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I'm going to be a junior in college soon, and I need help choosing between different statistics courses! I'm very much interested in Machine Learning and Data science, but I'm not sure which Statistics courses will help for that, if I decide to attend graduate school. I'm hoping to complete a minor in either Math or Stats, but I'm not too focused on the official title, rather the material I'll learn. Any help is appreciated!! Here's some course sequences I'm deciding between:

submitted by statsthrowaway124 to AskStatistics [link] [comments]
- Probability and Stochastic Processes (In the Math Department, 3 courses): Basic concepts of random variables, distributions, independence, correlations, moments, limit theorems, conditional probability, Markov chains, gambler's ruin, branching process, birth and death processes, numerical simulations in Matlab. / Exponential distributions, Poisson processes, continuous time Markov chains, renewal theory, insurance ruin and claim problems, numerical simulations in Matlab. / Martingales, Invariance Principle, Brownian motions and applications in option pricing, stationary processes and applications in Wiener filter, numerical simulations in Matlab.
- Statistical Methods for Data Analysis (3 courses): Introduction to statistical methods for analyzing data from experiments and surveys. Methods covered include two-sample procedures, analysis of variance, simple and multiple linear regression. / Emphasizes application and understanding of methods for categorical data including contingency tables, logistic and Poisson regression, loglinear models. / Topics covered include survival methods for censored time-to-event data, linear mixed models, non-linear mixed effects models, and generalized estimating equations.
- Introduction to Probability and Statistics (3 courses): Introduction to basic principles of probability and statistical inference. Axiomatic definition of probability, random variables, probability distributions, expectation. / Point estimation, interval estimating, and testing hypotheses, Bayesian approaches to inference. / Linear regression, analysis or variance, model checking.
- Introduction to Bayesian Data Analysis (1 course): Basic Bayesian concepts and methods with emphasis on data analysis. Special emphasis on specification of prior distributions. Development for one-two samples and on to binary, Poisson, and linear regression. Analyses performed using free OpenBugs software.
- Multivariate Statistical Methods (1 course): Theory and application of multivariate statistical methods. Topics include statistical inference for the multivariate normal model and its extensions to multiple samples and regression, use of statistical packages for data visualization and reduction, discriminant analysis, cluster analysis, and factor analysis.
- Linear Algebra (2 courses)
- Numerical Analysis (2 courses)
- Real Analysis (1-3 courses)

X-post from /AskStatistics

I'm going to be a junior in college soon, and I need help choosing between different statistics courses! I'm very much interested in Machine Learning and Data science, but I'm not sure which Statistics courses will help for that, if I decide to attend graduate school. I'm hoping to complete a minor in either Math or Stats, but I'm not too focused on the official title, rather the material I'll learn. Any help is appreciated!! Here's some course sequences I'm deciding between:

submitted by statsthrowaway124 to learnmachinelearning [link] [comments]
I'm going to be a junior in college soon, and I need help choosing between different statistics courses! I'm very much interested in Machine Learning and Data science, but I'm not sure which Statistics courses will help for that, if I decide to attend graduate school. I'm hoping to complete a minor in either Math or Stats, but I'm not too focused on the official title, rather the material I'll learn. Any help is appreciated!! Here's some course sequences I'm deciding between:

- Probability and Stochastic Processes (In the Math Department, 3 courses): Basic concepts of random variables, distributions, independence, correlations, moments, limit theorems, conditional probability, Markov chains, gambler's ruin, branching process, birth and death processes, numerical simulations in Matlab. / Exponential distributions, Poisson processes, continuous time Markov chains, renewal theory, insurance ruin and claim problems, numerical simulations in Matlab. / Martingales, Invariance Principle, Brownian motions and applications in option pricing, stationary processes and applications in Wiener filter, numerical simulations in Matlab.
- Statistical Methods for Data Analysis (3 courses): Introduction to statistical methods for analyzing data from experiments and surveys. Methods covered include two-sample procedures, analysis of variance, simple and multiple linear regression. / Emphasizes application and understanding of methods for categorical data including contingency tables, logistic and Poisson regression, loglinear models. / Topics covered include survival methods for censored time-to-event data, linear mixed models, non-linear mixed effects models, and generalized estimating equations.
- Introduction to Probability and Statistics (3 courses): Introduction to basic principles of probability and statistical inference. Axiomatic definition of probability, random variables, probability distributions, expectation. / Point estimation, interval estimating, and testing hypotheses, Bayesian approaches to inference. / Linear regression, analysis or variance, model checking.
- Introduction to Bayesian Data Analysis (1 course): Basic Bayesian concepts and methods with emphasis on data analysis. Special emphasis on specification of prior distributions. Development for one-two samples and on to binary, Poisson, and linear regression. Analyses performed using free OpenBugs software.
- Multivariate Statistical Methods (1 course): Theory and application of multivariate statistical methods. Topics include statistical inference for the multivariate normal model and its extensions to multiple samples and regression, use of statistical packages for data visualization and reduction, discriminant analysis, cluster analysis, and factor analysis.
- Linear Algebra (2 courses)
- Numerical Analysis (2 courses)
- Real Analysis (1-3 courses)

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submitted by AlexHil to eMobilecodereviews [link] [comments]
=========>>>>>>>>DOWNLOAD IT FROM HERE FOR GETTING DISCOUNT LINK<<<<<<<<<<=======

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