Course Description

Data science is booming and shaping future careers. Job opportunities for data analysts/scientists are growing rapidly as companies and big organizations are generating massive amounts of data and seeking to extract valuable insights from them. In this 21st century, aptitude and skills in statistics and machine learning are increasingly becoming important for researchers in banks, industries, academics, and international institutions. This course offers flexible practical training in machine learning, using the R program. You will acquire the essential skills and become a proficient R Data Analyst/Scientist. At the end of the course, you will know how to use the most widespread machine learning techniques to make accurate predictions and get valuable insights from financial, non-financial, and cross-sectional data. The first two modules are widely used for predictive analytics. Module 3 will expose you to text analysis which is part of Natural Language Processing (NLP). This knowledge is being used by data analysts to analyze unstructured data such as texts. Module 4 will enhance your capacity to utilize the latest techniques of data visualization using the grammar of graphics called the ggplot2 package in R.

Learning Outcomes

At the end of the training, participants will be able to:

• Understand the essential concepts related to machine learning
• Perform model cross-validation to assess model stability on independent data sets
• Execute advanced regression analysis techniques: best subset selection regression, penalized regression, PLS regression
• Perform logistic regression and discriminant analysis
• Apply complex classification techniques: Naive Bayes, K nearest neighbor, support vector machine, decision trees
• Use neural networks to make predictions
• Use principal components analysis to detect patterns in variables
• Conduct cluster analysis to group observations into homogeneous classes

Mode of Delivery

Interactive Virtual (Using Google Meet)