Artificial Intelligence for Business

The A.S. Degree in Artificial Intelligence for Business is designed for returning CIS, business, marketing, and data analytics professionals with industry experience or students who have completed CIS courses. The degree offers a balanced set of classes that provides students with the knowledge and skills to obtain jobs in the areas of data science, data analysis, data mining, text mining, business intelligence,machine learning, deep learning, natural language processing, and artificial intelligence research. Students will learn how to make business decisions using different machine learning algorithms and deep learning prediction models of different complexities. Students will learn how to use natural language processing to better understand customer intent through sentiment analysis and gather market intelligence.

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Students completing this associate degree will use different machine learning algorithms, deep learning prediction models, and natural language processing techniques to make effective business decisions.

The goals and objectives for the courses offered to obtain this degree will give students the basic skills needed to obtain one of several specialized positions.

Student Learning Outcomes for the Artificial Intelligence for Business Degree:

Students completing this associate degree will use different machine learning algorithms, deep learning prediction models, and natural language processing techniques to make effective business decisions.

The A.S. Degree in Artificial Intelligence for Business is designed for returning CIS, business, marketing, and data analytics professionals with industry experience or students who have completed CIS courses. The degree offers a balanced set of classes that provides students with the knowledge and skills to obtain jobs in the areas of data science, data analysis, data mining, text mining, business intelligence, machine learning, deep learning, natural language processing, and artificial intelligence research. Students will learn how to make business decisions using different machine learning algorithms and deep learning prediction models of different complexities. Students will learn how to use natural language processing to better understand customer intent through sentiment analysis and gather market intelligence.

Students need to take the following courses:

CISB 11 - Computer Information Systems: Overview of computer information systems including computer hardware, software, networking, programming, databases, Internet, security, systems analysis, ethics, and problem-solving using business applications.

MATH 110 - Elementary Statistics: Descriptive and inferential statistics and probability with emphasis on understanding statistical methods. Descriptive analysis of sample statistics, distribution of discrete and continuous random variables, estimation theory, tests of hypotheses, regression, correlation, and analysis of variance. (All this information is required for CISB60, CISB 61, and CISB 62)

CISP 71- Programming in Python: Design and development of object-oriented Python programming applications. Includes object-oriented business programs and applications, documentation and debugging techniques, user-interface, objects, various data types, methods, events, elementary control structures, arrays, inheritance, polymorphism, file operations, database interaction, and networking. Student must take CISP 71L concurrently. (All this information is required for CISB60, CISB 61, and CISB 62)

CISP 71 L- Programming in Python Laboratory: Laboratory for CISP 71- Python Programming exercises focusing on design and development of object-oriented business programs and applications, documentation and debugging techniques, user-interface, objects, variables, methods, events, elementary control structures, lists, arrays, inheritance , polymorphism, file operations, database interaction, and networking. Concurrent enrollment in CISP 71 is required. (All this information is required for CISB60, CISB 61, and CISB 62)

And either (CISD21 and CISD21 L) or (CISD31 and CISD31L) (All this information is required for CISB60, CISB 61, and CISB 62)

CISD 21 – Database Management - Microsoft SQL Server: Structured Query Language (SQL) and Transact-SQL for Microsoft SQL Server. Topics include creating database objects, retrieving and updating data, writing scripts, developing stored procedures and functions, developing triggers, and creating cursors. Student must be enrolled in CISD 21L, a concurrent lab co-requisite.

CISD 21 L - Database Management - Microsoft SQL Server Laboratory: Laboratory for CISD 21 - Structured Query Language (SQL) and Transact-SQL for Microsoft SQL Server. Topics include creating database objects, retrieving and updating data, writing scripts, developing stored procedures, functions, triggers, and creating cursors. Student must be enrolled in CISD 21, a concurrent lecture co-requisite.

CISD 31 –Database Management – Oracle: Oracle database management system (DBMS) functions, concepts, and terms. Procedure Language/Structure Query Language (PL/SQL) is used to code, test, and implement stored procedures, functions, triggers, and packages. Relational database projects will be built using PL/SQL. Concurrent enrollment in CISD 31L is required.

CISD 31 L - Database Management - Oracle Laboratory: Laboratory for CISD 31 - Oracle database management system (DBMS) functions, concepts, and terms. Procedure Language/Structured Query Language (PL/SQL) is used to code, test, and implement stored procedures, functions, triggers, and packages. Relational database projects will be built using PL/SQL. Concurrent enrollment in CISD 31 is required.

CISB 60 – Machine Learning in Business: A broad introduction to machine learning and its implementation to solve real-world business problems. Includes end-to-end process of investigating data through a machine learning lens and how to extract and identify useful features that best represent data and evaluate the performance of different machine learning algorithms. Topics include: supervised learning (linear regression, logistic regression, support vector machines, k-nearest neighbors, decision trees, random forest, and gradient boosted tree); unsupervised learning (clustering, dimensionality reduction, kernel methods); reinforcement learning and adaptive control.

CISB 61-Deep Learning in Business:  To learn the most cutting-edge deep learning algorithms and technique. Covers building deep learning prediction models of different complexities, from simple linear logistic regression to major categories of neural networks including convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTMs), gated recurrent units (GRUs), and more. Structured around special coding blueprint approaches not mathematical complexities. Valuable hands-on experience with real-world business challenges.

CISB 62- Natural Language Processing in Business: To learn natural language processing and its application in business. Regular expressions. Tokenization and text normalization. Part of speech tagging and grammar parsing. Extracting named entities from text. Feature engineering for text using count vector and term frequency-inverse document frequency (TF-IDF) representations of text. Mastering the art of text cleaning. Semantics and sentiment analysis. Interpreting patterns from text using laten Dirichlet allocation (LDA) and non-negative matrix factorization (NMF) topic models. Text generation with long short term memory algorithm. Creating chatbots.

Upon the completion of those courses the program goal mentioned above will be achieved.

Seq	Course	Title	Units	Year/Semester (Yr or S1)
1	CISB 11	Computer Information Systems	3.5	Yr 1
2	MATH 110	Elementary Statistics	3	Yr 1
3	CISP 71	Programming in Python	3	Yr 1
4	CISP 71 L	Programming in Python Laboratory	0.5	Yr 1
5 a	CISD 21	Database Management - Microsoft SQL Server	3	Yr 1
5 b	CISD 21 L	Database Management - Microsoft SQL Server  Laboratory	0.5	Yr 1
Or
5a	CISD 31	Database Management - Oracle	3	Yr 1
5b	CISD 31 L	Database Management – Oracle Laboratory	0.5
6	CISB 60	Machine Learning in Business	3.5	Yr 2, Fall
7	CISB 62	Deep Learning in Business	3.5	Yr 2, Spring
8	CISB 63	Natural Language Processing in Business	3.5	Yr 2, Spring

No inventory records need to be made inactive or changed in connection with this program. This associate degree does not replace any existing programs at the college.

None.

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