Module 1: Python Fundamentals for Biomedical Data

• Setting up the Python environment (Anaconda, Jupyter Notebooks)
• Introduction to NumPy for array manipulation and numerical computing
• Variables, data types, and fundamental control flow in Python
• Writing clean, well-documented, and modular Python code
• Practical session: Writing basic Python functions to calculate standard health metrics (e.g., BMI) from raw input data

Module 2: Data Wrangling with Pandas and NumPy

• Introduction to Pandas Series and DataFrame structures for tabular data
• Reading and writing various data formats (CSV, Excel, JSON) common in biomedical research
• Data cleaning: handling missing values (imputation, dropping), duplicates, and outliers
• Data transformation: merging, joining, pivoting, and reshaping clinical datasets
• Practical session: Cleaning and standardizing a messy Electronic Health Record (EHR) dataset using Pandas

Module 3: Biostatistics and Inferential Analysis

• Review of descriptive statistics and data distributions in health contexts
• Confidence intervals and their interpretation for clinical means and proportions
• Introduction to hypothesis testing: Null and Alternative hypotheses ($H_0$ and $H_a$)
• Performing t-tests (one-sample, two-sample independent) for clinical comparisons
• Practical session: Conducting a two-sample t-test in Python to compare patient outcomes between two treatment groups

Module 4: Clinical Data Processing and EHR Analysis

• Understanding the structure of clinical data (e.g., ICD codes, CPT codes, lab results)
• Handling time-stamped data and creating clinically relevant features (e.g., time since diagnosis)
• Data reduction techniques: feature selection and dimensionality reduction for patient records
• Managing patient confidentiality and working with anonymized/synthetic data
• Practical session: Extracting, aggregating, and analyzing drug prescription and adverse event data from a simulated EHR database

Module 5: Introduction to Genomics and Bioinformatics Data

• Overview of genetic data types (DNA sequences, RNA-Seq counts, variant calls)
• Handling large tabular genomics data (e.g., gene expression matrices) with Pandas
• Basic quality control and normalization techniques for gene expression data
• Introduction to simple association analysis between genetic markers and disease
• Practical session: Loading and performing initial quality control on a gene expression matrix dataset

Module 6: Statistical Modeling for Healthcare Outcomes

• Fundamentals of Simple and Multiple Linear Regression for continuous outcomes
• Introduction to Logistic Regression for binary outcomes (e.g., disease presence, mortality)
• Interpreting odds ratios, p-values, and model coefficients in a medical context
• Evaluating model fit using metrics like $R^2$, AUC, and confusion matrices
• Practical session: Building a Logistic Regression model in Python to predict patient readmission risk

Module 7: Machine Learning for Diagnostics and Prognostics

• Supervised learning algorithms: k-Nearest Neighbors (kNN) and Support Vector Machines (SVM)
• Training and optimizing classification models using Scikit-learn
• Cross-validation techniques to prevent model overfitting in clinical prediction
• Advanced evaluation metrics: Sensitivity, Specificity, ROC curves, and Precision-Recall
• Practical session: Building and evaluating a kNN classifier for disease diagnosis based on patient features

Module 8: Survival Analysis and Time-to-Event Modeling

• Core concepts of Survival Analysis: event, censoring, and follow-up time
• Calculating and visualizing survival probabilities using Kaplan-Meier curves
• Performing the Log-Rank test to compare survival curves between groups
• Introduction to Cox Proportional Hazards Model for multivariate analysis
• Practical session: Implementing Kaplan-Meier analysis in Python to visualize patient survival differences by treatment type

Module 9: Data Visualization with Matplotlib and Seaborn

• Principles of effective biomedical data visualization (e.g., avoiding chart junk)
• Creating high-quality scatter plots, line plots, and bar plots for research reports
• Visualizing distributions using histograms, density plots, and box plots in Seaborn
• Customizing plots for publication-ready figures (colors, fonts, axis labels)
• Practical session: Creating publication-quality visualizations of patient demographics and outcomes using Matplotlib and Seaborn

Module 10: Geospatial and Epidemiology Data Analysis

• Handling population-level health data and calculating incidence/prevalence rates
• Introduction to geographic data and creating basic health maps (choropleths) in Python
• Visualizing disease spread and public health intervention effects over time
• Identifying spatial clusters of disease using simple visual techniques
• Practical session: Analyzing and visualizing a public health dataset to show disease distribution across geographic regions

Module 11: Power BI Desktop Fundamentals and Data Loading

• Understanding the Power BI ecosystem (Desktop, Service, Gateway)
• Connecting to various biomedical data sources (Excel, CSV, SQL databases, web APIs)
• Best practices for folder organization and file naming conventions
• Initial data inspection and profiling within Power BI
• Practical session: Connecting Power BI to multiple simulated clinical data files and establishing initial data connections

Module 12: Data Transformation with Power Query (M Language)

• Using the Power Query Editor for data cleaning and preparation
• Common transformation steps: unpivoting, splitting columns, merging queries
• Creating custom columns and conditional logic using the M language
• Optimizing query performance and handling large datasets
• Practical session: Performing advanced data cleaning and reshaping of clinical trial data using Power Query

Module 13: Designing Robust Data Models in Power BI

• Understanding dimensional modeling: Facts and Dimensions
• Creating and managing table relationships (one-to-many, many-to-many)
• Optimizing data models for performance and reporting flexibility (star schema)
• Hiding unnecessary columns and structuring fields for end-user clarity
• Practical session: Building a robust Star Schema data model based on clinical patient records and lab results

Module 14: Introduction to DAX (Data Analysis Expressions)

• Understanding calculated columns versus measures in DAX
• Fundamental DAX functions (SUM, AVERAGE, CALCULATE, FILTER)
• Time intelligence functions for analyzing trends (YTD, previous period comparison)
• Creating key performance indicators (KPIs) relevant to healthcare (e.g., Infection Rate)
• Practical session: Writing core DAX measures to calculate hospitalization rates, average length of stay, and patient mortality

Module 15: Advanced Power BI Visualizations for Healthcare

• Utilizing custom visuals and marketplace visuals relevant to medical data (e.g., Gantt charts for trials)
• Advanced filtering, drill-through, and bookmarking for dynamic reporting
• Effective use of conditional formatting and visual cues to highlight critical health metrics
• Choosing the right chart type for different data types (e.g., waterfall for cost analysis)
• Practical session: Creating a complex clinical cohort comparison report using multiple interactive visuals

Module 16: Creating Interactive Dashboards for Healthcare Performance

• Principles of effective dashboard design and storytelling with data
• Structuring a narrative flow within a multi-page Power BI report
• Implementing row-level security (RLS) for data access control (conceptual overview)
• Best practices for publishing reports to the Power BI Service
• Practical session: Designing and publishing a comprehensive, interactive healthcare performance dashboard for executive review

Module 17: Integrating Python Scripts and Models into Power BI

• Setting up Python integration within Power BI Desktop
• Using Python scripts directly in Power Query for advanced data preparation
• Utilizing Python visuals (e.g., Matplotlib/Seaborn) to display specialized plots within Power BI
• Integrating machine learning model scores (e.g., readmission risk) into the Power BI data model
• Practical session: Integrating a Python script to perform custom data normalization and display the output in a Power BI visual

Module 18: Ethical AI, Data Governance, and Deployment

• Review of data governance policies (e.g., HIPAA, GDPR, institutional requirements)
• Identifying and mitigating bias in biomedical data and machine learning models
• Strategies for documentation, version control, and model lineage tracking
• Best practices for model deployment and continuous monitoring in a production environment
• Practical session: Developing a checklist for ethical and compliance review of a predictive healthcare dashboard before final deployment

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