Data Management and Visualization Mastery
26/10/2024Sustainable Urban Development and Smart Cities
28/10/2024Advanced Analytics for Data-Driven Decision Making
£4,000.00
Overview:
This 5-day program equips participants with advanced analytics techniques and practical tools to enhance organizational decision-making. Covering data collection, predictive modeling, multivariate analysis, and ethical data handling, the course provides hands-on experience with AI-driven analytics, interactive dashboards, and compliance frameworks. By the end, participants will be able to make data-informed decisions aligned with organizational goals and communicate insights effectively through advanced visualizations.
Program Objectives:
By the end of this course, participants will be able to:
- Source, assess, and prepare data to support decision-making.
- Apply statistical and machine learning techniques to uncover trends and predict outcomes.
- Develop interactive dashboards to visualize insights and communicate findings.
- Implement predictive modeling using advanced algorithms such as decision trees and neural networks.
- Ensure ethical and regulatory compliance in data handling and analytics processes.
- Integrate data-driven insights into strategic and tactical decisions for organizational impact.
Target Audience:
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- Business Professionals and Analysts interested in data-driven decision-making.
- Data Scientists and Analysts looking to enhance their analytics skill set.
- Managers and Executives leveraging analytics for forecasting and strategy.
- Compliance Officers, Project Managers, and Digital Transformation Leads.
- Professionals in finance, healthcare, marketing, or other sectors where data-driven decisions are essential.
Program Outline:
Day 1: Data Collection, Quality, and Governance
- Data Sources and Quality Assessment – Identifying Reliable Data Sources and Ensuring Accuracy.
- Data Bias and Mitigation – Recognizing and Addressing Bias in Data Collection and Analysis.
- Data Governance and Privacy – Establishing Compliance with Privacy Regulations.
- Hands-On Activity: Collecting and Assessing Data for a Real-World Business Case Study.
- Reflection & Review: Group Discussion on Data Quality and Responsible Data Use.
Day 2: Exploratory Data Analysis (EDA) and Visualization
- Introduction to EDA – Techniques for Initial Data Exploration.
- Descriptive Statistics and Data Visualization – Summarizing Data Through Visuals and Statistics.
- Interactive Dashboards – Creating Visualizations Using Power BI or Tableau.
- Workshop: Conducting an EDA and Building a Dashboard to Visualize Trends and Correlations.
- Reflection & Review: Best Practices for Effective Data Exploration and Visualization.
Day 3: Predictive Modeling and Machine Learning
- Fundamentals of Predictive Analytics – Algorithms and Applications.
- Machine Learning for Predictive Modeling – Decision Trees, Random Forests, and Neural Networks.
- Model Selection and Training – Choosing and Evaluating Models Based on Goals and Data.
- Hands-On Session: Building a Predictive Model Using Python or R and Evaluating Accuracy.
- Reflection & Review: Discussing Model Performance and Selection Criteria.
Day 4: Advanced Data Analysis Techniques and Forecasting
- Multivariate Analysis – Techniques for Analyzing Complex Relationships.
- Time Series Analysis and Forecasting – Identifying Trends and Seasonality.
- AI-Driven Analytics – Integrating AI for Enhanced Predictive Power.
- Workshop: Applying Multivariate Analysis and Forecasting to Real-World Data.
- Reflection & Review: Examining Practical Applications of Advanced Analytics.
Day 5: Ethical, Regulatory, and Implementation Considerations
- Ethics in Data Analytics – Ensuring Transparency, Privacy, and Integrity.
- Regulatory Compliance – Understanding Data Privacy Laws and Standards.
- Building a Data-Driven Culture – Aligning Analytics with Business Goals.
- Future Trends in Analytics – AI-Driven Insights, Augmented Analytics, and Automation.
- Capstone Project: Developing a Data-Driven Strategy or Forecast Using Collected and Analyzed Data.
- Reflection & Review: Project Presentations, Peer Feedback, and Discussion on Future Trends.