Easy Guide to Statistical analysis & Data Science Analytics

Published 2022-07-21
Platform Udemy
Number of Students 1
Price $84.99
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.


  1. Motivation

  2. Introduction to R

  3. R Data Management

  4. R Programming

  5. Statistics with R

  6. Statistics with R (Categorical)

  7. Statistics with R (Numerical)

  8. Data visualization

  9. Text mining and Apriori algorithm

  10. Dimensionality reduction and unsupervised machine learning

  11. Feature selection techniques

  12. Lazy learning (k-nearest neighbors)

  13. k-Means clustering

  14. Naive Bayesian classification

  15. Decision Trees classification

  16. Black box: Neural Network & Support Vector Machines

  17. Regression, Forecasting & Recurrent NeuralNet

  18. Model Evaluation, Meta-Learning & Auto-tuning

  19. Deep Learning

  20. 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.

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