Installing R - KALA

Data Analytics With Python & R Installing R

Data Analytics With Python & R

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Introduction: Installation of Python & R

Installing Python

R basics: variables, vectors, data frames

Loading datasets

Practical Assignments 1: Load datasets, Explore structure, missing data, variable names

Data Cleaning & Preparation

Filtering, selecting, recoding categorical variables

Creating new variables

Data type conversions

Practical Assignment 2: Data Cleaning

Exploratory Data Analysis

Summary statistics

Frequency tables

Cross-tabulation

Basic visualizations

Practical Assignment 3: Compute prevalence and Create visualizations

Data Visualization

Python’s matplotlib visualization

Exporting high-resolution images

Practical Assignment 4: Visualize prevalence and distribution

Statistical Testing

Chi-square test

Fisher’s exact test

Practical Assignment 5: Test associations between infection and key variables

Logistic Regression

Logistic regression in R

Logistic regression in Python

Calculating odds ratios

Model diagnostics

Practical Assignment 6: Build model predicting infection using risk factors

Multivariate Analysis

Multivariate logistic regression

Controlling for confounding

Stepwise regression

Practical Assignment 7: Build final multivariate model with aOR, CI, p-values

Reporting & Interpretation

Exporting regression tables

Creating scientific graphs

Writing results in journal style

Introduction to Machine Learning

Train-test split

Performance metrics

Logistic regression as ML

Practical Assignment 9: Build first ML model predicting infection risk

Advanced ML Models

Gradient Boosting

Model tuning and evaluation

Practical Assignment 10: Develop and test high-performance prediction model

Automation & Reproducibility

R Markdown for automated reports

Jupyter Notebook templates

Export to HTML/PDF

Practical Assignment 11: Build a fully reproducible analysis report

Announcements Course Info