Power BI for Business Intelligence: From Data Transformation to AI-Enhanced Analytics
25/10/2024Advanced Data Visualization and Predictive Analytics with Power BI
25/10/2024Data Science for Strategic Business Decision Making
£4,000.00
Overview:
This course provides a practical, hands-on approach to data science for business decision-making. Covering data collection, cleaning, analysis, visualization, and machine learning, participants learn how to translate data insights into actionable strategies. The curriculum emphasizes AI-driven insights, automation, and ethical data use, preparing participants to leverage data science for strategic growth and operational improvements.
Program Objectives:
By the end of this course, participants will be able to:
- Understand foundational data science concepts and their impact on strategic decision-making.
- Communicate data insights effectively using visualization and storytelling.
- Build and evaluate predictive models for business forecasting.
- Deploy and interpret predictive analytics models in real-world business contexts.
- Integrate data science insights seamlessly into business strategies, leveraging AI and automation where applicable.
- Address ethical considerations, privacy, and compliance in data-driven decision-making.
Target Audience:
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- Business Analysts, Data Analysts, Managers, and Executives
- Product Managers, Strategists, and Entrepreneurs
- IT Professionals and Business Owners interested in data-driven decision-making
Program Outline:
Day 1: Introduction to Data Science and Business Decision-Making
- Overview of Data Science Applications in Business – How data science addresses business needs.
- Topic The Data Science Process – Steps from data collection to actionable insights.
- Types of Data – Structured vs. unstructured data and business applications.
- Introduction to Data-Driven Decision-Making – Improving business outcomes with data.
- Hands-On Exercise: Analyzing a business case study and identifying key data sources.
- Reflection & Review: Discussing the role of data in strategic decision-making.
Day 2: Data Collection, Cleaning, and Visualization Tools
- Data Collection Techniques – Exploring internal and external sources.
- Data Cleaning and Preprocessing – Handling missing values and inconsistencies.
- Introduction to Visualization Tools – Overview of Tableau and Power BI for business insights.
- Automating Data Preparation – Creating workflows for ongoing data processing.
- Hands-On Exercise: Preparing a real-world dataset and creating an automated report using Power BI or Tableau.
- Reflection & Review: Discussion on data quality’s impact on analysis outcomes.
Day 3: Data Analysis, Visualization, and Storytelling
- Exploratory Data Analysis (EDA) – Uncovering trends and insights.
- Data Visualization Techniques – Best practices for creating impactful visuals.
- Storytelling with Data – Communicating insights effectively to stakeholders.
- Visualization Tools Deep Dive – Building interactive dashboards in Power BI and Tableau.
- Hands-On Exercise: Developing visualizations and a data story to support a business case.
- Reflection & Review: Case study discussion on the role of data visualization in strategic planning.
Day 4: Predictive Analytics, AI-Driven Insights, and Model Evaluation
- Introduction to Machine Learning for Business – Supervised vs. unsupervised learning.
- Building Predictive Models – Linear regression, decision trees, and random forest for business forecasting.
- AI-Driven Insights – Leveraging AutoML and AI-powered features for enhanced analysis.
- Model Evaluation and Metrics – Precision, recall, and accuracy in business contexts.
- Hands-On Exercise: Creating and evaluating a predictive model, exploring automated analysis tools.
- Reflection & Review: Group discussion on real-world applications of predictive analytics and AI.
Day 5: Integrating Data Science into Business Strategy and Ethical Considerations
- Aligning Data Science with Business Goals – Using insights to support organizational objectives.
- Optimizing Business Operations – Improving efficiency through data-driven insights.
- Ethical Considerations in Data Science – Addressing privacy, compliance, and ethical issues in data usage.
- Final Project Presentation – Presenting a data-driven business strategy with actionable recommendations.
- Capstone Project: Developing a comprehensive business strategy based on data science insights, including recommendations for implementation.
- Reflection & Review: Final project presentations, feedback on strategy alignment, and discussion on future trends.