Full Course Description List:

DRWA, DRWI, DSCI Courses

0 credits

This course is part of Walden's commitment to help prepare students to meet the university's expectations for writing in courses at the doctoral level. In this course, students write a short academic essay that will be scored by a team of writing assessors. Based on the essay score, students will be guided toward any further recommended or required writing support needed to meet writing proficiency standards. This required course is free. Students will be enrolled automatically in it after they complete their first term of their doctoral program.

0 credits

This course is part of Walden's commitment to help prepare students to meet the university's expectations for writing in courses at the doctoral level. In this course, students write a short academic essay that will be scored by a team of writing assessors. Based on the essay score, students will be guided toward any further recommended or required writing support needed to meet writing proficiency standards. This required course is free. Students will be enrolled automatically in it after they complete their first term of their doctoral program.

0 credits

This course is part of Walden's commitment to help prepare students to meet the university's expectations for writing in courses at the doctoral level. In this course, students write a short academic essay that will be scored by a team of writing assessors. Based on the essay score, students will complete or be exempted from additional required writing support needed to meet writing proficiency standards. This required assessment course is free. Students will be enrolled automatically in it at the beginning of their doctoral program.

0 semester credits

This course is part of Walden's commitment to help prepare students to meet the university's expectations for writing in courses at the doctoral level. In this course, students write a short academic essay that will be scored by a team of writing assessors. Based on the essay score, students will complete or be exempted from additional required writing support needed to meet writing proficiency standards. This required assessment course is free. Students will be enrolled automatically in it at the beginning of their doctoral program.

0 credits

The Dissertation Intensive has been designed to help students make progress in writing their dissertation proposals. This supportive experience provides an opportunity for students to work directly with faculty members who have extensive experience with qualitative, quantitative, and mixed methods research design. At least one of the members of the team is an experienced faculty member from the Walden University Writing Center who can help students strengthen skills required to write the dissertation. In addition, the Dissertation Intensive also provides an opportunity for students to network with other students who are at the same stage of the writing process and the potential to develop writing support groups.

0 credits

The Consulting Skills Intensive (CSI) is a hands-on learning experience featuring role play, teamwork, observed integration, and real time feedback and coaching. The skills intensive provides intense and in-person immersion into the scenarios and case studies that directly relate to the day-to-day challenges and opportunities participants may encounter in their current and future workplaces. The retreat is a participant-centered environment in which there is ongoing collaboration and interaction through discussion, practice, feedback, reflection, integration, teamwork, and coaching.

0 credits

This virtual residency experience provides students in the Behavioral Health Leadership program with knowledge and practice skills necessary to successfully complete their capstone courses, resulting in their Doctoral Study. Students will review the Doctoral Study capstone requirements, the Baldridge Excellence Framework, role of research/researcher, and the elements of the Prospectus. At the end of the residency, an outline of the Prospectus should be submitted to gain credit for the residency. Also, students are expected to submit a summary of their residency journal that documents their virtual residency experience. Participation in all sessions and submission of daily assignments is required in order to obtain credit.

Each day will include a seminar where the chairperson covers selected topic(s). Students will create an outline of the prospectus and also set up times to work individually with the chairperson in scheduled mentoring sessions.

0 credits

In this intensive experience, students immerse themselves in the coaching context to practice and reinforce coaching skills acquired during IPSY 6753/8753 - Leadership Coaching: Theories, Concepts, and Processes. Students participate in live virtual coaching sessions through various roles: coach, client, and observer. Each synchronous coaching practice session emphasizes Board Certified Coach competencies and ethical guidelines as well as coaching best practices. Small groups work closely with faculty members for 4 days on a final project that requires synthesis of and reflection on skills learned during the intensive.

Prerequisites

• IPSY 6753 or IPSY 8753

Prerequisites

• IPSY 8755 or CPSY 8755 or PSYC 8755 or DPSY 8755 or IPSY 6755 or DPSY 6755 or PSYC 6755 or PSYC 8752 or PBHL 8755 or EDDD 8755

0 credits

In this intensive experience, students immerse themselves in the coaching context to practice and reinforce coaching skills acquired during IPSY 6753/8753 - Leadership Coaching: Theories, Concepts, and Processes. Students participate in live virtual coaching sessions through various roles: coach, client, and observer. Each synchronous coaching practice session emphasizes Board Certified Coach competencies and ethical guidelines as well as coaching best practices. Small groups work closely with faculty members for 4 days on a final project that requires synthesis of and reflection on skills learned during the intensive.

Prerequisites

• IPSY 6753/8753
• IPSY 6755/8755

5 credits

The importance of data science cuts across nearly all major industries and companies. In this course, students explore the scope and multidisciplinary nature of data science with a focus on solving problems using data from across disciplines. Students will see how tools like Watson Analytics can be used to discover patterns and meaning in data. They consider ethical considerations related to data science applications, develop a questioning mindset, and explore a data science framework that can be applied to any industry, business, or organization.

5 credits

Once all the data is collected and analyzed, data analysts need to be able to communicate implications of the data in a way that all stakeholders can understand. In this course, students use data visualization tools and techniques to tell a compelling story with data. Students apply principles of effective communication and share insights and implications of an authentic data analysis project.

Prerequisites

• DSCI 2010
• PSYC 3002 or STAT 2001 or STAT 2002 or STAT 3001 or STAT 3401

5 credits

Data scientists add value to any enterprise by sorting through mountains of data, identifying patterns, and communicating important insights to stakeholders. In this course, students examine how data scientists prepare, cluster, and analyze data to solve real-world problems and predict future trends. Through real-world applications and examples, students analyze common data domains and tasks and apply principles of data analytics to solve problems from a variety of organizations and industries.

Prerequisites

• DSCI 2020

5 credits

In this course, students walk through various problem-solving scenarios in which data scientists demonstrate the use of various data science tools and techniques and explain approaches to querying data using different tools. Through clear, easy-to-understand tutorials, students practice and apply data science tools and techniques and demonstrate the value of using data science tools to solve problems.

Prerequisites

• DSCI 3030

3 semester credits

Students in this course are provided a critical "state-of-the-art" breadth-first review of the domain of information technology (IT). Designed to provide students with a wide-ranging introduction to topics such as software engineering, cybersecurity, and big data analytics, students completing the course have a comprehensive global view of the current IT landscape in the context of both commercial and noncommercial enterprises. The class blends both theory and practice to provide a solid foundation for future study. Students study the relationship between technological change, society and the law, and the powerful role that computers and computer professionals play in a technological society.

3 semester credits

Students in this course are introduced to the concepts of artificial intelligence and emergent areas of intelligent systems. Students have the opportunity to gain a critical understanding of knowledge representation, reasoning, machine learning, and evolutionary techniques. Students are presented with real-world problems and have the opportunity to apply "intelligent" techniques to provide solutions to these problems.

3 semester credits

Cloud computing has attained great commercial significance in recent years. As companies seek to drive down the capital (and recurrent) costs of doing business, using cloud computing to reduce expenditure by outsourcing aspects of the organizations' IT infrastructure to external, web-accessible systems has become a critical goal. In this course, students study the key concepts, theories, techniques, and practices that underpin cloud computing, including the main abstraction, component and deployment models that characterize cloud computing. Students have the opportunity to critically appreciate issues and problems, as well as cutting-edge solutions, pertaining to cloud computing.

3 semester credits

In order to create a competitive advantage, organizations store and analyze information in a variety of formats. This course covers key areas of database systems, such as requirements, design, implementation, security, performance, and scalability. Through a hands-on approach and practical projects, students have an opportunity to design and build database systems using the latest database technologies.

3 semester credits

Students in this course are provided with a comprehensive understanding of big data tools and techniques, related issues, and the different kinds of big data ecosystems that can be used to support advanced data analytics. Students consider big data management frameworks in general, but with a focus on the Hadoop open source distributed data storage and processing platform and its underpinning subsystems. Additionally, the course content introduces students to the role of big data systems in data-driven decision-making.

3 semester credits

In this course, students are provided with an in-depth understanding of the concepts of data mining, including the end-to-end processes involved and the major data mining tools and techniques in common usage. During the course, students have the opportunity to apply such tools and techniques to a variety of example data sets in order to gain a critical insight into their operation and an understanding of when and where such tools and techniques can best be applied. Students also have the opportunity, using the 'R' programming language, to implement several different kinds of data mining algorithms to gain a comprehensive understanding of their operation.

3 semester credits

Statistical analysis supports quality management, drives decision making, enables forecasting and prediction, and provides a means for understanding many aspects of our world. Data is everywhere in today's integrated technological society, and statistical analysis provides the means to access and interpret data. Students in this course are introduced to statistics focused on working with complex data sets and analyzing big data. Students synthesize theory with practical applications to learn the fundamentals of statistical reasoning, descriptive statistics, visual data display, regression, hypothesis testing, research design, anomaly detection, and advanced analysis practices. They have the opportunity to practice using a statistical software package to solve statistical problems. Students use a publicly available big data set to formulate their own study and complete a statistical analysis.

3 semester credits

Students learn key approaches to the integration of enterprise-wide information to support business strategy and decision making. They cover issues in data acquisition, storage, retrieval, and analysis in this course. Topics include data warehouses; data marts; dashboards, key performance indicators, and scorecards; online analytical processing; and data visualization.

3 semester credits

Students in this course are provided with insight into how predictive analytics can be used within organizations. In completing this course, students have the opportunity to gain a comprehensive understanding of how results from predictive analytics can be used by organizations to grow their customer base and run operations more efficiently. This course is oriented toward the practical applications of predictive analytics.

3 semester credits

Big data normally refers to petabytes (1000 terabytes) or exabytes (one billion gigabytes) of unstructured data. This amount of data requires new methods to analyze, visualize, and present these data in a way that yields insight and understanding. Students in this course are introduced to elementary graphics programming, focusing primarily on two-dimensional vector graphics and the programming platforms for graphics. This infrastructure will also include lessons on the human side of visualization, studying human perception and cognition to gain a better understanding of the target of the data visualization.