Module 1: Introduction to Epidemiology Analytics

·       Basics of epidemiology analytics

·       Importance of data in epidemiology

·       Overview of Python and Power BI

·       Introduction to data collection methods

Practical session: Data collection exercise

Module 2: Data Management with Python

·       Data cleaning techniques

·       Data transformation and manipulation

·       Handling missing data

·       Importing and exporting data in Python

Practical session: Data cleaning and manipulation using Python

Module 3: Descriptive Statistics in Python

·       Understanding descriptive statistics

·       Measures of central tendency

·       Measures of variability

·       Creating summary statistics in Python

Practical session: Generating descriptive statistics with Python

Module 4: Inferential Statistics in Python

·       Basics of inferential statistics

·       Hypothesis testing

·       Confidence intervals

·       Regression analysis

Practical session: Hypothesis testing and regression in Python

Module 5: Data Visualization with Python

·       Principles of data visualization

·       Creating basic plots in Python

·       Advanced plotting techniques

·       Customizing plots

Practical session: Creating visualizations with Python

Module 6: Introduction to Power BI

·       Overview of Power BI interface

·       Connecting to data sources

·       Creating simple visualizations

·       Building interactive dashboards

Practical session: Building your first Power BI dashboard

Module 7: Advanced Power BI Visualizations

·       Advanced chart types

·       Using calculated fields

·       Creating parameter controls

·       Customizing tooltips and labels

Practical session: Creating advanced visualizations in Power BI

Module 8: Integrating Python and Power BI

·       Benefits of integrating Python with Power BI

·       Creating Python scripts for Power BI

·       Using Python calculations in Power BI

·       Practical applications of integration

Practical session: Integrating Python with Power BI for advanced analytics

Module 9: Time Series Analysis in Python

·       Basics of time series analysis

·       Time series decomposition

·       Forecasting techniques

·       Visualizing time series data

Practical session: Time series analysis with Python

Module 10: Geospatial Analysis in Power BI

·       Introduction to geospatial data

·       Mapping data in Power BI

·       Using spatial functions

·       Creating interactive maps

Practical session: Geospatial analysis with Power BI

Module 11: Machine Learning Basics with Python

·       Introduction to machine learning

·       Supervised vs. unsupervised learning

·       Building machine learning models in Python

·       Evaluating model performance

Practical session: Building and evaluating a machine learning model

Module 12: Predictive Modeling in Python

·       Introduction to predictive modeling

·       Logistic regression

·       Decision trees

·       Random forests

Practical session: Creating predictive models with Python

Module 13: Advanced Analytics with Python

·       Advanced statistical methods

·       Multivariate analysis

·       Cluster analysis

·       Principal component analysis

Practical session: Performing advanced analytics in Python

Module 14: Power BI Service and Online

·       Overview of Power BI Service

·       Publishing dashboards to Power BI Service

·       Managing users and permissions

·       Scheduling data refreshes

Practical session: Publishing and managing dashboards on Power BI Service

Module 15: Data Storytelling with Power BI

·       Principles of data storytelling

·       Designing effective dashboards

·       Using storytelling features in Power BI

·       Communicating insights to stakeholders

Practical session: Creating a data story with Power BI

Module 16: Real-world Case Studies

·       Case study 1: Epidemic outbreak analysis

·       Case study 2: Vaccination campaign monitoring

·       Case study 3: Health resource allocation

·       Case study 4: Disease surveillance

Practical session: Analyzing real-world case studies

 

Module 17: Best Practices in Data Analysis

·       Data quality management

·       Ethical considerations in data analysis

·       Reporting and documentation

·       Continuous improvement in data practices

Practical session: Implementing best practices in a project

Module 18: Course Wrap-up and Review

·       Review of key concepts

·       Final project presentations

·       Feedback and Q&A session

·       Future learning resources

Practical session: Final project presentations

Trainer Experience

Our trainers are seasoned professionals with extensive experience in public health, data analysis, and visualization. They have worked on numerous projects involving epidemiology analytics and have a deep understanding of both Python and Power BI.

Quality Statement

We are committed to providing high-quality training that meets the needs of our participants. Our courses are designed to be practical, engaging, and relevant to current industry practices.

Tailor-made Courses

We offer tailor-made courses to meet the specific needs of organizations. Contact us to discuss your requirements, and we will design a course that fits your needs.

Payment Information

Payment is due a week before the training starts. Contact us for more details.

Accommodation and Airport Pick-up

We provide accommodation and airport pick-up for participants coming from outside the city. Let us know your travel arrangements, and we will make the necessary arrangements.