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Program Overview

Programming

Programming Certificate

The Programming post-baccalaureate certificate helps students develop skills in programming as well as systems analysis, database design and administration, and information technology project management. Working with Java, students learn how to program, but they also learn how to meet the business requirements of enterprise design, software implementation, data needs and databases, and how information systems serve the larger goals of a business or organization.

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About the Programming Certificate Program

Programming Goals and Courses

Additional Information

Students should have some general experience with computers before enrolling.  No analysis, database, programming or managerial experience is expected.

CIS 110 Introduction to Computer Programming is offered as a prerequisite for CIS 212, if needed.

Programming Tuition

Post-baccalaureate students at Northwestern's School of Professional Studies pay per course. For more information about financial obligations and tuition, please visit the Tuition page.

Admission for the Programming Certificate

In addition to completing an online application, you'll also need to submit a few supplemental materials. A list of requirements for admission including application deadlines and tips on how to apply can be found on the Admission page.

Programming Registration Information

Whether you're a first-time registrant or current and returning student, all students register using our online student registration and records systems. Important information about registering for courses at SPS, including registration timelines and adding or dropping courses in which you are already enrolled, can be found on the Registration Information page.

Find out more about the Programming Certificate

Program Courses:Course Detail
Intro to Object-Oriented Programming <> CIS 212-CN

This course introduces core elements of object-oriented programming and teaches how to transfer those concepts into Java language. First, the basics of the Java language are given an overview: variable, conditionals, looping and user-defined methods. Classes/objects, data hiding/encapsulation, inheritance and aggregation as principles of object-oriented programming will be introduced through interactive lectures and labs.

Note: Enrollment restricted to students who have completed CIS 110-CN. Instructor consent (permission number) is required for all other students.

May not be audited or taken P/N. 


View CIS 212-CN Sections
Intermediate Programming <> CIS 314-CN

The concepts and practices of advanced object-oriented software design and development are covered in this course. Students' programming foundation is enhanced through study of advanced concepts behind object orientation, including role-based programming, advanced concepts of inheritance, interface development, design patterns, and test-driven development. Using this foundation, students learn the real-world aspects of object orientation by putting the concepts into practice and by using a contemporary object-oriented programming language. Prerequisite: CIS 212 or equivalent programming course. May not be audited or taken P/N.


There is no available section.
Database Systems Design <> CIS 317-CN

This course covers the fundamentals of database design and management. Topics include the principles and methodologies of database design, database application development, normalization, referential integrity, security, relational database models, and database languages. Principles are applied by performing written assignments and a project using an SQL database system.


View CIS 317-CN Sections
Python for Data Science <> CIS 323-CN

This course provides an extensive overview of Python programming language with emphasis on capabilities to analyze data. Students will develop skills to use Python language to perform data science tasks including importing data from various sources, exploratory data analysis, visualization, data preprocessing, descriptive analytics and predictive modeling. Python-based, open source development frameworks, including the Scikit-Learn, pandas, numpy and Matplotlib libraries, are exposed to students. This course takes a hands-on approach to prepare students for applying appropriate python functions to analyze structured and unstructured data. The course will conclude with student completing data science project on real-world dataset.

Prerequisites: STAT 202 Introduction to Statistics and CIS 212 Introduction to Object-Oriented Programming, or equivalents.

This course was formerly CIS 395-CN Topics: Python for Data Science. 


View CIS 323-CN Sections
Applied Data Science <> CIS 324-CN

This course introduces data science concepts, techniques, and tools with an emphasis on building practical business applications. Students will gain the ability to decompose a business problem into actionable data science tasks that include exploratory data analysis, data visualization, data preprocessing and building predictive models using right algorithms. Using Python, students will implement supervised and unsupervised machine learning methods. The students will be exposed to a variety of machine learning algorithms including regression, classification, clustering, dimensionality reduction, and neural networks. This course takes a hands-on approach to prepare students to use data science with the focus on building data products for various industries. 

Prerequisite: CIS 323 Introduction to Python or equivalent.

This course was formerly CIS 395-CN Topics: Principles of Data Science.


View CIS 324-CN Sections
Enterprise Software Development <> CIS 365-CN

This course addresses the increasing need to integrate a broad range of data, information systems, and technologies across organizations to serve business goals. It will help students to understand how to implement comprehensive systems, such as ERPs, across an organization, and consider the impact on business processes. Other topics include basic concepts of distributed architectures, network communications, middleware, web services, and service-oriented architectures designed to meet the needs of today's complex organizations. May not be audited or taken P/N. Prerequisite: CIS 314 or equivalent (two programming courses).


There is no available section.
System Analysis and Design <> CIS 370-CN

This course provides an overview of the systems development lifecycle (SDLC), with an emphasis on developing quality software systems that meet business requirements and goals. Students acquire the basic skill set needed by business analysts in today's complex development environment. May not be audited or taken P/N.


View CIS 370-CN Sections
Topics: Python for Data Science <> CIS 395-CN

This course provides an extensive overview of Python programming language with emphasis on capabilities to analyze data. Students will develop skills to use Python language to perform data science tasks including importing data from various sources, exploratory data analysis, visualization, data preprocessing, descriptive analytics and predictive modeling. Python-based, open source development frameworks, including the Scikit-Learn, pandas, numpy and Matplotlib libraries, are exposed to students. This course takes a hands-on approach to prepare students for applying appropriate python functions to analyze structured and unstructured data. The course will conclude with students completing data science project on real-world dataset.


There is no available section.
Topics: Principles of Data Science CIS 395-CN

This course provides an overview of data science concepts, techniques, and tools with emphasis on building practical applications using real-world datasets. Students will learn to decompose a business problem into actionable data science tasks that include exploratory data analysis, data visualization, data preprocessing, building model using right algorithms and model evaluation. Open source development frameworks, including Python, TensorFlow and the Scikit-Learn libraries, are used to implement supervised and unsupervised learning methods, and students are exposed to a variety of machine learning algorithms. These include regression, classification, clustering, dimensionality reduction, and neural networks. This course takes a hands-on approach to prepare students build data products for industries such as finance, retail, and healthcare using python ecosystem. The course will conclude with student completing a data science project on real-world dataset.


There is no available section.
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