DATA ANALYTICS FOR MEDICAL STUDENTS
DATA ANALYTICS FOR MEDICAL STUDENTS
Introduction to Data Analytics
Data Analytics is the process of examining raw data to uncover trends, patterns, and insights that help organizations make informed decisions. By using various statistical and computational techniques, data analysts transform complex data sets into actionable knowledge. This field is essential for businesses, as it aids in improving operations, predicting future trends, and solving problems. Whether you're just starting out or looking to expand your skills, mastering data analytics opens up opportunities to drive success in a data-driven world. Join our training program to dive into the fundamentals of data analytics and learn how to turn data into valuable insights
Overview of Popular Data Analytics tool
1. Microsoft Excel
2. Tableau
3. Power BI
4. Google Analytics
5. Python
6. R
7. SPSS
8. Hadoop
Descriptive Statistics
1. Measures of Central Tendency (Mean, Median, Mode)
2. Measures of Variability (Range, Variance, Standard Deviation)
3. Data Visualization (Histograms, Box Plots, Scatter Plots)
Inferential Statistics
1. Hypothesis Testing (Z-test, T-test, ANOVA)
2. Confidence Intervals (CI)
3. Regression Analysis (Simple, Multiple, Logistic)
Probability Distributions
1. Normal Distribution
2. Binomial Distribution
3. Poisson Distribution
4. Exponential Distribution
Statistical Modeling Techniques
1. Linear Regression
2. Logistic Regression
3. Cox Proportional Hazards Model
4. Kaplan-Meier Survival Analysis
5. Receiver Operating Characteristic (ROC) Curve Analysis
Epidemiological Statistics
1. Odds Ratio (OR)
2. Relative Risk (RR)
3. Attributable Risk (AR)
4. Number Needed to Treat (NNT)
5. Incidence and Prevalence Rates
Clinical Trials Statistics
1. Intent-to-Treat (ITT) Analysis
2. Per Protocol (PP) Analysis
3. Randomization and Blinding
4. Sample Size Calculation
5. Interim Analysis and Stopping Rules
Diagnostic Test Evaluation
1. Sensitivity and Specificity
2. Positive Predictive Value (PPV) and Negative Predictive Value (NPV)
3. Likelihood Ratios
4. ROC Curve Analysis
Advanced Statistical Techniques
1. Bayesian Statistics
2. Machine Learning Algorithms (Random Forest, Support Vector Machines)
3. Generalized Linear Mixed Models (GLMM)
4. Generalized Estimating Equations (GEE)
5. Propensity Score Analysis
Medical Imaging Statistics
1. Image Segmentation and Registration
2. Image Analysis and Feature Extraction
3. Machine Learning for Image Classification
4. Statistical Parametric Mapping (SPM)
5. Functional MRI (fMRI) Analysis
These statistical models and techniques are essential for medical students to understand and apply in various medical disciplines, including
1. Clinical Research
2. Public Health
3. Epidemiology
4. Biostatistics
5. Medical Imaging
Expected Out Come
Mastering these statistical concepts will enable medical & nursing students to analyze complex medical data, interpret research findings, and make informed decisions in clinical practice.
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