Principles of Data Science

Module Title:
Principles of Data Science
Module Code:

Module Content
  • Fundamental principles of data science
  • Concepts in data exploration and visualization (relationships and causality, reliability and validity, relationships between interval, interval and categorical data with correlation analysis, ...) and basic visualization techniques
  • Database management systems, and core data mining techniques (association, Classification, Clustering, Prediction, Sequential patterns, Decision trees, ...)
  • Data collection, cleaning, pre-processing, and storage using various databases;
  • Numeric and categorical variable analysis and visualization with descriptive statistics
  • Exploratory data analysis to understand and profile complex data sets; Visual analytics, statistics, and statistical models, causal inference
  • Supervised and unsupervised modeling, overfitting and its avoidance, evaluation and model analytics
  • Graph and text data analyzing and visualizing techniques for web and big data
  • Visualization techniques for interactive quantitative analysis of relationships and information
  • Reporting the results and presenting the data with visualization techniques
  • Concepts in machine learning and mining for labeled, unlabelled data to identify relationships, patterns, and trends (predict into the future);
  • Communicate findings to varied audiences and effective use of data visualizations
  • Data pipeline