Easy Guide to Statistical analysis & Data Science Analytics

What you’ll learn
To be familiar with R environment with guided coding
To run end-to-end data to inference using basic and advanced statistical techniques
To train,test,evaluate and improve machine learning algorithm for common to complex research problems
To complete statisics and data science practice exercises using real and simulated data sets
To cultivate independent and innovative analytical plans for ground-breaking research output

Requirements
High school level English is essential.
Beginner-level research knowledge is helpful.
This course is for beginner to advanced users. No programming experience required but would be helpful.
No minimum qualification required.

Description
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.

Content

Motivation

Introduction to R

R Data Management

R Programming

Statistics with R

Statistics 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 NeuralNet

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.

Who this course is for
College level student researchers
University level graduate researchers
Entry-level researcher
Senior-level researcher