Mathematics Courses and Certifications

Data Science Decisions in Time: Using Causal Information

Explore causal decision-making in data science, focusing on optimizing policies, pricing, and actions using causal structures and advanced analytical techniques for business, medical, and technological applications.

• Coursera
• 1 day 5 hours 23 minutes
• Self-Paced
• Paid Course

Honors Algebra 2: Polynomials and Complex Numbers

Master polynomial functions, complex numbers, and advanced algebraic techniques to solve higher-order equations and analyze mathematical relationships in this rigorous algebra course.

• Coursera
• 12 hours 6 minutes
• Self-Paced
• Paid Course

Honors Algebra 2: Algebraic, Exponential & Log Functions

Master advanced function families including exponential, logarithmic, radical, and inverse functions through theory and real-world applications like growth modeling and data analysis.

• Coursera
• 15 hours 2 minutes
• Self-Paced
• Paid Course

Career Certificate

Honors Algebra 2

Master advanced algebraic concepts including exponential, logarithmic, and polynomial functions while developing problem-solving skills for STEM applications and college readiness.

• Coursera
• Self-Paced
• Paid Course
• 3 courses

Linear Regression in R for Public Health

530 ratings at Coursera

Aprenda a criar modelos estatísticos em R para analisar fatores que afetam doenças e resultados de saúde, focando em regressão linear e suas aplicações em saúde pública.

• Coursera
• 15 hours 2 minutes
• Self-Paced
• Paid Course

Goldsmiths University of London;  University of London

Foundations of Data Science: K-Means Clustering in Python

735 ratings at Coursera

Learn essential data science concepts, focusing on K-means clustering in Python. Develop mathematical, statistical, and programming skills through hands-on exercises and a practical data clustering project.

• Coursera
• 1 day 5 hours 8 minutes
• Self-Paced
• Paid Course

Causal Inference

107 ratings at Coursera

Rigorous mathematical exploration of causal inference methods, including randomization, regression, propensity scores, and matching. Ideal for understanding and applying causal relationships in various fields.

• Coursera
• 12 hours 26 minutes
• Self-Paced
• Paid Course

Probability & Statistics for Machine Learning & Data Science

688 ratings at Coursera

Explore probability, statistics, and data analysis for machine learning. Apply concepts using Python in hands-on exercises. Gain skills in estimation, hypothesis testing, and exploratory data analysis.

• Coursera
• 1 day 9 hours 28 minutes
• Self-Paced
• Paid Course

Inferential Statistics

610 ratings at Coursera

Explore inferential statistics, from hypothesis testing to advanced techniques like regression and ANOVA. Learn to analyze data, make inferences, and interpret results using various statistical tests.

• Coursera
• 14 hours 41 minutes
• Self-Paced
• Paid Course

Causal Inference 2

16 ratings at Coursera

Explore advanced causal inference topics like mediation, principal stratification, and longitudinal analysis. Gain rigorous mathematical insights for applications in science, medicine, policy, and business.

• Coursera
• 5 hours 49 minutes
• Self-Paced
• Paid Course

Experimental Design Basics

313 ratings at Coursera

Explore efficient experiment design and data analysis techniques. Learn to plan, conduct, and interpret experiments effectively using statistical methods and software tools for reliable results in various industries.

• Coursera
• 12 hours 22 minutes
• Self-Paced
• Paid Course

Regression Analysis: Simplify Complex Data Relationships

599 ratings at Coursera

Explore predictive modeling techniques to uncover complex data relationships, from linear and logistic regression to ANOVA, enhancing your ability to extract valuable insights for data-driven decision-making.

• Coursera
• 1 day 3 hours 47 minutes
• Self-Paced
• Paid Course

Single Variable Calculus

4 ratings

This introductory calculus course covers differentiation and integration of functions of one variable, with applications.

• MIT OpenCourseWare
• 29 hours
• Self-Paced
• Free Video

Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

4 ratings

This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.

• MIT OpenCourseWare
• 28 hours
• Self-Paced
• Free Video

Bayesian Statistics: Techniques and Models

497 ratings at Coursera

Expand your Bayesian toolbox with advanced models and MCMC methods. Learn to construct, fit, assess, and compare sophisticated statistical models using R and JAGS for real-world data analysis.

• Coursera
• 1 day 5 hours 37 minutes
• Self-Paced
• Paid Course