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Hanif Abdul Rahman
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Practical Guide to Statistical analysis and Data Science Analytics for students and researchers
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 tSNE, 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.
Introduction to R
R Data Management
Statistics with R
Statistics with R (Categorical)
Statistics with R (Numerical)
Text mining and Apriori algorithm
Dimensionality reduction and unsupervised machine learning
Feature selection techniques
Lazy learning (k-nearest neighbors)
Naive Bayesian classification
Decision Trees classification
Black box: Neural Network & Support Vector Machines
Regression, Forecasting & Recurrent NeuralNet
Model Evaluation, Meta-Learning & Auto-tuning
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.
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