About This Course
In an era where data is the currency of competitive advantage, the ability to analyse quantitative information and present it visually is one of the most sought-after professional skills across every sector. Python has emerged as the world's leading language for data analysis, combining unmatched flexibility with a rich ecosystem of libraries — including Pandas, NumPy, Matplotlib, Seaborn, and Plotly — that make sophisticated analysis accessible and reproducible.
This 10-day programme from TrainingComplex is designed for professionals who want to move beyond spreadsheets and develop genuine proficiency in Python-based data analysis and visualization. Participants will work through the full data pipeline: importing and cleaning messy real-world datasets, conducting descriptive and inferential statistical analysis, building interactive dashboards, and communicating findings to both technical and non-technical audiences.
The course is structured to build competency progressively, beginning with Python fundamentals before advancing to statistical modelling, regression analysis, and publication-quality data visualization. Each module is anchored in practical application, with participants working on datasets drawn from finance, public health, agriculture, monitoring and evaluation, and social development — sectors directly relevant to the African and Middle Eastern professional contexts in which TrainingComplex operates.
By the end of the programme, delegates will have completed a capstone data analysis project using their own organisational data or a provided sector-specific dataset, producing a full analytical report and visual dashboard they can immediately deploy in their professional roles.
Who Should Attend
- Data analysts and research officers seeking to upgrade from Excel to Python-based workflows
- Monitoring and evaluation professionals who need to analyse survey and programme data at scale
- Finance and accounting professionals responsible for financial modelling and reporting
- Public health and epidemiology officers handling quantitative research datasets
- Project managers and programme officers who interpret and present quantitative performance data
- Policy analysts and government statisticians requiring reproducible data workflows
- ICT officers and systems administrators expanding into data engineering roles
- Academicians, lecturers, and graduate researchers conducting quantitative studies
- Business intelligence and strategy professionals building data-driven reporting systems
Learning Objectives
- Install and configure the Python data science environment including Anaconda, Jupyter Notebooks, and key libraries
- Write clean, efficient Python code using core programming constructs relevant to data analysis tasks
- Import, clean, reshape, and merge datasets using Pandas and NumPy
- Apply descriptive statistics to summarise and explore quantitative datasets effectively
- Conduct inferential statistical tests including t-tests, chi-square, ANOVA, and correlation analysis using SciPy
- Build and interpret simple and multiple linear regression models using Statsmodels and Scikit-learn
- Create static, publication-quality charts and plots using Matplotlib and Seaborn
- Design interactive and web-ready visualizations using Plotly and Dash
- Develop structured data analysis workflows that are reproducible, documented, and shareable
- Present quantitative findings through well-structured analytical reports and visual dashboards
- Handle time series data and conduct basic trend analysis and forecasting
- Apply data analysis techniques to sector-specific use cases including finance, health, and development programming
- Complete an end-to-end capstone data analysis and visualization project
Course Modules
Module 1: Python Environment Setup and Core Programming Fundamentals
- Installing Anaconda, configuring Jupyter Notebooks, and navigating the Python data science stack
- Python syntax essentials: variables, data types, operators, and control flow
- Working with functions, loops, and list comprehensions for data processing tasks
- Introduction to Python libraries: importing, aliasing, and understanding the data science ecosystem
- Managing virtual environments and package dependencies using conda and pip for reproducible setups
- Debugging Python code: reading tracebacks, using print statements, and applying Jupyter's built-in debugging tools
Module 2: NumPy for Numerical Computing
- Creating and manipulating NumPy arrays and understanding array dimensions
- Array indexing, slicing, and reshaping for structured data access
- Performing vectorised mathematical and statistical operations efficiently
- Generating random data and understanding broadcasting rules
- Using NumPy's linear algebra functions for matrix operations and dot products relevant to data analysis
- Applying Boolean masking and fancy indexing to filter and extract array subsets conditionally
Module 3: Data Importing and Exploration with Pandas
- Reading data from CSV, Excel, JSON, and SQL sources into Pandas DataFrames
- Inspecting datasets: shape, data types, missing values, and basic summary statistics
- Indexing, filtering, and selecting subsets of data using loc and iloc
- Sorting, ranking, and grouping data to uncover initial patterns
- Using value_counts, unique, and nunique to profile categorical and discrete variables quickly
- Applying lambda functions and the apply method to create and transform DataFrame columns
Module 4: Data Cleaning and Transformation
- Identifying and handling missing values through imputation, deletion, and flagging strategies
- Removing duplicates, correcting data types, and standardising inconsistent entries
- Reshaping data using pivot tables, melt, stack, and unstack operations
- Merging, joining, and concatenating multiple datasets for integrated analysis
- Normalising and scaling numeric variables using min-max and z-score transformations to prepare data for analysis
- Encoding categorical variables with one-hot encoding and ordinal mapping for downstream statistical or modelling tasks
Module 5: Descriptive Statistics and Exploratory Data Analysis
- Computing measures of central tendency, dispersion, and distribution shape
- Generating frequency tables, cross-tabulations, and grouped summaries
- Identifying outliers using IQR, z-scores, and visual inspection methods
- Conducting systematic exploratory data analysis (EDA) with a structured analytical workflow
- Assessing variable distributions and testing for normality using Shapiro-Wilk and Q-Q plots
- Quantifying and visualising relationships between pairs of variables during EDA to guide hypothesis selection
Module 6: Inferential Statistics with SciPy
- Understanding sampling distributions, confidence intervals, and hypothesis testing logic
- Conducting one-sample, independent, and paired t-tests and interpreting p-values
- Applying chi-square tests for independence and goodness-of-fit
- Performing one-way and two-way ANOVA and post-hoc comparisons
- Using non-parametric alternatives such as Mann-Whitney U and Kruskal-Wallis tests when normality assumptions are violated
- Calculating and interpreting effect sizes (Cohen's d, eta-squared) alongside significance tests to assess practical importance
Module 7: Correlation and Regression Analysis
- Computing Pearson and Spearman correlation coefficients and testing significance
- Building simple linear regression models using Statsmodels and interpreting output
- Extending to multiple linear regression: variable selection, multicollinearity, and model diagnostics
- Introduction to logistic regression for binary outcome analysis
- Evaluating regression model performance using R-squared, RMSE, and residual diagnostic plots
- Applying feature transformation techniques such as log transformation and polynomial terms to address non-linear relationships
Module 8: Static Data Visualization with Matplotlib and Seaborn
- Creating and customising line charts, bar charts, histograms, and scatter plots with Matplotlib
- Using Seaborn for statistically informed plots: box plots, violin plots, pair plots, and heatmaps
- Designing publication-quality figures: annotations, colour palettes, gridlines, and layout control
- Choosing the right chart type for different data structures and analytical messages
- Organising multiple plots into faceted grids using Matplotlib subplots and Seaborn FacetGrid for comparative analysis
- Applying accessibility best practices in visualisation design including colour-blind-safe palettes and clear labelling conventions
Module 9: Interactive and Web-Ready Visualization with Plotly and Dash
- Building interactive charts with Plotly Express: hover tooltips, zooming, and dynamic filtering
- Creating choropleth maps and geospatial visualizations for location-based datasets
- Designing multi-panel dashboards with Plotly subplots and layout controls
- Introduction to Dash: structuring a simple interactive web dashboard with callbacks
- Adding dropdown menus, range sliders, and radio buttons as Dash components to enable user-driven data exploration
- Deploying a Dash application locally and understanding the steps for sharing it via cloud platforms or internal servers
Module 10: Time Series Data Analysis
- Parsing and indexing datetime data in Pandas for time-aware operations
- Resampling, rolling averages, and period-over-period calculations for trend analysis
- Decomposing time series into trend, seasonality, and residual components
- Basic forecasting using moving averages and introduction to Prophet or Statsmodels ARIMA
- Handling irregularly spaced time series data and filling gaps using forward-fill, backward-fill, and interpolation methods
- Detecting structural breaks and anomalies in time series data using visual and statistical approaches
Module 11: Working with Survey and Programme Data
- Importing and preparing Likert-scale and multi-choice survey data for analysis
- Weighting, stratification, and handling sampling design in programme datasets
- Computing composite indices, programme performance scores, and beneficiary disaggregation
- Visualizing M&E results: indicator dashboards and progress-against-target charts
- Conducting subgroup analysis by demographic and geographic disaggregation variables to identify equity and inclusion patterns
- Applying reliability checks such as Cronbach's alpha to assess the internal consistency of multi-item survey scales
Module 12: Reproducible Analytical Workflows and Reporting
- Structuring Python projects: directory organisation, naming conventions, and version control basics with Git
- Documenting analysis using Markdown in Jupyter Notebooks for readable, shareable reports
- Exporting reports to HTML, PDF, and Excel using nbconvert and Pandas ExcelWriter
- Automating repetitive analysis tasks with reusable Python scripts and functions
- Parameterising analysis pipelines so that scripts can be run across different datasets or time periods with minimal manual adjustment
- Writing modular, well-commented code that meets team readability standards and supports handover to colleagues
Module 13: Capstone Project: End-to-End Data Analysis and Visualization
- Defining the analytical question, selecting variables, and planning the analysis approach
- Conducting full data cleaning, statistical analysis, and model building on the capstone dataset
- Designing a visual dashboard or report that communicates findings to a non-technical audience
- Presenting the capstone project and receiving structured peer and facilitator feedback
- Documenting analytical decisions, assumptions, and limitations transparently within the capstone notebook
- Reflecting on course learning objectives and identifying areas for continued development beyond the training
Requirements & Prerequisites
Participants should have a working knowledge of basic statistics concepts such as mean, median, variance, and basic data interpretation. Familiarity with spreadsheet tools such as Microsoft Excel is expected, as the course builds on data handling intuitions already formed through such tools. No prior programming experience in Python is required, though any exposure to coding concepts will be an advantage.
Training Methodology
- Guided live coding sessions where the trainer walks through each concept step-by-step in Jupyter Notebooks, with participants coding along in real time
- Hands-on lab exercises at the end of each module using provided real-world datasets from sectors including public health, finance, agriculture, and development
- Dataset debugging challenges in which participants receive intentionally messy or incomplete datasets and must diagnose and resolve quality issues
- Peer code review sessions where small groups examine each other's scripts and offer structured feedback on logic, efficiency, and readability
- Case study analysis drawing on published data analytics projects from African and international development contexts
- Statistical interpretation workshops where participants discuss the practical meaning and limitations of computed results — not just the code output
- Visualization critique exercises in which participants evaluate chart design choices and redesign ineffective graphs using course tools
- Capstone project work sessions with dedicated facilitator coaching, where participants progressively build their end-to-end analysis project throughout the course
- Group presentation of capstone findings, simulating a real briefing to a senior management or donor audience
Organisational Benefits
- Reduces dependence on proprietary statistical software by building Python-based analytical capability in-house
- Improves the quality, speed, and reproducibility of data analysis across departments
- Enables staff to derive actionable insights from large, complex datasets that Excel cannot efficiently handle
- Strengthens the quality of monitoring and evaluation reporting with statistically sound methods
- Builds a culture of evidence-based decision-making supported by credible quantitative analysis
- Enhances donor and stakeholder confidence through professional, data-driven reporting and visualizations
- Reduces the cost of outsourcing data analysis and report production to external consultants
- Positions the organisation as a data-mature institution capable of leveraging modern analytics tools
- Creates a pipeline of internal data champions who can mentor colleagues and sustain analytical capacity
Where Is Quantitative Data Analysis and Visualization With Python Training Offered?
This course runs in-person across: Mombasa, Johannesburg, Lagos, Nakuru, Pretoria, Kisumu, Zanzibar, Abuja, Nairobi, Arusha, Dar es Salaam, Dubai, Riyadh, Addis Ababa, Cape Town, Kigali, Naivasha, Accra, Kampala, plus Live Virtual (online) cohorts. Browse live dates for your city below.
Upcoming Schedules
| Location | Format | Dates | Price |
|---|
| Mombasa, Kenya |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
KES 230,000 |
| Johannesburg, South Africa |
In-Person |
5 Oct 2026 – 16 Oct 2026 |
USD 7,000 |
| Lagos, Nigeria |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
USD 5,000 |
| Nakuru, Kenya |
In-Person |
12 Oct 2026 – 23 Oct 2026 |
KES 230,000 |
| Live Virtual |
Online |
17 Oct 2026 – 6 Dec 2026 |
KES 160,000 |
| Pretoria, South Africa |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 6,600 |
| Kisumu, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 240,000 |
| Zanzibar, Tanzania |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 5,000 |
| Abuja, Nigeria |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 5,600 |
| Nairobi, Kenya |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
KES 230,000 |
| Live Virtual |
Online |
19 Oct 2026 – 28 Oct 2026 |
KES 160,000 |
| Arusha, Tanzania |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 4,200 |
| Dar es Salaam, Tanzania |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 4,600 |
| Dubai, United Arab Emirates |
In-Person |
19 Oct 2026 – 28 Oct 2026 |
USD 8,200 |
| Riyadh, Saudi Arabia |
In-Person |
19 Oct 2026 – 30 Oct 2026 |
USD 9,000 |
+234 more dates — view live schedule →
Frequently Asked Questions
Do I need prior programming experience to attend this course?
No prior Python experience is required. The course begins from the absolute basics of Python programming and builds progressively. A comfort level with spreadsheet tools like Excel and a basic understanding of statistics will help you settle in quickly.
What software will I need, and will it be set up for me?
The course uses Python with the Anaconda distribution, which is free and open-source. Facilitators will guide all participants through a full environment setup on Day 1, so you only need to arrive with a laptop that has at least 8 GB of RAM and administrative access to install software.
Will I receive a certificate at the end of the course?
Yes. Participants who attend the full programme and complete the capstone project will receive a Certificate of Completion from TrainingComplex, confirming proficiency in Quantitative Data Analysis and Visualization with Python.
Can I bring my own organisational dataset to work with during the course?
Absolutely — and we encourage it. While TrainingComplex provides curated datasets for all exercises, participants are welcome to use anonymised data from their own work for the capstone project, making the learning directly applicable to their day-to-day roles.
Is this course suitable for someone who already uses R or SPSS for statistics?
Yes, this course is an excellent complement for professionals who already have a statistical background in R or SPSS and want to add Python to their toolkit. You will likely progress through the statistics modules quickly and will gain the most value from the Python-specific visualization and workflow automation components.
