Data Analytics and Business Intelligence
Course Content
Introduction to Data Analytics and Business Intelligence
Overview of Data Analytics and Business Intelligence
- Definition and importance of data analytics
- Definition and significance of business intelligence
- Differences and relationship between data analytics and business intelligence
History and Evolution
- Evolution of data analytics and BI from traditional methods to modern techniques
- Key milestones in the development of data analytics and BI
Key Concepts and Terminology
- Data, information, and knowledge
- Key terms: data warehousing, data mining, ETL (Extract, Transform, Load), OLAP (Online Analytical Processing)
- Types of data: structured, semi-structured, unstructured
Data Collection and Preparation
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Complete course content will be provided when requesting for a quoatation.
Course Materials
Textbooks:
- Data Analytics Made Accessible by Anil Maheshwari
- Competing on Analytics by Thomas H. Davenport and Jeanne G. Harris
- The Data Warehouse Toolkit by Ralph Kimball and Margy Ross
Online Resources:
- Coursera and edX courses on data analytics and BI
- Tutorials and documentation from BI tool providers (e.g., Tableau, Microsoft Power BI)
- Articles and case studies from industry publications and websites
Tools and Software:
- Data preparation tools: Alteryx, Talend, Informatica
- Analytics tools: R, Python, SAS
- BI tools: Tableau, Power BI, QlikView
- Data warehousing tools: Amazon Redshift, Google BigQuery, Snowflake
Learning Outcomes
By the end of the training, participants will be able to:
- Understand the fundamental concepts of data analytics and business intelligence.
- Collect, prepare, and integrate data from various sources.
- Apply descriptive, diagnostic, predictive, and prescriptive analytics techniques.
- Utilize data warehousing, visualization, and reporting tools effectively.
- Design and implement BI strategies and solutions that align with business objectives.
- Ensure data security and compliance in BI implementations.