Designing and developing course materials, syllabi, and projects that cover a range of topics in data analytics, including statistical analysis, machine learning, data visualization, and data mining. 

Analyzing large datasets to extract meaningful insights and trends.  Cleaning and organizing data to ensure accuracy and consistency.  Developing and implementing data collection systems and strategies. 

 Creating visualizations and reports to communicate findings to stakeholders.  Collaborating with cross-functional teams to address business challenges and opportunities.  

Conducting statistical analyses and predictive modeling to support business objectives.  Providing recommendations for process improvements and optimizations based on data-driven insights.

  Providing guidance and support to students, including answering questions, offering feedback on assignments and projects, and helping students navigate challenges they encounter in their learning journey. 

 Delivering lectures, leading discussions, and conducting hands-on exercises to teach students various concepts, tools, and techniques used in data analytics. This include using software such as SPSS, SAS, Excel, R, Python, SQL, Power BI and Tableau.