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

Course Outline

Module 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

Module 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

Module 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

Module 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

Module 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