Interactive Data Dashboards with Python Training Course
Interactive Data Dashboards with Python Training Course is designed to empower learners with the expertise to build dynamic, user-friendly dashboards using cutting-edge Python tools such as Dash and Streamlit.
Skills Covered

Course Overview
Interactive Data Dashboards with Python Training Course
Introduction
In today’s fast-paced data-driven world, the ability to visualize and interpret data efficiently is a critical skill. Interactive Data Dashboards with Python Training Course is designed to empower learners with the expertise to build dynamic, user-friendly dashboards using cutting-edge Python tools such as Dash and Streamlit. With a focus on interactivity, real-time updates, and deployment capabilities, this course bridges the gap between raw data and actionable insights for analysts, developers, and decision-makers.
This hands-on training blends data visualization, web development, and analytical thinking into one powerful skillset. Whether you're an aspiring data scientist, a business analyst seeking to enhance reporting, or a developer aiming to integrate live analytics, this course delivers job-ready skills using trending technologies. By completing this program, learners will be equipped to design and deploy production-ready dashboards, transforming complex datasets into impactful visual stories.
Course Objectives
- Master the fundamentals of Dash and Streamlit for building Python dashboards
- Create interactive and real-time data visualizations using Plotly and Altair
- Integrate Python dashboards with live data sources such as APIs and databases
- Design responsive UI/UX layouts for web-based dashboards
- Implement user-driven components like dropdowns, sliders, and filters
- Apply best practices for structuring scalable dashboard applications
- Customize themes and branding for enterprise-ready solutions
- Deploy dashboards on Heroku, Streamlit Cloud, and other platforms
- Perform data preprocessing and aggregation with pandas and NumPy
- Embed ML model results and predictions into dashboards
- Implement role-based access control and authentication
- Analyze and optimize dashboard performance and load time
- Apply case-based thinking through real-world project examples
Target Audiences
- Data Scientists and Analysts
- Business Intelligence Professionals
- Python Developers
- Web Developers transitioning into data roles
- Statisticians and Researchers
- IT Professionals and Engineers
- Marketing and Sales Analysts
- University Students and Academic Researchers
Course Duration: 5 days
Course Modules
Module 1: Introduction to Interactive Dashboards
- Importance of dashboards in data storytelling
- Overview of Dash vs. Streamlit
- Use cases in various industries
- Core architecture and how they work
- Installation and environment setup
- Case Study: Comparing dashboards for sales reporting
Module 2: Data Manipulation with Python
- Introduction to pandas and NumPy
- Data cleaning and wrangling techniques
- Handling missing data and outliers
- Creating aggregated views and KPIs
- Efficient data pipelines for dashboards
- Case Study: Cleaning e-commerce data for dashboard use
Module 3: Visual Components in Dash
- Using Plotly for interactive charts
- Building dynamic layouts and themes
- Integrating widgets like dropdowns and sliders
- Callback functions and interactivity
- Real-time data update techniques
- Case Study: Sales monitoring dashboard with live updates
Module 4: Visual Components in Streamlit
- Streamlit widgets and layout options
- Charting with Altair and Matplotlib
- Uploading and displaying custom data
- Caching and performance optimization
- Sidebar navigation and user input
- Case Study: Financial analysis dashboard for investors
Module 5: Building User-Centric Dashboards
- UI/UX design principles for dashboards
- Creating responsive, mobile-friendly interfaces
- Multi-page dashboards and navigation
- Accessibility and design consistency
- Collecting and using user feedback
- Case Study: HR dashboard for employee analytics
Module 6: Embedding Machine Learning into Dashboards
- Overview of ML integration with dashboards
- Displaying prediction outputs dynamically
- Visualizing model metrics (accuracy, confusion matrix)
- Model retraining and input interfaces
- Risk and performance dashboards for ML
- Case Study: Churn prediction dashboard with ML insights
Module 7: Deployment and Hosting
- Exporting projects for deployment
- Hosting with Streamlit Cloud, Heroku, and Render
- Continuous integration and updates
- Docker and cloud containerization basics
- Troubleshooting deployment issues
- Case Study: Publishing a COVID-19 analytics dashboard
Module 8: Final Project and Capstone
- Project planning and scope definition
- Dataset selection and use case development
- Design, build, and test a full dashboard
- Peer review and feedback sessions
- Presentation and documentation best practices
- Case Study: Custom dashboard based on learner’s industry
Training Methodology
- Hands-on coding exercises using Jupyter and IDEs
- Live interactive sessions with real-time examples
- Mini projects and quizzes at the end of modules
- Case-based discussions and collaborative problem solving
- Final capstone project presentation and certification
- Access to post-training resources and code templates
Register as a group from 3 participants for a Discount
Send us an email: info@datastatresearch.org or call +254724527104
Certification
Upon successful completion of this training, participants will be issued with a globally- recognized certificate.
Tailor-Made Course
We also offer tailor-made courses based on your needs.
Key Notes
a. The participant must be conversant with English.
b. Upon completion of training the participant will be issued with an Authorized Training Certificate
c. Course duration is flexible and the contents can be modified to fit any number of days.
d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.
e. One-year post-training support Consultation and Coaching provided after the course.
f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.