Courses & Exercises – Omotola Ayodele Lawal

Great Tables 3 in Python: Data Color and Polishing

code

note

Posit PBC

Data Reporting

Great Tables

python

The Great Tables package is ideal for crafting beautiful, high-quality tables for reports, publications, and data presentations. Effective tables typically share three key…

Great Tables 2 in Python: Introducing Units Notation

code

note

Posit PBC

Data Reporting

Great Tables

python

Building on the foundation of the previous {GT} lesson on table structure, I advanced my skills in using the {GT} package to refine, annotate, and style tables for…

Great Tables 1 in Python: Structure, Format and Style

code

note

Posit PBC

Data Reporting

Great Tables

python

Tables are an essential tool for presenting data in a structured, clear, and visually appealing format. With python and the GT package, we can create highly customizable…

Creating Parameterized Reports with {R Markdown}

code

note

The GRAPH Course

Automation

Data Reporting

Data Visualization

R

Parameterized reporting plays a vital role in modern epidemiological workflows, enabling the dynamic generation of reports tailored to specific inputs such as geographic…

Automating Visualizations

code

note

The GRAPH Course

Automation

Data Reporting

Data Visualization

R

In many real-world data analysis tasks, especially in epidemiology, we often need to generate the same type of plots for multiple regions or indicators. In this lesson, I…

COURSE 21 | Experimental Design in Python

code

note

DataCamp

Statistic

python

Building expertise in experimental design has equipped me with the analytical tools to rigorously test hypotheses and quantify risks in my work.

Omotola Ayodele Lawal

Epidemiological Time Series Visualization

code

note

The GRAPH Course

Epidemiology

Data Reporting

Data Visualization

Timeseries

R

Understanding time series data is crucial in epidemiology—it helps uncover trends, detect disease outbreaks, and drive data-informed decisions for better health outcomes.

Optimizing gt Tables for Enhanced Visualization

code

note

The GRAPH Course

Data Reporting

Grammar of tables

R

Building on the foundation of the previous {gt} lesson — which focused on table structure and manipulation—I learned how to use the {gt} package to polish, style, and…

The GRAPH Network

Organizing Public Health Data with gt Tables in R - Basics

code

note

The GRAPH Course

Data Reporting

Grammar of tables

R

Tables are an essential tool for presenting data in a structured, clear, and visually appealing format. With R and the gt package, we can create highly customizable tables…

The GRAPH Network

Creating Choropleth maps with {ggplot2}

code

note

The GRAPH Course

Data Reporting

Epidemiology

Geospatial

Data Visualization

R

A choropleth map is a powerful thematic map where geographic regions are shaded or patterned based on the value of a specific variable. These maps are widely used in…

The GRAPH Network

Enhancing Disease Maps with Labels in R

code

note

The GRAPH Course

Data Reporting

Epidemiology

public health

Data Visualization

R

Maps are powerful storytelling tools in geospatial data visualization. However, a map without clear annotations and labels is like a book without titles or chapter…

The GRAPH Network

COURSE 20 | Hypothesis Testing in Python

code

note

DataCamp

Statistic

python

Completing the Hypothesis Testing in Python course has enhanced my understanding of the one-sample proportion test workflow. Along the way, I explored key concepts such as z-…

Omotola Ayodele Lawal

Plot Labels with ggplot2

code

note

The GRAPH Course

Data Reporting

Data Visualization

R

Bar plots are among the most versatile chart types, with numerous variations for visualizing data. In a previous lesson, I learned how to create bar plots and their circular…

The GRAPH Network

Bar Charts & Pie Charts

code

note

Data Reporting

Data Visualization

R

During this learning journey, I have developed a clear understanding of key concepts and skills for data visualization:

The GRAPH Network

Demographic Pyramids for Epidemiological Analysis

code

note

The GRAPH Course

Data Reporting

Epidemiology

Geospatial

Data Visualization

R

A demographic pyramid, also known as a population or age-sex pyramid, is a powerful tool for visualizing population distribution by age and sex.

The GRAPH Network

COURSE 19 | SAMPLING AND POINT IN PYTHON

code

note

DataCamp

Statistic

python

Completing the course on Sampling and Point Estimation in Python has provided me with a thorough understanding of key concepts and techniques in statistical sampling. Below…

Omotola Ayodele Lawal

Layers

code

note

The GRAPH Course

Geospatial

R

In this lesson, I learned to enhance ggplot maps by using the {ggspatial} package to add north arrows and scale annotations, and the {ggsflabel} package to incorporate text…

The GRAPH Network

Boundary Data

code

note

Geospatial

R

Country borders and boundaries serve multiple purposes. They can be used as a background in thematic maps, as delimiters for other spatial data, or to identify spread…

The GRAPH Network

Read and Write Shapefiles

code

note

The GRAPH Course

Geospatial

R

Completing this course equipped me with valuable skills to:

The GRAPH Network

Density Map

code

note

The GRAPH Course

Epidemiology

Geospatial

Data Visualization

R

A density map is a type of thematic map that uses colors to represent the intensity of a value. Unlike traditional maps, it doesn’t rely on predefined regions or…

The GRAPH Network

Physical Features

code

note

Geospatial

R

Understanding geographic context is essential for spatial data, as it helps in locating events in relation to environmental features like streets and landmarks.

The GRAPH Network

Physical Features

code

note

The GRAPH Course

Geospatial

Data Visualization

R

Understanding geographic context is essential for spatial data, as it helps in locating events in relation to environmental features like streets and landmarks.

The GRAPH Network

Thematic Maps

code

note

The GRAPH Course

Geospatial

Data Visualization

R

After completing this course, I have gained the following skills:

The GRAPH Network

R for GIS

code

note

The GRAPH Course

Geospatial

R

Geospatial analysis involves collecting, analyzing, and interpreting data tied to geographic locations and the characteristics of physical features or phenomena on Earth’s…

The GRAPH Network

Data Cleaning 2: Fixing Inconsistencies

code

note

The GRAPH Course

Data Cleaning

R

Building on the knowledge from the previous lesson , where I explored various functions for diagnosing data issues, completing this lesson has enabled me to:

The GRAPH Network

Data Cleaning 1: Data Diagnostics

code

note

The GRAPH Course

Data Cleaning

R

Data cleaning is the process of converting raw, “messy data” into reliable, analyzable information. This involves identifying and addressing inaccurate, incomplete, or improb…

The GRAPH Network

COURSE 18 | INTRODUCTION TO REGRESSION WITH STATSMODELS IN PYTHON

code

note

DataCamp

Statistic

python

Completing the course on Introduction to Regression with statsmodels in Python, I gained a comprehensive understanding of regression analysis and its application in Python.…

Omotola Ayodele Lawal

Lesson Notes: Intro to Functions and Conditionals

code

note

The GRAPH Course

functions

R

The R language revolves around two fundamental components: objects and functions. Objects are the data structures used to store information, while functions are the tools…

The GRAPH Network

Loops, Across, and Conditionals

code

note

The GRAPH Course

Conditional

R

Repetition is a fundamental concept in programming, often essential for automating tasks. In R, a for loop provides an efficient way to repeat a task multiple times, saving…

The GRAPH Network

Joining 2: Mismatched Values, One-to-Many & Multi-Key Joins

code

note

The GRAPH Course

Data Manipulation

Data Joining

R

Building on the knowledge gained in Part 1 of the course on joining datasets in R, this lesson enhanced my ability to work with messier and more complex datasets. I learned…

The GRAPH Network

Intro to Joining Datasets

code

note

The GRAPH Course

Data Manipulation

Data Joining

R

Joining datasets is an essential skill in health data analysis, enabling the integration of information from multiple sources for more comprehensive and insightful analyses.…

The GRAPH Network

Dates 2: Intervals, Components and Rounding

code

note

The GRAPH Course

Timeseries

R

Building on the knowledge gained in Part 1 of the course on date analysis in R, this lesson helped me strengthen my ability to perform basic date analyses. I learned…

The GRAPH Network

Dates 1: Recognizing and Formatting Dates

code

note

The GRAPH Course

Timeseries

R

Manipulating dates is a crucial skill in health data analysis, as date-related data, from patient admission dates to vaccination schedules, plays a vital role in…

The GRAPH Network

Factors in R

code

note

The GRAPH Course

Data Manipulation

R

Factors are an important data class for representing and working with categorical variables in R. Completing the course was crucial in helping me build strong foundational…

The GRAPH Network

Working with Strings in R

code

note

The GRAPH Course

Data Cleaning

Data Manipulation

R

Completing this course has made me proficient in string manipulation, a crucial skill for data scientists. Tasks such as cleaning messy data and formatting outputs rely…

The GRAPH Network Member

Fundamentals of Statistical Software & Analysis

Statistic

public health

The GRAPH Course

R

I completed the Fundamentals of Statistical Software and Analysis course, which comprises seven modules. The course is designed to teach students about statistical tests and…

The GRAPH Network

COURSE 16: WORKING WITH DATES AND TIMES IN PYTHON

code

note

DataCamp

Timeseries

python

We may never have a time machine, but we do have Python—a powerful tool for analyzing time!️

Omotola Ayodele Lawal

COURSE 15: CLEANING DATA IN PYTHON

code

note

DataCamp

Data Cleaning

python

Dirty data can appear because of duplicate values, mis-spellings, data type parsing errors and legacy systems. Without ensuring that data is properly cleaned in the…

Omotola Ayodele Lawal

Joining data with Pandas

code

note

DataCamp

Data Manipulation

Data Joining

python

The pandas package is a powerful tool for manipulating and transforming data in python. However, when working on an analytics, the data needed could be in multiple tables.…

Omotola Ayodele Lawal

Conditional Mutation

code

note

The GRAPH Course

Conditional

R

This was a quiz on the topic: Conditional Mutation. You can find the project here

Omotola Ayodele Lawal