| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Intermediate |
| Duration | 3 months |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹2499 / $59 |
| Tools | Python R Pandas NumPy Matplotlib Seaborn Tableau SQL |
About the Advanced Medical Statistics: Data Analysis for Evidence-based Decision Making Course
Advanced Medical Statistics: Data Analysis for Evidence-based Decision Making is a comprehensive intermediate-level program offered by NanoSchool (NSTC) that provides in-depth training in Advanced Medical Statistics. The course covers critical areas including Data Analysis for Evidence, based Decision Making, equipping learners with both theoretical foundations and practical expertise. Through a carefully structured curriculum, participants will develop the skills needed to tackle real-world challenges in Data Science.
Whether you are a student looking to enter the field of Data Science, a working professional seeking to upgrade your skill set, or a researcher exploring new methodologies, this course offers a structured learning pathway. Each module combines theoretical concepts with hands-on exercises, case studies, and projects to ensure practical mastery. Upon completion, learners will earn an e-Certification and e-Marksheet from NSTC.
Program Highlights
• Comprehensive coverage of Advanced Medical Statistics from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Data Science
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Exposure to industry-standard tools and platforms used in Data Science
• Career-oriented training for academic and professional growth in Data Science
Course Curriculum
Module 1: Introduction to Medical Statistics and Research Design
- Overview of medical statistics and its role in clinical research
- Understanding different types of research designs and their implications for statistical analysis
Module 2: Descriptive Statistics and Data Presentation
- Calculation and interpretation of descriptive statistics (measures of central tendency, variability)
- Effective data presentation techniques for clinical research
Module 3: Probability and Probability Distributions
- Understanding probability theory and its applications in clinical research
- Study of common probability distributions (normal, binomial, Poisson)
Module 4: Statistical Inference and Hypothesis Testing
- Principles of statistical inference and hypothesis testing
- Performing t-tests, chi-square tests, and other parametric and non-parametric tests
Module 5: Confidence Intervals and Sample Size Determination
- Construction and interpretation of confidence intervals
- Sample size determination for clinical research studies
Module 6: Analysis of Variance (ANOVA)
- Introduction to ANOVA and its applications in clinical research
- Performing one-way and two-way ANOVA tests
Module 7: Linear Regression and Correlation Analysis
- Understanding the concepts of linear regression and correlation
- Analyzing the relationship between variables and interpreting regression coefficients
Tools, Techniques, or Platforms Covered
Python R Pandas NumPy Matplotlib Seaborn Tableau SQL
Real-World Applications
- Apply Advanced Medical Statistics skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Data Science competencies
- Solve industry-relevant problems using Advanced Medical Statistics methodologies and tools
- Contribute to open-source projects and collaborative research in Data Science
- Prepare for competitive examinations, interviews, and professional certifications in Data Science
Who Should Attend & Prerequisites
- Students pursuing degrees in Data Science, science, engineering, or related disciplines
- Working professionals seeking to upskill or transition into Data Science roles
- Researchers and academicians looking to adopt modern techniques in Data Science
- Entrepreneurs, freelancers, and self-learners interested in practical Data Science knowledge
Certification
