Health Data Analysis Courses

Algebra and Differential Calculus for Data Science

265 ratings at Coursera

Learn essential algebra and calculus concepts for data science, focusing on derivatives, graphing, and function optimization. Gain a solid math foundation without unnecessary complexity.

• Coursera
• 8 hours 1 minute
• Self-Paced
• Paid Course

Credential

Data Mining Foundations and Practice

182 ratings at Coursera

Discover patterns in large-scale datasets through key data mining concepts and techniques. Learn data preprocessing, modeling, frequent pattern analysis, classification, clustering, and gain hands-on project experience.

• Coursera
• 8 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 3 courses

Control Systems Analysis: Modeling of Dynamic Systems

134 ratings at Coursera

Explore dynamic system modeling, feedback control, and stability analysis. Master Laplace transforms, transfer functions, and performance metrics to design and optimize control systems.

• Coursera
• 14 hours 46 minutes
• Self-Paced
• Paid Course

Credential

Vital Skills for Data Science

131 ratings at Coursera

Explore key areas in data science, including ethics, cybersecurity, and visualization. Gain practical skills and insights to determine your interests and future specialization in this diverse field.

• Coursera
• 8 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 3 courses

Data Mining Pipeline

117 ratings at Coursera

Explore key steps in data mining: understanding, preprocessing, warehousing, modeling, interpretation, and real-world applications. Gain practical skills for effective data analysis and decision-making.

• Coursera
• 21 hours 54 minutes
• Self-Paced
• Paid Course

Integral Calculus and Numerical Analysis for Data Science

109 ratings at Coursera

Intuitive approach to integral calculus and numerical analysis, covering integration, root-finding, matrix decomposition, and partial derivatives for data science applications.

• Coursera
• 3 hours 32 minutes
• Self-Paced
• Paid Course

Credential

Software Architecture for Big Data

107 ratings at Coursera

Learn principles and patterns for architecting large-scale distributed systems that handle big data. Gain industry insights on building performant software for data-intensive applications.

• Coursera
• 8 weeks, 10 hours/week
• Self-Paced
• Paid Course

Product Cost and Investment Cash Flow Analysis

101 ratings at Coursera

Explore product costing, time value of money, and personal finance principles using spreadsheets. Gain practical skills for engineering and science careers in industry.

• Coursera
• 20 hours 37 minutes
• Self-Paced
• Paid Course

Statistical Estimation for Data Science and AI

94 ratings at Coursera

Learn statistical inference, sampling distributions, and confidence intervals. Explore estimation methods, construct good estimators, and develop confidence intervals for various settings. Gain practical skills for data analysis and interpretation.

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

Fundamentals of Software Architecture for Big Data

92 ratings at Coursera

Aprende a construir y escalar sistemas distribuidos intensivos en datos, aplicando prácticas de ingeniería de software y microservicios. Ideal para profesionales y graduados recientes en ciencia de datos o informática.

• Coursera
• 20 hours 29 minutes
• Self-Paced
• Paid Course

Python Packages for Data Science

79 ratings at Coursera

Explore essential Python packages for data science: Pandas, NumPy, Matplotlib, and Seaborn. Learn data manipulation and visualization techniques to enhance your data analysis skills.

• Coursera
• 20 hours
• Self-Paced
• Paid Course

Data Mining Methods

72 ratings at Coursera

Explore core data mining techniques including pattern analysis, classification, clustering, and outlier analysis. Learn advanced methods for complex data and discover research frontiers in the field.

• Coursera
• 1 day 25 minutes
• Self-Paced
• Paid Course

Credential

Statistical Modeling for Data Science Applications

62 ratings at Coursera

Develop advanced statistical modeling skills for data science, including linear regression, ANOVA, and generalized linear models, with practical applications using R programming.

• Coursera
• 12 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 3 courses

Credential

Expressway to Data Science: R Programming and Tidyverse

56 ratings at Coursera

Master R programming and Tidyverse for data science: import, tidy, and visualize data using ggplot2. Develop skills for reproducible research and advanced data analysis techniques.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 3 courses

Statistical Inference and Hypothesis Testing in Data Science Applications

53 ratings at Coursera

Learn hypothesis testing for data science applications, covering theory, implementation, and ethical considerations. Develop skills to make informed decisions from data using statistical inference techniques.

• Coursera
• 1 day 12 hours 40 minutes
• Self-Paced
• Paid Course