This course provides a comprehensive foundation in data analytics, covering the entire analytics lifecycle from data collection and preparation to analysis, visualization, reporting, and interpretation. Learners will explore statistical concepts, data management practices, business intelligence tools, and predictive analytics techniques used in modern organizations.

The course emphasizes practical application through case studies, projects, and hands-on exercises using industry-standard tools such as Microsoft Excel, SQL, Power BI, and introductory Python for analytics.

Course Objectives

Upon successful completion of this course, learners will be able to:

• Understand the principles and applications of data analytics.
• Collect, organize, and manage data from multiple sources.
• Clean and prepare data for analysis.
• Apply statistical methods to analyze datasets.
• Create dashboards and data visualizations.
• Use analytics tools to generate business insights.
• Interpret findings and communicate recommendations effectively.
• Support organizational decision-making through data-driven approaches.

Learning Outcomes

By the end of the course, learners should be able to:

• Explain key concepts and methodologies in data analytics.
• Identify and evaluate data sources.
• Perform data cleaning and preparation tasks.
• Conduct descriptive and basic predictive analyses.
• Create meaningful reports and dashboards.
• Use Excel, SQL, Power BI, and foundational Python analytics tools.
• Present analytical findings to stakeholders.
• Apply ethical standards in data handling and reporting.