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Continuing Education

Data Visualization and Statistical Analysis Certificate (Learn-by-doing, 24/7, Online, Self-Paced, Gamified, Realtime Instructional Support)

0 credit hours

Credits awarded upon completion

Self-Paced

Progress at your own speed

40 hours

Estimated learning time

About the Course

Description

Data Visualization and Statistical Analysis (NCLab): This online, self-paced, learn-by-doing course equips Learners with the skills to interpret, visualize, and analyze data using charts, spreadsheets, and statistical methods. Learners begin by classifying data types and learning visualization techniques using bar charts, line charts, histograms, geo charts, and more—including advanced formats like treemaps, Gantt charts, radar, and candlestick charts. They then explore statistical analysis through hands-on spreadsheet work, gaining confidence with linear, multiple, and logistic regression, hypothesis testing, and understanding data distributions. Topics include correlation, skewness, kurtosis, confidence intervals, ANOVA, and Chi-squared testing. Learners also apply critical thinking to identify causal relationships, interpret trendlines, and make data-driven predictions across business and research contexts.

Topics

  • Foundations of Data Types and Sources
  • Visualizing and Communicating Data Effectively
  • Statistical Measures and Data Distribution
  • Probability Theory and Distributions
  • Correlation and Regression Analysis
  • Advanced Chart Customization and Interpretation
  • Cluster Analysis and Pattern Recognition.

Prerequisites

  • NCLab Data Literacy Course is a REQUIRED prerequisite for this course.

Sections

Schedule

Asynchronous

Delivery method

Online

Deliverables

  • 0 Credits

    Academic Excellence

    Earn necessary number of credit hours for completing this content

  • Professional Program

    Launch of Career

    Data Visualization and Statistical Analysis Completion Certificate.

Outcomes

Have the knowledge and skills to work with Foundations of Data Types and Sources; Visualizing and Communicating Data Effectively; Statistical Measures and Data Distribution; Probability Theory and Distributions; Correlation and Regression Analysis; Advanced Chart Customization and Interpretation; Cluster Analysis and Pattern Recognition.

Outcomes Image