Data Analytics

Turning data into insights that support practical, people-centered decisions.

My Analytical Workflow

I use a clear, repeatable process: collect the right data, clean it carefully, explore with Python and visualization tools, communicate findings clearly, and connect insights to decisions that affect people and programmes.

Data Collection

Gather data from forms, programmes, surveys, and operational sources with clear purpose.

Data Cleaning

Handle missing values, inconsistencies, and structure data for reliable analysis.

Exploratory Analysis

Use Python (Pandas) and visualization tools to understand patterns, distributions, and relationships.

Visualization & Storytelling

Communicate findings clearly with Matplotlib, Seaborn, Excel, and Power BI.

Recommendations & Impact

Translate insights into practical recommendations that support people-centered decisions.

Tools I Use

PythonPandasNumPyMatplotlibSeabornExcelPower BIGoogle Colab

Case Studies

Documented analytics work from programme and community contexts. Structure is designed so new case studies (including Colab notebooks and public repositories) can be added easily. Confidential organisational data is not published.

Application Analysis & Programme Selection Support

Problem

Need to process and analyze a large volume of programme applications to support fair, data-informed selection of participants for digital literacy and related programmes.

Context

Work at Swahilipot Hub Foundation supporting digital literacy initiatives, applicant and programme data processing, and data-driven programme support.

Data Source

Programme application forms and related applicant data

Tools

Python, Pandas, Excel, Power BI

Process

· Collected and consolidated application data
· Cleaned and preprocessed records
· Performed exploratory data analysis
· Supported selection processes with structured insights
· Produced impact reporting and dashboards

Key Findings

· Analyzed 495+ applications
· Supported selection of approximately 180 participants

Impact

Enabled more structured, data-informed selection and reporting for digital literacy and community programmes.

Django Girls Mombasa: Data Output

Problem

Support data-related needs and produce a shareable presentation or final output linked to Django Girls Mombasa and Pure Pearl Foundation work.

Context

Data and technical contribution in the context of Django Girls Mombasa and Pure Pearl Foundation, focused on outcomes that can be shared publicly.

Data Source

Programme and event-related data (non-confidential summaries)

Tools

Python, Excel, Power BI, Presentation tools

Process

· Structured relevant data for the event / programme context
· Prepared visualizations and summary findings
· Produced a presentation or final documented output

Key Findings

· Delivered a completed data-related presentation / output for the event context

Salu Solutions : Analytical Work

Problem

Conduct data analysis in support of business and operational questions while keeping confidential source data private.

Context

Data analysis work associated with Salu Solutions. Only high-level process and authorised insights are presented publicly.

Data Source

Internal operational data (not published)

Tools

Python, Pandas, Excel, Power BI

Process

· Defined analysis questions with stakeholders
· Cleaned and explored available data
· Built visualizations and summarised findings
· Communicated insights without exposing confidential records

Key Findings

· Produced analysis and reporting outputs for internal use