AI-driven Data Analytics and Business Intelligence #401024

• Analyse the impact of future trends on the development and application of AI in data analytics and BI.
• Identify key challenges and opportunities within your organization's data landscape, considering how AI can improve data processing, analysis, and visualization.
• Develop a strategic vision for integrating AI into your data analytics and BI processes to unlock new insights and enhance decision-making.
• Explore various AI techniques and tools applicable to different stages of the data analytics life cycle, such as data preparation, feature engineering, predictive modelling, and anomaly detection.
• Gain practical experience using user-friendly AI tools and platforms to perform basic AI-powered data analysis tasks on real-world datasets.
• Understand the challenges and potential biases within AI-driven data analytics, and explore strategies for responsible AI implementation.
• Communicate effectively about the potential and limitations of AI-powered data analytics to stakeholders within the organization.
• Develop a personalized action plan outlining steps to implement AI-driven data analytics solutions within your specific role or department.

• Data analysts, business analysts, and data scientists interested in utilizing AI for more powerful data analysis.
• Business intelligence professionals seeking to leverage AI for advanced insights and improved decision-making.
• IT professionals responsible for integrating AI into existing data analytics and BI infrastructure.
• Business leaders and managers who want to understand the potential of AI in transforming data-driven strategies.
• Anyone interested in exploring the future of data analytics and BI with AI integration.

• Pre-assessment
• Live group instruction
• Use of real-world examples, case studies and exercises
• Interactive participation and discussion
• Power point presentation, LCD and flip chart
• Group activities and tests
• Each participant receives a binder containing a copy of the presentation
• slides and handouts
• Post-assessment

Day 1: The Future of Data Analytics & AI
• Welcome and program overview.
• The Power of AI in Data Analytics: Exploring how AI is transforming data analytics by automating tasks, uncovering hidden patterns, and enabling more accurate predictions.
• Future Studies for Data Professionals: Learning how to incorporate future studies methodologies like scenario planning to anticipate potential disruptions and opportunities related to AI in the data analytics landscape.
• The Future of Work & Data Analytics: Discussing the impact of AI on data jobs and the future of work, focusing on the evolving role of data professionals with AI skills.
• Guest Speaker: A data scientist or industry leader who has successfully implemented AI-powered data analytics can be invited to share their insights and answer participant questions.

Day 2: Leveraging AI for Data Preparation & Feature Engineering
• Challenges of Big Data & AI: Discussing the challenges associated with large and complex datasets in AI-powered data analytics, including data cleaning, integration, and transformation.
• AI-powered Data Preprocessing: Exploring how AI techniques like natural language processing (NLP) and computer vision can be used for automated data cleaning, anomaly detection, and data wrangling tasks.
• Feature Engineering with AI: Learning how AI algorithms can assist in feature selection, feature extraction, and dimensionality reduction for improved model performance.
• Hands-on Workshop: AI-powered Data Cleaning with Python Libraries: Participants will gain practical experience using popular Python libraries like Pandas and scikit-learn to perform basic data cleaning tasks on a sample dataset relevant to business intelligence.
Day 3: AI for Advanced Analytics & Predictive Modeling
• Machine Learning for Data Analysis: Understanding the core concepts of machine learning and its various algorithms used for predictive modelling, classification, and clustering in AI-driven data analytics.
• AI-powered Forecasting & Trend Analysis: Exploring how AI models can be used to forecast future trends, identify seasonality patterns, and improve demand forecasting accuracy.
• Building Predictive Models with Azure Machine Learning (Optional): This session can introduce participants to a cloud platform like Azure Machine Learning for building, training, and deploying machine learning models for business intelligence applications. (Can be replaced with a similar platform depending on preference).
• Case Study: analysing a real-world example of how a company used AI-powered predictive modelling to improve customer churn prediction and marketing campaign effectiveness.
Day 4: Responsible AI & Explainable Analytics
• Understanding Bias in AI-driven Data Analytics: Discussing the potential for bias in AI algorithms and datasets, and its impact on data analysis results and decision-making.
• Strategies for Mitigating Bias: Exploring techniques for identifying and mitigating bias in data collection, model training, and interpretation of AI-powered analytics.
• Explainable AI (XAI) for Business Intelligence: Learning about XAI techniques that help explain the rationale behind AI model decisions, fostering trust and transparency in data-driven insights.
• The Ethics of AI in Data Analytics: Discussing ethical considerations surrounding AI use in data analytics, such as data privacy concerns and algorithmic fairness.

Day 5: The Future of AI & Data-driven Business
• Real-time Analytics & AI for Decision Support: Exploring the potential of real-time data processing and AI for near-instant insights and data-driven decision-making within organizations.
• The Democratization of AI: Discussing the future trends towards more user-friendly AI tools and platforms, enabling wider adoption of AI-powered data analytics across businesses.
• Building Your AI Action Plan: Participants create personalized action plans outlining steps to explore and implement AI-driven data analytics solutions within their specific roles or departments. This may include:
o Identifying key business questions where AI-powered data analysis could be beneficial.
o Researching available AI tools and techniques relevant to their data sources and needs.
o Building a pilot project demonstrating the value proposition of AI for data analytics within their organization.
o Advocating for investment in AI skills development and responsible AI practices within their teams.
• Course Wrap-Up & Ongoing Learning: Reviewing key takeaways from the program, addressing any remaining questions, and discussing ongoing learning resources for staying informed about advancements in AI and its application in data analytics and business intelligence.
• Networking & Knowledge Sharing: Participants engage in a facilitated discussion to share their AI action plans and explore potential collaborations on data analytics projects using AI within their organizations. They can also showcase their ideas for leveraging AI to gain deeper business insights.