Advanced Diploma in Data and Predictive Analytics in R Programming

• Managers, Data Analysts, Professionals, Executives involved in the analysis, interpretation and presentation of data for decision-making across various business functions such as marketing, customer service, and corporate communications.
• Data Scientists who are familiar with basics in R programming and want to learn how to perform web scraping from multiple webpages using packages in R.
  
  The course is intended to advance the knowledge and expertise of working professionals who already possess the fundamentals of R programming and data analytics, along with a basic understanding of statistical and causal inference modelling (modelling for explanation).
• The course is intended to advance such expertise, using both base R and tidyverse ways of R programming for cutting-edge practices, to predictive models and machine learning.
• Participants who wish to advance their expertise in predictive models and machine learning, along with productivity tools for data science (e.g., creating and maintaining GitHub and interactive dashboards), beyond the cornerstone knowledge and expertise on data analytics and visualisation, will find it a great fit and will learn the most on this course.
• Targeted Job Roles: Data analyst, Statisticians, Data scientist, Data Architect, Quantitative Analysis with R, System Intelligence Manager, Trading analyst

PREREQUISITES

• Functional Laptop
  • CPU must be of at least intel core I3;
  • GPU must have an integrated graphics card and;
  • RAM must be of at least 4GB

• Hands-on R programming meets lessons in statistical thinking
• Analysing datasets that hold real-world implications
• Data visualisation and storytelling—beyond the “Data Speaks for Itself” approach
• Capstone project based on participants’ professional interest
• Equip with the fundamental concepts and systemic framework (workflow) of supervised machine learning
• Learn the differences between solving regression and classification problems in the workflow of predictive models
• Learn the essential concepts of dimensionality reduction using Tidymodels framework in the R data science ecosystem
• Learn the core concepts of K-means and hierarchical clustering and differentiate them from dimensionality reduction
• Learn about supervised and unsupervised learning to text data and executing predictive models when given prediction tasks dealing with text
• Learn how to store and deploy models and algorithms, using an interactive dashboard, web API, and Github

Certified Data Analytics (R) Specialist

• Introduction To Data Analytics (using R programming)
• Introduction To Data Visualisation (using R programming)
• Web Scraping and Data Insights (using R programming)
• Statistical Inference for Managerial Insights (using R programming)
• A First Look at Visual Analytics (using ggplot2 packages in R)
• Advancing Skillsets Of Visual Analytics (Using Ggplot2 Packages In R)

Predictive Analytics and Machine Learning

• Predictive Modelling for Numerical Data
• Predictive Modelling for Categorical Data
• Dimensionality Reduction
• K-Means and Hierarchical Clustering
• Text Classification and Topic Modelling
• Shiny and Machine Learning in Production

Graduation Requirement

The Advanced Diploma qualification will be conferred to participants who successfully complete both Certification programmes within 3 years of commencing the first certificate.