Master in Data Science and Business Analytics

1. Provide students with the needed knowledge and understanding of current and emerging technologies in data science.
2. Leverage students’ understanding of the importance of data science to organizations.
3. Enable students to apply fundamental knowledge and skills of data science to improve organizational performance.
4. Improve the student’s ability to analyze data in an organization and to make management decisions based on that.
5. Strengthen the student’s ability to critically evaluate problems and identify analytical solutions, specially that are based on data.
6. Provide the students with an understanding of the relationship between data science, artificial intelligence and business analytics.
7. Provide students with the needed knowledge and skills in machine learning and how to employ that in data analysis.
8. Provide students with the basics of Artificial Intelligence, and train them how to use that in data analysis.
9. Provide students with needed knowledge in building prediction models and how to use that to solve problems and enhance organizations’ performance.
10. Provide students with the needed knowledge and skills in business intelligence and how to use that in boosting profit and lowing cost.

Careers of graduates:

• Data Analyst: A data analyst’s key task is to look at organization data, analyze it and use the results to answer business questions, and then communicate these results with managers to utilize them in decision making.
• Data Scientist: Do many of the same things as data analysts, but additionally, they work to build machine learning models to make accurate predictions about the future based on past data.
• Data Engineers: work to manages an organization’s data infrastructure. Their job requires software development and programming skill. They’re also likely responsible for building and maintaining the infrastructure needed to store and quickly access data.
• Machine Learning Engineer: Overlap with data engineer and a data scientist. Sometimes this job is a data scientist who is specialized in the use of machine learning, which requires the heavy use of programming and software development.
• Quantitative Analyst: use sophisticated statistical analyses techniques to answer questions and make predictions related to finance and risk.
• Data Warehouse Architect: Essentially, this is a specialty or sub-field within data engineering for those who like to be in charge of a company’s data storage systems.
• Business Intelligence Analyst: focuses on analyzing market and business trends using machine learning and data mining techniques.
• Statistician: A job that requires deep understanding and high level of technical skills in using different techniques in data analysis, without the need for using machine learning and Artificial Intelligence.
• Systems Analyst: often tasked with identifying organizational problems, and then planning and overseeing the changes or new systems required to solve those problems.
• Marketing Analyst: Analyze sales and marketing data to assess and improve the effectiveness of marketing operations.
• Freelance as a Data Scientist:  Although most people who study data science are looking for full-time employment with a  company it’s worth remembering that data science skills afford you the opportunity to work as a freelancer.