Easy Guide to Statistical analysis & Data Science Analytics

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Price: 99.99$

This online training provides a comprehensive list of analytical skills designed for students and researchers interested to learn applied statistics and data science to tackle common and complex real world research problems. This training covers end-to-end guide from basic statistics such as Chi-square test and multi-factorial ANOVA, to multivariate statistics such as Structural equation modeling and Multilevel modeling. Similarly, you will also learn powerful unsupervised machine learning techniques such as Apriori algorithm and t SNE, to more complex supervised machine learning such as Deep Learning and Transfer Learning. Whether you are a beginner or advanced researcher, we believe there is something for you! This workshop helps you better understand complex constructs by demystifying data science and statistical concepts and techniques for you. This also means you do not need to understand everything. Your goal (at least for now) is to be able to run your data end-to-end and get a result. You can build up on the knowledge over time, comfortably at your own pace. Statistics and data science can be intimidating but it does not have to be! Remember, learning the fundamentals of data science and statistical analysis for personal and professional usage is a great investment you will never regret, especially because these are essential skills to stay relevant in the digital era. Content: Motivation Introduction to RR Data Management R Programming Statistics with RStatistics with R (Categorical)Statistics with R (Numerical)Data visualization Text mining and Apriori algorithm Dimensionality reduction and unsupervised machine learning Feature selection techniques Lazy learning (k-nearest neighbors)k-Means clustering Naive Bayesian classification Decision Trees classification Black box: Neural Network & Support Vector Machines Regression, Forecasting & Recurrent Neural Net Model Evaluation, Meta-Learning & Auto-tuning Deep Learning Transfer Learning At the end of the training, participants are expected to be equipped with a tool chest of statistical and data science analytical skills to interrogate, manage, and produce inference from data to decision on respective research problems.