Introduction

Augmented Analytics leverages artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to automate data preparation, insight generation, and data-driven decision-making. This course introduces the concept of augmented analytics and provides participants with the tools and techniques to apply AI-driven analytics to business intelligence (BI) processes. With a focus on automating data insights, augmented analytics allows businesses to generate actionable results without requiring advanced technical skills, making it an essential tool for modern data-driven organizations.

Objectives

By the end of this course, participants will:

• Understand the principles and evolution of augmented analytics.
• Learn how to integrate AI, machine learning, and NLP into analytics workflows.
• Gain hands-on experience using augmented analytics tools to automate data processing, exploration, and insight generation.
• Learn how to create and evaluate AI-driven reports and dashboards.
• Apply augmented analytics to solve real-world business challenges and improve decision-making.
• Understand the ethical and practical implications of using AI in data analytics.

Who Should Attend?

This course is designed for:

• Data analysts, data scientists, and BI professionals who want to automate their analytics workflows.
• Business intelligence and data professionals looking to integrate AI and ML into their decision-making processes.
• Managers and decision-makers interested in understanding how augmented analytics can benefit their organizations.
• Anyone looking to stay ahead in the rapidly evolving field of data science and AI-driven analytics.

Day 1: Introduction to Augmented Analytics

Morning Session: Fundamentals of Augmented Analytics

• Defining augmented analytics and its role in the future of data analytics.
• The evolution of data analytics: From descriptive to predictive to prescriptive analytics.
• Key components of augmented analytics: AI, machine learning, and natural language processing (NLP).
• How augmented analytics improves the BI process: Automating data preparation, exploration, and insight generation.
• Examples of augmented analytics in action: Case studies from various industries (e.g., finance, healthcare, retail).
• Hands-on: Introduction to an augmented analytics tool (e.g., Tableau, Power BI with AI capabilities).

Afternoon Session: Data Preprocessing with Augmented Analytics

• The importance of data preparation in analytics and the role of AI in automating this process.
• Techniques for data cleansing, transformation, and feature engineering using augmented analytics tools.
• Integrating machine learning models for automated feature selection and outlier detection.
• Hands-on: Using augmented analytics tools to clean and prepare a dataset for analysis.

Day 2: AI and Machine Learning in Augmented Analytics

Morning Session: Machine Learning for Automated Insights

• Overview of machine learning models used in augmented analytics: Regression, classification, clustering, and anomaly detection.
• How machine learning is integrated into augmented analytics platforms for automated insights generation.
• The role of AI in automating data exploration and the discovery of patterns and trends in data.
• Hands-on: Building and deploying a simple machine learning model within an augmented analytics tool.

Afternoon Session: Natural Language Processing (NLP) for Augmented Analytics

• Introduction to natural language processing and its role in making data insights more accessible.
• Using NLP to generate reports, summaries, and insights directly from data.
• Conversational analytics: Interacting with data through natural language queries and voice interfaces.
• Hands-on: Using NLP features within an augmented analytics platform to generate insights and reports from raw data.

Day 3: Creating AI-Driven Dashboards and Reports

Morning Session: Building AI-Powered Dashboards

• Designing dashboards that automatically surface insights using augmented analytics.
• Techniques for enhancing interactivity: Predictive models, scenario analysis, and “what-if” analyses.
• Integrating real-time data streams and AI-driven predictions into dashboards for decision-makers.
• Hands-on: Creating an AI-powered dashboard in an augmented analytics tool (e.g., Power BI with machine learning integration).

Afternoon Session: Automated Report Generation with Augmented Analytics

• Leveraging AI to generate written reports and summaries based on analytics.
• Best practices for creating automated reporting workflows that generate insights in plain language.
• Customizing automated reports for different stakeholders: executives, analysts, and operations teams.
• Hands-on: Using an augmented analytics tool to create automated reports with machine-generated text and visualizations.

Day 4: Advanced Augmented Analytics Techniques

Morning Session: Predictive Analytics with Augmented Analytics

• Overview of predictive analytics and its integration into augmented analytics platforms.
• Using machine learning to forecast trends and outcomes based on historical data.
• Model evaluation and performance tracking in augmented analytics tools.
• Hands-on: Building a predictive model for forecasting using an augmented analytics tool.

Afternoon Session: Prescriptive Analytics with Augmented Analytics

• Introduction to prescriptive analytics: AI-driven recommendations and decision support.
• Using optimization algorithms and simulations for automated decision-making.
• How augmented analytics platforms generate actionable insights for business strategies.
• Hands-on: Creating a prescriptive analytics model for business decision-making.

Day 5: Ethical Considerations, Best Practices, and Future Trends

Morning Session: Ethical Implications of Augmented Analytics

• Understanding the ethical challenges in AI and machine learning in augmented analytics.
• Ensuring transparency, fairness, and accountability in AI-driven analytics processes.
• Best practices for preventing algorithmic bias and ensuring ethical decision-making.
• Legal and regulatory considerations in using AI in business analytics.
• Hands-on: Analyzing a case study on ethical considerations in the application of augmented analytics.

Afternoon Session: Advanced Applications and Future of Augmented Analytics

• Emerging trends in augmented analytics: The role of deep learning, reinforcement learning, and AI chatbots.
• The impact of augmented analytics on different industries (finance, healthcare, retail, marketing).
• Best practices for implementing augmented analytics in organizations.
• Future challenges: Explainability, scalability, and human-machine collaboration.
• Final Project: Participants will apply augmented analytics tools to a real-world business problem, demonstrating the techniques learned throughout the course.
• Wrap-up and Q&A: Recap of key takeaways and next steps for continuing the journey with augmented analytics.

Materials and Tools:

• Software and Tools: Power BI, Tableau, Qlik, Google Cloud AI, Microsoft Azure AI, IBM Watson Analytics, Python (scikit-learn, TensorFlow, etc.), R (tidyverse, caret).
• Resources: Course slides, datasets for hands-on exercises, Python/R notebooks, sample AI models.
• Case Studies: Real-world examples of augmented analytics implementations in industries like finance, healthcare, and retail.

Post-Course Support:

• Access to recorded sessions, course slides, and additional resources.
• Post-course webinars for exploring advanced topics in augmented analytics.
• Community forum for sharing projects, discussing challenges, and collaborating with peers.
• One-on-one mentoring sessions for applying augmented analytics in your organization.