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Analytics certificate

credits 18 | cost/credit $425 | completion 1 year (3 semesters)

Northwestern's online analytics certificate is a 6-course program designed to be completed in 12 months (3 semesters), taking one course at a time. The courses build on statistics and computational learning, so students must have taken both a statistics course and a computer programming course—or have equivalent work experience—in order to be successful.

Requirements

CSC 481 - Introduction to Data Analytics (2 credits, 8 weeks)

Data analytics is an emerging interdisciplinary area of study focused on making more intelligent decisions through the analysis, interpretation and visualization of large data sets. It is related to, and sometimes used interchangeably with, the terms "data science," "business intelligence" or "business analytics." This course provides an overview of the major concepts and topics in data analytics, including ETL (Extract-Transformation-Load) processes, statistical analysis, programming and scripting and visualization. Familiarity with basic concepts of computer programming, relational databases, and/or statistics is helpful, but not required. The course materials will include an overview of the necessary background material and additional resources will be provided for self-study as needed. Prerequisites: MAT116QR or MAT117QR, and CSC171QR; or permission of Computer Science department chair. (2 credits)

Data analytics is an emerging interdisciplinary area of study focused on making more intelligent decisions through the analysis, interpretation and visualization of large data sets. It is related to, and sometimes used interchangeably with, the terms "data science," "business intelligence" or "business analytics." This course provides an overview of the major concepts and topics in data analytics, including ETL (Extract-Transformation-Load) processes, statistical analysis, programming and scripting and visualization. Familiarity with basic concepts of computer programming, relational databases, and/or statistics is helpful, but not required. The course materials will include an overview of the necessary background material and additional resources will be provided for self-study as needed. Prerequisites: MAT116QR or MAT117QR, and CSC171QR; or permission of Computer Science department chair. (2 credits)

CSC 482 - Introduction to Text Analytics (3 credits, 8 weeks)

Text analytics is the process of analyzing, searching and retrieving unstructured text. Introduction to Text Analytics presents an introduction overview of the field, incorporating topics such as text preprocessing, categorization and clustering. Familiarity with the basic concepts of computer programming and statistics is helpful, but not required. The course materials will include an overview of the necessary background material and additional resources will be provided for self-study as needed. Prerequisites: MAT116QR or MAT117QR, and CSC171QR; or permission of Computer Science department chair. (3 credits)

Text analytics is the process of analyzing, searching and retrieving unstructured text. Introduction to Text Analytics presents an introduction overview of the field, incorporating topics such as text preprocessing, categorization and clustering. Familiarity with the basic concepts of computer programming and statistics is helpful, but not required. The course materials will include an overview of the necessary background material and additional resources will be provided for self-study as needed. Prerequisites: MAT116QR or MAT117QR, and CSC171QR; or permission of Computer Science department chair. (3 credits)

CSC 483 - Introduction to Predictive Analytics (3 credits, 8 weeks)

Introduction to Predictive Analytics provides an overview of applied predictive techniques through description, discussion and hands-on exercises. Its purpose is to equip students to effectively apply the right tool to solve data problems too large or too difficult to be solved with conventional methods. Familiarity with basic concepts of computer programming and statistics is helpful, but not required. The course materials will include an overview of the necessary background material and additional resources will be provided for self-study as needed. Prerequisites: MAT116QR or MAT117QR; CSC171QR, CSC481 and CSC491. (3 credits)

Introduction to Predictive Analytics provides an overview of applied predictive techniques through description, discussion and hands-on exercises. Its purpose is to equip students to effectively apply the right tool to solve data problems too large or too difficult to be solved with conventional methods. Familiarity with basic concepts of computer programming and statistics is helpful, but not required. The course materials will include an overview of the necessary background material and additional resources will be provided for self-study as needed. Prerequisites: MAT116QR or MAT117QR; CSC171QR, CSC481 and CSC491. (3 credits)

CSC 491 - Advanced Data Analytics (4 credits, 8 weeks)

Data analytics is an emerging interdisciplinary area of study focused on making more intelligent decisions through the analysis, interpretation and visualization of large data sets. It is related to, and sometimes used interchangeably with, the terms "data science," "business intelligence" or "business analytics." This course provides an overview of the major concepts and topics in data analytics, including ETL (Extract-Transform-Load) processes, statistical analysis, programming and scripting, and visualization. Familiarity with the basic concepts of computer programming, relational databases, and/or statistics is helpful, but not required. The course materials will include an overview of the necessary background material and additional resources will be provided for self-study as needed. Prerequisites: MAT116QR or MAT117QR, MAT111QR or MAT112QR, CSC171QR; or permission of Computer Science department chair. (4 credits)

Data analytics is an emerging interdisciplinary area of study focused on making more intelligent decisions through the analysis, interpretation and visualization of large data sets. It is related to, and sometimes used interchangeably with, the terms "data science," "business intelligence" or "business analytics." This course provides an overview of the major concepts and topics in data analytics, including ETL (Extract-Transform-Load) processes, statistical analysis, programming and scripting, and visualization. Familiarity with the basic concepts of computer programming, relational databases, and/or statistics is helpful, but not required. The course materials will include an overview of the necessary background material and additional resources will be provided for self-study as needed. Prerequisites: MAT116QR or MAT117QR, MAT111QR or MAT112QR, CSC171QR; or permission of Computer Science department chair. (4 credits)

CSC 492 - Advanced Text Analytics (3 credits, 8 weeks)

Advanced Text Analytics will provide a thorough exposition of text analytic techniques, with a focus on classification, information retrieval and extraction as well as predictive analyitics. The purpose of Advanced Text Analytics is to provide an in-depth examination of the text analytics field and draw the course material together into a project. Familiarity with basic concepts of computer programming is expected. Although not a formal prerequisite, CSC482 Introdcution to Text Analytics is recommended. Prerequisites: MAT116QR or MAT117QR, CSC171QR and CSC482. (3 credits)

Advanced Text Analytics will provide a thorough exposition of text analytic techniques, with a focus on classification, information retrieval and extraction as well as predictive analyitics. The purpose of Advanced Text Analytics is to provide an in-depth examination of the text analytics field and draw the course material together into a project. Familiarity with basic concepts of computer programming is expected. Although not a formal prerequisite, CSC482 Introdcution to Text Analytics is recommended. Prerequisites: MAT116QR or MAT117QR, CSC171QR and CSC482. (3 credits)

CSC 493 - Advanced Predictive Analytics (3 credits, 8 weeks)

Advanced Predictive Analytics is an exploration of effective statistical, data-driven, machine-learning techniques that produce useful patterns and correlations. Those patterns and correlations are beneficial for evaluating potential courses of action through predicting prospective outcomes. The purpose of the course is to prepare students to know the correct approach to use in a given set of circumstances, to understand why the approach is correct and to wisely place the use of the approach within the context of organizational utility. The course culminates in a project using one of the approaches of the course. Familiarity with the basic concepts of computer programming and statistics is expected. Although not a formal prerequisite, CSC483 Introduction to Predictive Analytics is recommended. Prerequisites: MAT116QR or MAT117QR, CSC171QR and CSC483. (3 credits)

Advanced Predictive Analytics is an exploration of effective statistical, data-driven, machine-learning techniques that produce useful patterns and correlations. Those patterns and correlations are beneficial for evaluating potential courses of action through predicting prospective outcomes. The purpose of the course is to prepare students to know the correct approach to use in a given set of circumstances, to understand why the approach is correct and to wisely place the use of the approach within the context of organizational utility. The course culminates in a project using one of the approaches of the course. Familiarity with the basic concepts of computer programming and statistics is expected. Although not a formal prerequisite, CSC483 Introduction to Predictive Analytics is recommended. Prerequisites: MAT116QR or MAT117QR, CSC171QR and CSC483. (3 credits)

Total credits required: 18

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