.
You are encouraged to take the courses in the recommended sequence to ensure a smoother learning experience, as each course builds on the knowledge and skills developed in the previous ones. However, you are not required to follow a specific order, and you can take the courses in the order that best suits your needs and prior knowledge.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
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