Epidemic Preparedness Analytics using R and Tableau Training Course

Module 1: Introduction to Epidemic Preparedness

·       Basics of epidemic preparedness

·       Importance of data in epidemic response

·       Overview of R and Tableau

·       Introduction to data collection methods

·       Practical session: Data collection exercise

Module 2: Data Management with R

·       Data cleaning techniques

·       Data transformation and manipulation

·       Handling missing data

·       Importing and exporting data in R

·       Practical session: Data cleaning and manipulation using R

Module 3: Descriptive Statistics in R

·       Understanding descriptive statistics

·       Measures of central tendency

·       Measures of variability

·       Creating summary statistics in R

·       Practical session: Generating descriptive statistics with R

Module 4: Inferential Statistics in R

·       Basics of inferential statistics

·       Hypothesis testing

·       Confidence intervals

·       Regression analysis

·       Practical session: Hypothesis testing and regression in R

Module 5: Data Visualization with R

·       Principles of data visualization

·       Creating basic plots in R

·       Advanced plotting techniques

·       Customizing plots

·       Practical session: Creating visualizations with R

Module 6: Introduction to Tableau

·       Overview of Tableau interface

·       Connecting to data sources

·       Creating simple visualizations

·       Building interactive dashboards

·       Practical session: Building your first Tableau dashboard

Module 7: Advanced Tableau Visualizations

·       Advanced chart types

·       Using calculated fields

·       Creating parameter controls

·       Customizing tooltips and labels

·       Practical session: Creating advanced visualizations in Tableau

Module 8: Integrating R and Tableau

·       Benefits of integrating R with Tableau

·       Creating R scripts for Tableau

·       Using R calculations in Tableau

·       Practical applications of integration

·       Practical session: Integrating R with Tableau for advanced analytics

Module 9: Time Series Analysis in R

·       Basics of time series analysis

·       Time series decomposition

·       Forecasting techniques

·       Visualizing time series data

·       Practical session: Time series analysis with R

Module 10: Geospatial Analysis in Tableau

·       Introduction to geospatial data

·       Mapping data in Tableau

·       Using spatial functions

·       Creating interactive maps

·       Practical session: Geospatial analysis with Tableau

Module 11: Machine Learning Basics with R

·       Introduction to machine learning

·       Supervised vs. unsupervised learning

·       Building machine learning models in R

·       Evaluating model performance

·       Practical session: Building and evaluating a machine learning model

Module 12: Predictive Modeling in R

·       Introduction to predictive modeling

·       Logistic regression

·       Decision trees

·       Random forests

·       Practical session: Creating predictive models with R

Module 13: Advanced Analytics with R

·       Advanced statistical methods

·       Multivariate analysis

·       Cluster analysis

·       Principal component analysis

·       Practical session: Performing advanced analytics in R

Module 14: Tableau Server and Online

·       Overview of Tableau Server

·       Publishing dashboards to Tableau Server

·       Managing users and permissions

·       Scheduling data refreshes

·       Practical session: Publishing and managing dashboards on Tableau Server

Module 15: Data Storytelling with Tableau

·       Principles of data storytelling

·       Designing effective dashboards

·       Using storytelling features in Tableau

·       Communicating insights to stakeholders

·       Practical session: Creating a data story with Tableau

Module 16: Realworld 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 realworld 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 Wrapup and Review

·       Review of key concepts

·       Final project presentations

·       Feedback and Q&A session

·       Future learning resources

·       Practical session: Final project presentations

Requirements

•    Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588