Students are introduced to the world of data science and learn about the key concepts of data science, the data science ecosystem, the role of data science in creating solutions to business problems, the roles and responsibilities of each major data science position, and examine the organizational goals and value provided by analytics and big data systems and processes.

Students learn about the collection, use, and storage of data responsibly in accordance with customer expectations, regulations, and laws. They will also learn the ethical way of handling and managing data.

Students learn how to apply project management skills and concepts to data science projects in various levels and types of companies.

Students learn how to develop communication skills through verbal, visual, and written exercises. Students will then look at facilitating leadership and management skills in an era of dynamic and ongoing changes.

Students will learn to use the main programs within the data science ecosystem. We will focus on learning about SQL, R, Python, Tableau, Power BI, and Spark. Each program will be studied in how they fit in the overall process of data science

Excel is widespread in the data industry. It is a very powerful data analysis tool and almost all big and small businesses use Excel daily. This course is designed to give an introductory and applied knowledge of Excel towards data analysis and business statistics.

Students will learn how to make sense of the volumes of data collected by incorporating data-driven modeling. Students will explore, interpret, prepare, and model data through R and Python.

Students learn how to use statistical methods for collecting, analyzing, interpreting and presenting data. Students explore inferential statistics for making probability decisions and accurate predictions.

This course introduces you to the fundamentals of data visualization and exploratory data analysis. The growing availability of informative datasets and software tools has led to increased reliance on data visualizations across many areas.

Students will learn about and use the main programs for big data. These programs include Hadoop, Spark, Hive, and Pig. Each program will be studied in how they fit in the overall process of data science.

Predictive analysis and machine learning are two of the most critical areas in analytics. Machine learning uses algorithms to track data and make predictions.

Learn to apply data analytics skills to the area of business intelligence (BI) with emphasis on the project lifecycle such as project planning, BI tool selection, data modeling, ETL design, BI application design and deployment and reporting.

Data engineers are on the front lines of data strategy and the first people to tackle structured and unstructured data that enters a company’s systems. Learning about Postgres, being able to build data pipelines, and how to optimize systems and algorithms for large volumes of data are all skills that will make data analytics easier and efficient.

This course introduces you cloud analytics using cloud computing. We will use a range of tools and techniques that help various companies acquire information from large volumes of data and present it that is available in a web browser.

This course introduces you to the exciting, dynamic, and rapidly evolving world of Artificial Intelligence (AI). We will look at the various methods that companies, organizations, and institutions can or potentially use AI to address their needs.

Showcasing the application of all learned techniques through a comprehensive and impactful final project presentation.