Empower your child to extract, analyze, and visualize complex data. Master Python libraries like Pandas, NumPy, and Matplotlib to solve real-world problems. Suitable for teens aged 12-20.
Students load and clean real-world files, including global weather statistics, sports scoring cards, and demographic datasets using Python.
Learn core mathematical and statistical logic, including mean, median, standard deviation, and value filtering to discover key trends.
Generate detailed scatter plots, line charts, bar graphs, and heatmaps to translate raw numbers into meaningful visual summaries.
Explore Pandas arrays, dataset manipulation, statistical plot visualizers, and data algorithms
Deep dive into object classes, dictionaries, and NumPy arrays. Learn vector arithmetic, slice operations, and data filtering.
Understand loading CSV files, index headers, cleaning null values, renaming columns, and grouping categories. Project: Clean and summarize a global carbon emissions file.
Master coordinate plotting, labels, legends, customized bar charts, scatter graphs, and line trends. Project: Visualizing historical sports player metrics.
Learn the basics of linear regression, data trends, and data ethics. Capstone Project: Analyze and present a public dataset, drawing statistical recommendations.
Focused attention for rapid learning and coding growth
Interactive coding classes with peers
Common questions parents ask about our online Data Science classes