Introduction

Python has become one of the most widely used languages in healthcare research and medical informatics. From analyzing patient data and visualizing disease trends to supporting AI-based diagnostics, Python is transforming how health professionals handle data.

This workshop provides a hands-on, structured approach to learning Python fundamentals and applying them to real-world medical and research datasets.

• Duration: 3-day intensive workshop
• Format: Hands-on learning with practical exercises
• Schedule: October 2025
• Delivery: Interactive sessions with live coding demonstrations

• Handle Data Effectively: Organize and manage patient or laboratory data using Python’s built-in structures
• Perform Data Analysis: Use pandas to explore health-related datasets and compute descriptive statistics
• Create Visualizations: Generate charts for patient outcomes, clinical trials, or epidemiological trends
• Perform Statistical Tests: Apply tests such as t-test, ANOVA, chi-square, and logistic regression tests to biomedical datasets
• Build a Foundation: Prepare for advanced applications like biostatistics, machine learning, and clinical decision support

• Read and process data from various file formats
• Filter and query datasets to extract meaningful insights
• Perform statistical tests (t-tests, ANOVA, chi-square, logistic regression)
• Create professional data visualizations
• Build a foundation for advanced Python applications

• Students in the Davao Medical School Foundation, Inc.
• Complete beginners with no prior programming experience
• Professionals looking to add Python skills to their toolkit
• Researchers who need to analyze data programmatically

• No programming experience required - we start from the very basics
• Basic computer literacy (file management, using applications)
• Willingness to learn and practice hands-on coding
• A laptop with internet connection for installation and practice

• Medical professionals aiming to integrate data science into clinical practice or research
• Medical students preparing for coursework or electives involving biostatistics, informatics, or computational tools
• Clinicians and researchers seeking to enhance decision-making through data-driven approaches
• Healthcare professionals curious about programming, data analysis, and their applications in medicine

• Confidently write basic to intermediate Python programs
• Understand and apply fundamental programming concepts
• Perform basic data manipulation and analysis tasks
• Create simple visualizations from datasets

• Data-Driven Insight: Confidently explore and interpret clinical or survey data
• Research Independence: Perform your own data cleaning and analysis for publications
• Problem-Solving: Develop computational thinking skills
• Foundation Building: Prepared for advanced topics like machine learning
• Automation in Practice: Streamline repetitive workflows, such as summarizing lab data or generating reports

By workshop completion, you’ll have created:

• A personal library of useful Python functions
• Mini-projects analyzing sample clinical datasets (e.g., patient demographics, vital signs, or lab results)
• Solutions to data-driven questions often encountered in healthcare and research
• A foundation for continued learning and development

• Python basics and development environment setup
• Variables, data types, and basic operations
• Introduction to programming logic

• Control structures and functions
• Working with modules and handling errors
• Hands-on practice with increasingly complex problems

• Data analysis with pandas
• Statistical analysis and visualization
• Capstone project and wrap-up

• Comprehensive understanding of Python fundamentals
• Experience with industry-standard data analysis tools
• Portfolio of completed programming projects
• Confidence to tackle new programming challenges

• Complete workshop materials and code examples
• Recommended resources for further study
• Clear pathways for advancing your Python skills

Participants who complete all workshop activities will receive a Certificate of Completion in “Data Analysis in Python for Complete Beginners,” issued by the Center for Research and Development, DMSFI.

• Variable – A named container used to store data values in a program.
• Data Type – The classification of data that tells Python what kind of value is being stored (e.g., integer, float, string, boolean).
• List – An ordered, mutable collection of items (e.g., [10, 20, 30]).
• Dictionary – A collection of key-value pairs (e.g., {'name': 'John', 'age': 30}).
• Function – A reusable block of code that performs a specific task.
• Module / Library – A collection of pre-written Python functions and tools (e.g., numpy, pandas, matplotlib).
• DataFrame – A two-dimensional, labeled data structure in pandas similar to an Excel spreadsheet, with rows and columns.
• Series – A one-dimensional labeled array (e.g., a single column of a DataFrame).
• Control Structures – Logical structures that determine the order in which code executes, such as loops (for, while) and conditionals (if, else).

• Querying – Extracting specific subsets of data based on certain criteria or conditions.
• Filtering – Selecting rows or columns in a dataset that meet a particular condition (e.g., heart rate greater than 100 BPM).
• Indexing – Accessing data using row or column labels or positions.
• iloc – Index-based selection (integer position).
• loc – Label-based selection (using column or row names).
• Boolean Masking – Using logical conditions (True/False) to filter data.
• isin() Function – Filters rows where a column’s value matches any in a given list.
• String Operations – Text-based filters or transformations (e.g., str.contains() to find names with a certain substring).
• query() Method – A more readable way to filter data using string expressions (e.g., df.query('age > 30 and heart_rate > 100')).

• Grouping (groupby) – Combining rows with the same value in one or more columns to perform summary calculations.
• Aggregation – Calculating summary statistics such as mean, median, count, or standard deviation for each group.
• Custom Aggregation – User-defined summary functions applied to groups (e.g., calculating body mass index).
• Sorting – Arranging rows in ascending or descending order based on column values.
• Chained Operations – Combining multiple DataFrame methods in a single line (e.g., filtering → sorting → selecting).

• Matplotlib – A powerful Python library for creating static, 2D plots like histograms, scatter plots, and bar charts.
• Seaborn – A visualization library built on Matplotlib that simplifies statistical plotting with cleaner aesthetics.
• Figure – The entire plotting area that may contain one or more subplots.
• Axes / Subplot – Individual plots within a figure.
• Histogram – Displays the frequency distribution of a numeric variable.
• Box Plot – Visualizes data spread and detects outliers using quartiles.
• Scatter Plot – Shows relationships or correlations between two numeric variables.
• Bar Plot – Represents categorical data using rectangular bars.
• Violin Plot – Combines box plot and density plot to show data distribution.
• Heatmap – Displays a color-coded correlation matrix or table of values.
• Pie Chart – Represents proportions of categories in a circular chart.
• Legend – A key that identifies what each color or symbol represents in a plot.
• Regression Line – A line that shows the general trend in scatter plot data (e.g., using sns.regplot).

• Statistics – The branch of mathematics dealing with the collection, analysis, interpretation, and presentation of data.
• Descriptive Statistics – Summarize data features without drawing conclusions beyond the data itself.
• Inferential Statistics – Use sample data to make generalizations about a larger population.

🔹 Descriptive Measures

• Mean – The arithmetic average.
• Median – The middle value when data are sorted.
• Mode – The most frequently occurring value.
• Range – Difference between the maximum and minimum values.
• Standard Deviation (SD) – Measures how spread out the data are from the mean.
• Variance – Square of the standard deviation.
• Interquartile Range (IQR) – Difference between the 75th and 25th percentiles.

• Null Hypothesis (H₀) – The default assumption that there is no effect or difference.
• Alternative Hypothesis (H₁) – States that we fail to reject the null hypothesis.
• p-value – Probability of observing the data (or more extreme) assuming H₀ is true.
• Significance Level (α) – Threshold (commonly 0.05) used to decide whether to reject H₀.
• Type I Error – Incorrectly rejecting a true null hypothesis (false positive).
• Type II Error – Failing to reject a false null hypothesis (false negative).

• T-test – Compares means between groups.
  • One-sample t-test: compares sample mean to a known population mean.
  • Independent (two-sample) t-test: compares means of two independent groups.
  • Paired t-test: compares means of the same group before and after a treatment.
• ANOVA (Analysis of Variance) – Tests differences among means of three or more groups.
  • One-way ANOVA: compares one factor across multiple groups.
  • Two-way ANOVA: examines the effect of two factors simultaneously.
  • Post-hoc tests: pairwise comparisons following a significant ANOVA result.
• Chi-Square Test – Tests relationships between categorical variables.
  • Test of Independence: determines if two categorical variables are related. This is usually the case.
  • Goodness of Fit: checks if observed data match expected distributions. This is when we have a theoretical distribution or percentages.

• Correlation – Measures the strength and direction of a linear relationship between two variables (range: -1 to +1).
  • Positive correlation: variables increase together.
  • Negative correlation: one increases while the other decreases.
• Pearson’s r – The most common correlation coefficient for continuous variables.
• Regression Analysis – Predicts the value of a dependent variable based on one or more independent variables.
• Simple Linear Regression – Uses one predictor variable to predict an outcome.
  • Slope: change in outcome for every one-unit increase in predictor.
  • Intercept: predicted outcome when the predictor is zero.
  • R-squared: proportion of variance in the dependent variable explained by the independent variable.
• Logistic Regression – Predicts binary outcomes (e.g., yes/no, 0/1) using continuous or categorical predictors.
  • Odds Ratio: how much the odds of the outcome change with a one-unit increase in the predictor.
  • Confusion Matrix: table showing correct and incorrect predictions.
  • Accuracy: proportion of correct predictions.

• Train-Test Split – Dividing data into training (for model building) and testing (for evaluation).
• Random Seed – Ensures reproducibility by fixing random number generation.
• Feature – An independent variable or predictor in a dataset.
• Target Variable – The dependent variable or outcome being predicted.
• Normalization / Scaling – Adjusting data values to a common scale without distorting differences.

1. To download Python, go to https://www.python.org/downloads/ and click ‘Download Python x.xx.x’. There are separate download options depending on your operating system (OS), example, Windows, macOS, Linux, etc. Double-click the downloaded file and follow the default options for installation.
2. To download Anaconda, go to https://www.anaconda.com/download/success and click one of the options under “Distribution Installers”. Once download is finished, double-click the downloaded file and follow the default options for installation.
   • Anaconda is a free and open-source distribution of the Python and R programming languages for scientific computing. It simplifies package management and deployment, making it the standard choice for data science.

Once installed, open Anaconda Navigator, find Jupyter Notebook, and click Launch — you’re ready to code!