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KCSE 2025
Data Analytics With Python & R
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About Course
Learn Data Analytics With Python & R
Course Content
Introduction: Installation of Python & R
Objectives: Install required tools, understand data structures, load datasets.
Installing R
Installing Python
R basics: variables, vectors, data frames
Python basics
Loading datasets
Practical Assignments 1: Load datasets, Explore structure, missing data, variable names
Data Cleaning & Preparation
Objectives: Clean, filter, transform, and prepare datasets.
Filtering, selecting, recoding categorical variables
Missing values
Creating new variables
Data type conversions
Practical Assignment 2: Data Cleaning
Exploratory Data Analysis
Objectives: Understand data patterns, distributions, and preliminary insights.
Summary statistics
Frequency tables
Cross-tabulation
Basic visualizations
Practical Assignment 3: Compute prevalence and Create visualizations
Data Visualization
Objectives: Adavacnced Data Visualizations
ggplot2
Python’s matplotlib visualization
Exporting high-resolution images
Practical Assignment 4: Visualize prevalence and distribution
Statistical Testing
Objectives: Apply hypothesis testing
Chi-square test
t-test
ANOVA
Fisher’s exact test
Practical Assignment 5: Test associations between infection and key variables
Logistic Regression
Objectives: Build and interpret epidemiological models.
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
Objectives: Build robust adjusted models.
Multivariate logistic regression
Controlling for confounding
Stepwise regression
Practical Assignment 7: Build final multivariate model with aOR, CI, p-values
Reporting & Interpretation
Objectives: Produce publication-ready results.
Exporting regression tables
Creating scientific graphs
Writing results in journal style
Introduction to Machine Learning
Objectives: Understand the basics of prediction models.
Train-test split
Performance metrics
Logistic regression as ML
Decision trees
Practical Assignment 9: Build first ML model predicting infection risk
Advanced ML Models
Objectives: Apply more powerful algorithms.
Random Forest
Gradient Boosting
ROC curves
Model tuning and evaluation
Practical Assignment 10: Develop and test high-performance prediction model
Automation & Reproducibility
Objectives: Create automatic reports.
R Markdown for automated reports
Jupyter Notebook templates
Export to HTML/PDF
Practical Assignment 11: Build a fully reproducible analysis report
Michael Kala Oyoya
+254785022994
michael.kala@yahoo.com
