Data Analytics Visualization

3 Credits · Level: Undergraduate

This course introduces the fundamental concepts and tools of data preparation, data analytics, and visualization. Using lectures and assignments, students will learn how to collect, evaluate, scrub, describe, and analyze datasets of various forms as well as visualize. The course will cover various statistical tools used in data analytics to build and test models to discover insights and make predictions. This course is intended for non-majors seeking a general overview and practical knowledge on handling, analyzing, and visualizing data for better decision making. The course covers the following three modules: (1) Data Management (DM): Data quality assessment, data forms, data scrubbing, data collection, data storage, data integration, data transformation. (2) Data Analysis (DA): descriptive analytics, predictive analytics, text analytics, supervised and unsupervised AI & machine learning techniques, Pandas, NumPy/SciPy. (3) Data Visualization (DV): Excel, tableau, Python, seaborn/ggplot, matplotlib, Box and Whisker Plot, Scatterplot, Timeline, histograms, Bar/Column/Pie/Line charts.