This course is designed to enable students to learn the significance of data visualization in data science and big data analytics, and develop knowledge and skills to present quantitative data using data visualization tools. This course emphasizes on the practical aspects of data science with a focus on using R or Python programming language to process data, produce visualizations, and interpret these visualizations. Students will learn the practice of data cleaning, reshaping of data, basic tabulations, aggregations and visual representation in order to increase the understanding of complex data and models.

Course Objectives

• Describe the development and principles of data analytics and data visualization
• Identify different types of data (qualitative vs quantitative) and use appropriate analysis techniques (probabilistic, regression, cluster, etc.) best to explore them
• Draw conclusions and formulate hypotheses from data presented graphically
• Apply theories of data analytics and data visualization and competence in using software (Python, R/RStudio, Excel, etc.) for data visualization and data analytics
• Analyze, critique, and revise data visualizations

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