Download Easy Guide to Statistical analysis & Data Science Analytics

What you would learn in Easy Guide to Statistical analysis & Data Science Analytics course?

This online course offers a complete listing of analytical abilities that are designed for researchers and students looking to acquire the application of statistics and data science to solve common and complex real-world research issues.

The training will cover the full instruction from the basics of statistics like the Chi-square test, multi-factorial ANOVA, and multivariate statistical concepts like structural equation modelling and the Multilevel model. In addition, you'll learn powerful machine learning methods such as the Apriori algorithm and tSNE and more advanced machine learning with supervised learning, like Deep Learning or Transfer Learning. Whether you're a novice or a seasoned researcher, we are sure there is something to suit your needs!

This workshop will help you comprehend complex concepts by decoding the concepts of data science and statistics, and methods for you. Also, you don't have to be able to comprehend every aspect. Your aim (at least for the moment) is to process your data entirely and obtain a conclusion. You can increase your knowledge over time quickly at your own pace.

Data science and statistics aren't easy to master, but it doesn't need to be! Be aware that learning the basics of statistical science and data analysis to use for personal and professional purposes is an investment you'll not regret, especially since these are the essential skills needed to remain relevant in today's digital age.

Content:

Motivation
Introduction to R
R Data Management
Programming in R
Statistics using R
Statistics using R (Categorical)
Statistics using R (Numerical)
Data visualization
Text mining and the Apriori algorithm
Unsupervised machine learning as well as dimensionality reduction
Feature selection techniques
Lazy learning (k-nearest neighbors)
k-Means clustering
Naive Bayesian classification
Classification of Decision Trees
Black box: Neural Network & Support Vector Machines
Regression, Forecasting & Recurrent NeuralNet
Model Evaluation, Meta-Learning & Auto-tuning
Deep Learning
Transfer Learning

After the course, participants will have a box of data and statistical analytical skills in science to analyze how to manage and make conclusions from the data to make decisions about the relevant research questions.

Course Content:

To be comfortable with R's programming environment using guided code
To analyze end-to-end data for infer using advanced and basic techniques of statistical analysis
To test, train, and refine the machine learning algorithms to solve common to more complex research issues
To complete the statistics as well as data science exercises that use actual and simulation data sets
To create independent and innovative analytical strategies for cutting-edge research output