All training courses can be modified to suit the needs of extension services in form of Training of Trainers (ToT), or directly to the beneficiaries
About the Course
Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. This 5-day intensive, in-person training is designed to equip Inter-agency Collaboration (IAC) technical representatives with practical skills in data analytics and visualization using Python. Through a hands-on, step-by-step approach, participants will learn how to prepare, clean, and analyze datasets using Pandas; create static visualizations with Matplotlib and Seaborn; and develop interactive dashboards using Plotly. The course will also cover Exploratory Data Analysis (EDA), basic hypothesis testing, and the integration of visualizations into data-driven reports for decision-making and policy input. By the end of the program, participants will have the knowledge, tools, and confidence to transform raw data into actionable insights that enhance performance, accountability, and evidence-based planning.
Target Participants
This course is ideal for technical professionals, analysts, and decision-makers—seeking to build practical skills in data analytics and visualization using Python.
What you will learn
By the end of the course the learner should be able to:
- Explain the role of data analytics and visualization in enhancing performance, policy input, and accountability.
- Set up a Python environment and run basic scripts for data manipulation and analysis.
- Apply core Python concepts—data types, structures, control flow, and functions—to solve data tasks.
- Use Pandas to clean, transform, merge, and aggregate datasets to meet quality standards.
- Create and customize static visualizations using Matplotlib and Seaborn.
- Develop interactive visualizations with Plotly for dynamic and user-friendly outputs.
- Conduct Exploratory Data Analysis (EDA) to detect patterns, anomalies, and trends.
- Perform basic hypothesis testing to validate analytical findings.
- Integrate analysis and visualizations into a coherent, data-driven report or dashboard.
- Complete a capstone project applying all learned skills to a real or simulated dataset.
Course duration
5 days
TOPICS TO BE COVERED:
Day 1: Introduction to Data Analytics and Python Programming
- Overview of data analytics for decision-making and accountability
- Setting up the Python environment (Anaconda, Jupyter Notebook)
- Python programming basics: syntax, variables, data types, operators
- Data structures in Python: lists, tuples, dictionaries, and sets
- Control structures: loops and conditionals
- Hands-on exercises: writing simple Python scripts
Day 2: Data Wrangling and Preparation
- Introduction to Pandas for data manipulation
- Importing data from CSV, Excel, and databases
- Cleaning data: handling missing values, duplicates, and outliers
- Transforming and merging datasets
- Aggregation, filtering, and summary table techniques
- Hands-on practice using real or simulated datasets
Day 3: Static Data Visualization with Matplotlib and Seaborn
- Principles of effective data visualization
- Creating histograms, bar charts, and scatter plots in Matplotlib
- Enhancing visual appeal with labels, legends, and color schemes
- Using Seaborn for advanced statistical visualizations
- Customizing plots for reports and presentations
- Practical exercises: generating and customizing visual outputs
Day 4: Interactive Visualizations and Exploratory Data Analysis (EDA)
- Introduction to interactive visualization with Plotly
- Building line charts, heat maps, and interactive dashboards
- Exploratory Data Analysis concepts and techniques
- Using descriptive statistics to summarize datasets
- Identifying patterns, correlations, and anomalies
- Guided EDA project on provided datasets
Day 5: Hypothesis Testing, Integration, and Final Project
- Introduction to hypothesis testing: concepts, t-tests, and chi-square tests
- Applying statistical tests to real-world datasets
- Integrating analysis and visualization for storytelling with data
- Participants’ capstone project: analyze a dataset and create interactive visualizations
- Group presentations and peer feedback
- Wrap-up: lessons learned, additional resources, and post-training action plans
Course LeaderDr. Moses Gweyi. LinkedIn: mgweyi LinkedIn: mgweyi