The data science program at Becker College is a technically focused program, with managerial elements. Students enrolled in the data science program learn to optimize business decisions through insight and informed decision making. An interdisciplinary application of data science tools and techniques from applied math and computer science prepare students to analyze and strategically manage large data sets.
From health care to high tech, from education to energy providers, to sport leagues, all are generating massive amounts of information. In the era of big data, skilled analytics professionals are needed to make sense of the information companies, governments, and educational institutions are collecting. The collection and use of big data continues to expand in all of these areas. As businesses seek to maximize the value of vast stores of available data, employees will be needed to fill the growing demand for individuals who can learn from that data and predict and forecast consumer behavior.
Learning Outcomes:
Communication: Students demonstrate effective written and oral communication including informed arguments, persuasion, synthesize complex data, and concepts.
Context: Students demonstrate comfort with and ability to effectively operate in: ambiguity, complexity, uncertainty, and change. Students will be adept at situational awareness. Students understand the historical context of their culture and they will view the world from a global lens acknowledging the diversity of cultures and values. Students demonstrate propositional thinking; they will test and iterate on ideas/solutions based upon feedback and learn from their own failures.
Quantitative: Students use quantitative and analytical methods to address unstructured business problems.
Ethics: Students demonstrate ability to evaluate decisions based on an awareness of relevant stakeholders in the creation of sustainable social, environmental, and economic value.
Cognitive Flexibility: Students demonstrate mastery of various thinking modes, notably analytic, synthetic, convergent, divergent, creative, and critical; and when to employ each type. Student demonstrate awareness of their own strengths and weaknesses and will seek continual improvements. Student demonstrate both self and social awareness and management to effectively play various roles on an interdisciplinary team from leader to contributor to participant (role fluidity). These skills are essential for all leadership and management roles.
Value Creation: Students identify, create, shape, and capture value through understanding economic models and the reality of industry disruption cycles.
| Fall Semester | Course Name | Credits | Spring Semester | Course Name | Credits |
|---|---|---|---|---|---|
| Freshman Year | |||||
| CPTR1100 | Computer Programming I | 3 | DATA2001 | Data Science Tools and Techniques | 3 |
| CORE1001 | Managing Transitions: Change as a Norm | 3 | MATH2302 | Calculus II | 3 |
| ECON1200 | Global Economics: Micro and Macro Perspectives | 3 | CORE-ENGL | The New Normal: Exploring Unstructured Problems | 3 |
| MATH2202 | Calculus I | 3 | MKTG2004 | Marketing I - Creating Marketing, Branding and Sales Strategies | 3 |
| MGMT1000 | Introduction to Business Models | 3 | CPTR1400 | Programming II | 3 |
| 15 | 15 | ||||
| Sophomore Year | |||||
| PSYC1001 | Introduction to Psychology | 3 | INFO2500 | Data Visualization | 3 |
| CPTR2350 | Data Structures and Algorithms | 3 | CPTR2400 | Database Management | 3 |
| MKTG3004 | Marketing II - Analyzing Marketing, Branding and Sales Strategies | 3 | MGMT2900 | Business Career Exploration | 3 |
| CORE3100 | Developing an Entrepreneurial Mindset | 3 | ENGL1003 | Writing about Literature | 3 |
| MATH3205 | Statistical Methods for Data Analysis | 3 | Science Elective with Lab | 4 | |
| 15 | 16 | ||||
| Junior Year | |||||
| MGMT3700 | Business Decision Making: Law, Ethics and Strategies | 3 | CPTR3400 | Data Warehousing | 3 |
| ACCT2100 | Accounting and Finance I - Concepts and Tools | 3 | ACCT3100 | Accounting and Finance II - Financial Decision Making | 3 |
| MATH3200 | Multivariate Statistics | 3 | MATH3305 | Linear Algebra | 3 |
| CPTR3450 | Data Warehouse & Data Mining with AI | 3 | CPTR4100 | Machine Learning | 3 |
| General Education Elective | 3 | INFO3901 | Data Science Practicum | 3 | |
| 15 | 15 | ||||
| Senior Year | |||||
| MGMT4101 | Capstone I (Business Policy) | 3 | MGMT4102 | Capstone II (Business Elective 3000+) | 3 |
| INFO4200 | Predictive Analysis | 3 | PHIL3001 | Ethics | 3 |
| MGMT4003 | Developing Business Leadership (MGMT4195) | 3 | Humanities and Fine Arts Elective | 3 | |
| Open Elective | 3 | General Education Elective | 3 | ||
| Humanities and Fine Arts Elective | 3 | Open Elective | 3 | ||
| 15 | 15 | ||||
| Total Credits: | 121 |
