Duration 4 Days – 28 hrs.

 

Overview

The AI-Powered Data Analytics Training Course equips participants with the knowledge and practical skills needed to analyze, visualize, and interpret data using artificial intelligence and modern analytics tools.

The course covers the complete analytics workflow, from data collection and preparation to exploratory analysis, dashboard development, forecasting, and AI-assisted reporting. Participants will learn how generative AI can help create formulas, queries, analytical summaries, visualizations, and data-driven recommendations.

The course also emphasizes data accuracy, privacy, responsible AI use, and the importance of human validation when working with AI-generated results.

 

Objectives

• Explain the role of AI, machine learning, and generative AI in data analytics
• Understand descriptive, diagnostic, predictive, and prescriptive analytics
• Identify appropriate business applications for AI-powered analytics
• Collect, clean, transform, and prepare data for analysis
• Use AI to assist with spreadsheet formulas, queries, and analytical workflows
• Perform exploratory data analysis
• Identify trends, patterns, relationships, and anomalies
• Define meaningful business metrics and KPIs
• Create effective charts, reports, and interactive dashboards
• Apply basic forecasting and predictive analytics techniques
• Write effective prompts for analytical tasks
• Generate AI-assisted summaries and business recommendations
• Validate AI-generated formulas, analyses, and conclusions
• Present analytical findings through effective data storytelling
• Apply responsible AI, data privacy, security, and governance practices

 

 Target Audience 

• Data analysts and junior data professionals
• Business analysts
• Reporting and management information system personnel
• Finance and accounting professionals
• Sales and marketing analysts
• Operations and supply chain personnel
• Human resources professionals
• Project managers and team leaders
• IT professionals supporting data and analytics initiatives
• Managers and supervisors who work with business data
• Employees who regularly prepare spreadsheets, reports, and dashboards

 

Prerequisites 

• Basic computer proficiency
• Basic knowledge of spreadsheets and tabular data
• Familiarity with Microsoft Excel or an equivalent spreadsheet application
• Basic understanding of formulas, charts, and business reports
• No prior machine learning or advanced programming experience required

 

Technical Requirements

Participants should have access to:

• A laptop with a modern web browser
• Microsoft Excel and Power Query
• Microsoft Power BI or an equivalent visualization platform
• An organization-approved generative AI tool
• Sample or anonymized datasets
• Reliable internet connection when cloud-based tools are used

  

Confidential, personal, customer, financial, or regulated data should not be entered into public AI tools. Company datasets used during training must be anonymized and approved by the organization.

 

Course Outline

 

Day 1: Data Analytics and Artificial Intelligence Foundations

Module 1: Introduction to Data Analytics 

• Meaning and importance of data analytics
• Data-driven decision-making
• Descriptive, diagnostic, predictive, and prescriptive analytics
• Structured, semi-structured, and unstructured data
• The data analytics lifecycle
• Common business analytics applications
• From raw data to actionable insight

 Module 2: Artificial Intelligence in Data Analytics 

• Introduction to artificial intelligence
• Machine learning and generative AI concepts
• Traditional analytics versus AI-powered analytics
• How AI supports the analytics lifecycle
• Strengths and limitations of AI tools
• Common AI analytics use cases across business functions
• Selecting appropriate tasks for AI assistance

 Module 3: Understanding Data and Business Requirements 

• Defining the business problem
• Identifying analytical objectives
• Formulating appropriate analytical questions
• Identifying relevant data sources
• Understanding rows, columns, fields, and data types
• Defining analytical scope and expected outputs
• Recognizing data quality and availability issues

 Module 4: Prompt Engineering Fundamentals for Analytics 

• Components of an effective analytical prompt
• Providing business context and data definitions
• Defining objectives, constraints, and output formats
• Using iterative prompts and follow-up questions
• Requesting explanations and validation procedures
• Common prompting mistakes
• Creating reusable prompt templates

       Day 1 Practical Activity

• Define a business analytics problem
• Examine a sample dataset
• Formulate analytical questions
• Develop prompts for understanding the data

  

Day 2: Data Preparation and Exploratory Analysis

Module 5: Data Quality Assessment 

• Dimensions of data quality
• Accuracy, completeness, consistency, and validity
• Identifying missing and invalid values
• Detecting duplicate records
• Correcting inconsistent categories and formats
• Identifying outliers and unusual observations
• Documenting data quality issues

 Module 6: Data Cleaning and Transformation 

• Converting and standardizing data types
• Managing missing values
• Removing or consolidating duplicates
• Splitting and combining data fields
• Creating calculated fields
• Filtering and sorting data
• Combining multiple datasets
• Introduction to Power Query transformations

 Module 7: AI-Assisted Data Preparation 

• Using AI to generate spreadsheet formulas
• Requesting data-cleaning recommendations
• Generating transformation steps and queries
• Using AI to explain formulas and code
• Troubleshooting formula and transformation errors
• Reviewing AI-generated scripts
• Testing results against source data

 Module 8: Exploratory Data Analysis 

• Calculating summary statistics
• Understanding distributions and variability
• Comparing categories and time periods
• Segmenting and filtering data
• Identifying trends, relationships, and anomalies
• Correlation versus causation
• Using AI to suggest areas for further investigation

       Day 2 Practical Activity

• Clean and transform a business dataset
• Conduct an initial data quality assessment
• Perform exploratory data analysis
• Document key patterns and potential issues

  

Day 3: KPIs, Data Visualization and Dashboard Development

Module 9: Business Metrics and KPI Development 

• Difference between metrics and KPIs
• Translating business goals into measurable indicators
• Selecting meaningful performance measures
• Calculating totals, averages, percentages, and ratios
• Growth, variance, productivity, and efficiency measures
• Establishing targets and benchmarks
• Avoiding vanity and misleading metrics
• Using AI to refine KPI definitions

 Module 10: Data Visualization Principles 

• Selecting the appropriate visualization
• Bar, column, line, area, pie, scatter, and combination charts
• Tables, scorecards, and KPI indicators
• Applying labels, colors, and formatting
• Presenting comparisons, trends, and relationships
• Avoiding misleading charts
• Accessibility and readability considerations

 Module 11: Dashboard Design and Development 

• Defining the dashboard audience and purpose
• Planning the dashboard layout
• Establishing information hierarchy
• Developing interactive filters and slicers
• Creating drill-down and drill-through experiences
• Connecting dashboard elements to business questions
• Improving dashboard usability and visual consistency
• Validating calculations and visual outputs

 Module 12: AI-Assisted Visualization and Insight Generation 

• Using AI to recommend suitable charts
• Generating visualization instructions
• Requesting explanations for trends and variances
• Identifying potential anomalies
• Creating management-level summaries
• Translating technical findings into business language
• Distinguishing evidence from AI-generated assumptions

       Day 3 Practical Activity

• Define business KPIs
• Create data visualizations
• Develop an interactive dashboard
• Produce an AI-assisted executive summary

Day 4: Predictive Analytics, Reporting and Responsible AI

Module 13: Introduction to Predictive Analytics 

• Predictive analytics concepts
• Features, targets, and historical data
• Introduction to classification and regression
• Training data and test data
• Business applications of predictive models
• Evaluating predictive results
• Understanding confidence and uncertainty
• Common predictive analytics limitations

 Module 14: Forecasting and Scenario Analysis 

• Understanding time-based data
• Identifying trends and seasonality
• Creating baseline forecasts
• Using historical data to estimate future results
• What-if and scenario analysis
• Comparing alternative assumptions
• Interpreting forecasting outputs
• Identifying unreliable forecasts
• Using AI to explain forecasting results

 Module 15: Data Storytelling and AI-Assisted Reporting 

• Structuring an analytical narrative
• Connecting findings to business objectives
• Presenting insights to nontechnical audiences
• Explaining risks, assumptions, and limitations
• Creating AI-assisted analytical reports
• Developing practical recommendations
• Supporting recommendations with evidence
• Preparing an executive-level presentation

 Module 16: Responsible AI, Privacy and Governance 

• AI accuracy, bias, fairness, and transparency
• Risks of inaccurate or fabricated AI outputs
• Data privacy and confidentiality
• Information security considerations
• Intellectual property and data ownership
• Organizational policies for approved AI tools
• Human review and accountability
• Documenting prompts, sources, assumptions, and changes
• Establishing an AI output validation checklist

 Module 17: Capstone Activity 

Participants will complete an end-to-end analytics project involving:

• Defining a business problem
• Preparing and validating the dataset
• Conducting exploratory data analysis
• Establishing relevant KPIs
• Creating visualizations and an interactive dashboard
• Developing a basic forecast or scenario analysis
• Producing AI-assisted insights and recommendations
• Validating the results
• Presenting findings to the group

 Suggested Training Tools

        The course may use:

• Microsoft Excel
• Microsoft Power Query
• Microsoft Power BI
• ChatGPT, Microsoft Copilot, or another organization-approved AI platform
• Python with Pandas and visualization libraries, if required for a more technical audience