CARES is committed to supporting academic institutions in their mission to provide high-quality education and foster intellectual growth among students, faculty, and staff. Our academic training programs are designed to enhance teaching and learning methodologies, promote professional development, and improve overall academic excellence.
We offer a wide range of academic training programs tailored to the needs of college institutions, faculty members, and students. Our programs cover areas such as effective teaching strategies, curriculum development, assessment and evaluation methods, data analytics and research skills, and academic leadership.
Through our academic training programs, we aim to empower educators with innovative teaching approaches, pedagogical techniques, and the latest educational technologies. We also provide students with the necessary skills and knowledge to excel in their academic pursuits and prepare them for future careers in the field of data analytics.
Excel fundamentals, data entry and manipulation, basic formulas and functions.
Advanced formulas and functions, data analysis tools, pivot tables.
Advanced data modeling, automation with macros, advanced functions (e.g., VLOOKUP, INDEX-MATCH).
Introduction to Power BI, data visualization basics, creating interactive reports and dashboards.
Advanced data modeling, DAX formulas and functions, data transformation and cleansing.
Power Query, Power Pivot, advanced visualization techniques, advanced data analysis.
Introduction to Tableau, data connection and visualization basics, creating basic charts and graphs.
Advanced chart types, calculations and parameters, interactive dashboards.
Advanced analytics, data blending, scripting and automation, server administration.
Introduction to SPSS, data entry and manipulation, basic statistical analysis.
Descriptive statistics, inferential statistics, regression analysis.
Multivariate analysis, factor analysis, structural equation modeling.
Introduction to Jamovi, basic data analysis, simple statistical tests.
Advanced statistical tests, data visualization, data cleaning and transformation.
Complex statistical modeling, ANOVA, advanced data analysis techniques.
Introduction to R, data types and structures, basic data manipulation.
Data visualization with ggplot2, statistical analysis with base R, programming fundamentals.
Advanced statistical modeling, packages for specific analysis (e.g., machine learning, time series).
Introduction to Python, data types and structures, basic data manipulation.
Data visualization with libraries like Matplotlib and Seaborn, data analysis with Pandas.
Advanced data manipulation and cleaning, machine learning with libraries like Scikit-learn, deep learning with TensorFlow or PyTorch.
For student level Research and data analytics (dissertation project) we offer the following:
The program aims to bridge the gap between traditional research methods and data analytics, providing academics with the skills to integrate data analytics techniques into their research work. Participants will gain a comprehensive understanding of how data analytics can enhance research outcomes.
Note: The program can be customized to meet specific needs and requirements.