Course Details
AI for Data Analytics and Predictive Insights
This intensive hands-on workshop empowers participants to leverage AI for data analytics and predictive insights, enabling actionable intelligence from business data. Participants will learn to clean, process, analyze, and visualize data, build predictive models, and create dashboards for decision-making. Emphasis is on practical exercises and real-world projects, allowing participants to develop AI-powered solutions for forecasting trends, detecting patterns, and generating actionable insights applicable to finance, operations, marketing, and customer service. By the end of the workshop, attendees will gain the confidence and hands-on experience to apply AI for predictive decision-making, improve business performance, and anticipate future risks and opportunities.
Learning Outcomes:
Course Outline:
AI in business analytics: benefits and challenges
Predictive vs descriptive vs prescriptive analytics
Case studies across finance, marketing, and operations
Overview of AI analytics tools: ChatGPT, Microsoft Co-Pilot, Google Gemini AI, Tableau, Power BI, n8n
Account setup, platform navigation, and environment configuration
Understanding data types: structured vs unstructured
Cleaning, normalizing, and transforming datasets
Handling missing and inconsistent data
Prepare a sample business dataset for AI analysis
Explore basic data cleaning techniques
Exploratory data analysis using AI
Detecting patterns, anomalies, and correlations
Feature selection for predictive modeling
Creating interactive dashboards
Visualizing trends, KPIs, and forecasts
Automating reports for decision-makers
Automating data ingestion, processing, and reporting
Integrating AI with corporate tools (Excel, Google Workspace, Slack, Teams)
Build an AI-powered analytics dashboard
Automate data collection and reporting workflow
Regression, classification, and time-series forecasting
Model selection and evaluation metrics
Understanding AI model outputs
Applying predictive models to real-world business cases
What-if analysis using AI
Risk assessment and opportunity identification
Build a predictive model for sales, customer churn, or financial forecasting
Evaluate model accuracy and refine predictions
Generate actionable recommendations based on insights
Improving model performance
Integrating predictive outputs into dashboards and workflows
Identify a real workplace dataset or challenge
Apply AI tools to clean, analyze, visualize, and generate predictive insights
Build a deployable dashboard or report for decision-making
Scaling AI analytics solutions across departments
Monitoring performance and ROI of AI models
Responsible AI practices and ethical considerations
Complete and present capstone project
Peer review and facilitator feedback
Develop a deployment roadmap for real-world application
Review and discussion of tools and concepts covered.
Q&A session to address any questions.
Let’s Upskill Teams Together.
