Adaptive
Varies by mastery
Curated
How the course works
A session is a short study-and-practice checkpoint, not a fixed class meeting. The course can move faster when material is already familiar and slow down when a topic needs more practice.
Study a focused page
Read a small prerequisite-ordered set that gives the context for the next practice step.
Check understanding
Answer linked questions so the system can tell what is already strong and what needs review.
Keep moving
Unlock the next set after the current material is understood, with review scheduled as needed.
Who this course is for
High school students
Objectives
Not specified yet.
Syllabus
Covers data collection methods, survey design, web scraping, and data cleaning techniques.
2.1 Overview of Data Collection Methods
2.2 Survey Design and Implementation
2.3 Web Scraping and Social Media Data Collection
2.4 Data Cleaning and Preprocessing
2.5 Handling Large Datasets
Explores measures of center, variation, position, and probability distributions using Python.
3.1 Measures of Center
3.2 Measures of Variation
3.3 Measures of Position
3.4 Probability Theory
3.5 Discrete and Continuous Probability Distributions
Dives into statistical inference, confidence intervals, hypothesis testing, and regression analysis.
4.1 Statistical Inference and Confidence Intervals
4.2 Hypothesis Testing
4.3 Correlation and Linear Regression Analysis
4.4 Analysis of Variance (ANOVA)
Introduces time series analysis, forecasting methods, and evaluation techniques.
5.1 Introduction to Time Series Analysis
5.2 Components of Time Series Analysis
5.3 Time Series Forecasting Methods
5.4 Forecast Evaluation Methods
Covers machine learning basics, classification, regression, and decision trees.
6.1 What Is Machine Learning?
6.2 Classification Using Machine Learning
6.3 Machine Learning in Regression Analysis
6.4 Decision Trees
6.5 Other Machine Learning Techniques
Introduces neural networks, deep learning, and natural language processing.
7.1 Introduction to Neural Networks
7.2 Backpropagation
7.3 Introduction to Deep Learning
7.4 Convolutional Neural Networks
7.5 Natural Language Processing
Examines ethical considerations across data collection, analysis, and reporting.
8.1 Ethics in Data Collection
8.2 Ethics in Data Analysis and Modeling
8.3 Ethics in Visualization and Reporting
Focuses on data visualization techniques using Python.
9.1 Encoding Univariate Data
9.2 Encoding Data That Change Over Time
9.3 Graphing Probability Distributions
9.4 Geospatial and Heatmap Data Visualization Using Python
9.5 Multivariate and Network Data Visualization Using Python
Covers writing reports, validating models, and creating executive summaries.
10.1 Writing an Informative Report
10.2 Validating Your Model
10.3 Effective Executive Summaries
References
No references are attached to the current course nodes yet.