1. Develop a foundational understanding of data science and its applications in business intelligence, including key concepts, techniques, and tools used in the field.
2. Learn how to collect, clean, and preprocess data for analysis, including basic data manipulation using spreadsheets.
3. Gain proficiency in data visualization techniques and learn how to communicate insights to stakeholders effectively.
4. Learn how to apply simple machine learning algorithms to business problems, including supervised and unsupervised learning techniques, and understand how these algorithms can be used to drive business insights and decision-making.

Day one:

Introduction to Data Science and Business Intelligence

• Understanding the basics of data science and business intelligence
• Key differences between the two fields
• Overview of popular data science tools and techniques

Day Two:

Data Collection and Cleaning

• How to collect and clean data for analysis
• Introduction to spreadsheets and basic data manipulation using Microsoft Excel or Google Sheets
• Hands-on exercises in data cleaning and formattin

Day Three:

Data Visualization and Analysis

• Basics of data visualization and how to communicate insights to stakeholders
• Introduction to creating charts and graphs in Excel or Google Sheets
• Hands-on exercises in creating basic data visualisations and identifying patterns in data

Day Four:

Introduction to Machine Learning

• Overview of machine learning and its applications in business intelligence
• Introduction to supervised and unsupervised learning
• Hands-on exercises with simple machine learning algorithms in Excel or Google Sheets

Day Five:

Business Intelligence Applications and Conclusion

• • Real-world examples of how data science and machine learning are applied in business intelligence
  • Discussion on the business impact of data science and its limitations
  • Closing thoughts and next steps for pursuing a career in data science for business intelligence.