Big Data Analytics for Business

The A.S. Degree in Big Data Analytics 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, big data and data mining. Students will learn how to make business decisions based upon the analysis of large amounts of data. Students will learn about processing, integrating, modeling, mining, and analytics related to big data. Students will use frameworks, tools, services, and programming languages to analyze data. 

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Students completing this associate degree will conduct predictive data analysis and data mining with large amounts of diverse data to make effective business decisions.

The goals and objectives for the courses offered to obtain this associate degree will give students the basic skills needed to obtain one of several specialized positions. Students will develop competencies that relate to each of the following occupations: computer and information research scientists, data warehousing specialists, business intelligence analysts, search marketing strategists, and clinical data managers.

Big Data Analytics for Business associate degree involves every aspect of acquiring, processing, integrating, modeling, mining, and analyzing big data to make business decisions applying statistical methods.

Student Learning Outcomes for the Big Data Analytics for Business Associate degree:

Students completing this associate degree will conduct predictive data analysis and data mining with large amounts of diverse data to make effective business decisions.

The A.S. Degree in Big Data Analytics 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, big data and data mining. Students will learn how to make business decisions based upon the analysis of large amounts of data. Students will learn about processing, integrating, modeling, mining, and analytics related to big data. Students will use frameworks, tools, services, and programming languages to analyze data.  

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 CISD 41, CISD 42, and CISD 43)

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 CISD 41, CISD 42, and CISD 43)

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 CISD 41, CISD 42, and CISD 43)

And either (CISD21 and CISD21 L) or (CISD31 and CISD31L) (All this information is required for CISD 41, CISD 42, and CISD 43)

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.

CISD 41 – Introduction to Data Science: Introduces students to the evolving domain of data science. Addresses the key knowledge domains in data science, including data development and management, statistical analysis, data visualization, and inference. Provides an exposure to some of the technologies involved in application of data science. Goals are to learn how to use tools for acquiring, cleaning, analyzing, exploring, and visualizing data; making data-driven inferences and decisions; and effectively communicating results.

CISD 42- Big Data Integration and Processing: Learn Big Data: why and where. Characteristics of Big Data and dimensions of scalability. Use Big Data frameworks and tools. Retrieve data from example database and Big Data management systems. Acquire and ingest Big Data. Get value out of Big Data by using a 5-step process to structure an analysis. Process Big Data using various technologies. Identify when a Big Data problem needs data integration. Integrate Big Data and warehouse data using various technologies. Describe the connections between data management operations and the Big Data processing patterns needed to utilize them in large-scale analytical applications.

CISD 43- Big Data Modeling and Analysis: Introduces students to various Big Data management systems and analytical tools. Addresses data mining vs predictive analytics. Provides an exposure to data modeling, data mining, text mining, analytics, real-time analytics, and graph analytics from Big Data perspective.

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
5 a	CISD 31	Database Management - Oracle	3	Yr 1
5 b	CISD 31 L	Database Management – Oracle Laboratory	0.5
6	CISD 41	Introduction to Data Science	3.5	Yr 2, Fall
7	CISD 42	Big Data Integration and Processing	3.5	Yr 2, Spring
8	CISD 43	Big Data Modeling and Analysis	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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