Executive Development Programme in Advanced Statistical Methods: Cutting-Edge Techniques
-- ViewingNowThe Executive Development Programme in Advanced Statistical Methods: Cutting-Edge Techniques is a certificate course designed to enhance the statistical skills of professionals. In today's data-driven world, there is a high demand for experts who can analyze and interpret complex data sets.
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⢠Advanced Regression Analysis: This unit will cover various types of regression analysis such as multiple linear regression, logistic regression, and polynomial regression. It will also cover advanced topics like regularization techniques (Ridge, Lasso, and Elastic Net) and interactions.
⢠Time Series Analysis: This unit will focus on analyzing data that is collected over time. It will cover topics such as autoregressive (AR), moving average (MA), autoregressive integrated moving average (ARIMA), and seasonal ARIMA models.
⢠Multivariate Analysis: This unit will cover techniques for analyzing data with multiple dependent variables. It will include topics such as factor analysis, principal component analysis, and discriminant analysis.
⢠Machine Learning Techniques: This unit will cover various machine learning techniques such as decision trees, random forests, and support vector machines. It will also cover unsupervised learning techniques like clustering and dimensionality reduction.
⢠Experimental Design and Analysis: This unit will cover the design and analysis of experiments. It will include topics such as completely randomized designs, randomized block designs, factorial designs, and analysis of variance (ANOVA).
⢠Survival Analysis: This unit will focus on analyzing time-to-event data. It will cover topics such as Kaplan-Meier survival curves, Cox proportional hazards models, and survival trees.
⢠Bayesian Inference: This unit will cover the basics of Bayesian inference and its applications in statistics. It will include topics such as Bayes' theorem, prior and posterior distributions, and Markov chain Monte Carlo (MCMC) methods.
⢠Data Mining and Big Data Analysis: This unit will cover techniques for analyzing large datasets. It will include topics such as data preprocessing, data visualization, and parallel computing.
⢠Statistical Learning Theory: This unit will cover the theoretical foundations of statistical learning. It will include topics such as bias-variance tradeoff, model selection, and regularization.
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