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BS in Data Science

Bachelor of Science in Data Science

Course Delivery

On Campus / Online

Total Credits

120

Tuition Per Year

$17,850

Duration

4 Years

Program Overview

The BS program in Data Science integrates scientific methods from statistics, computer science and data-based business management to extract knowledge from data and drive decision making. Graduates are prepared to meet the challenges at the intersection between big data, business analytics, and other emerging fields.

Learning Goals

    • To meet the growing demand for high-level information systems/science skills
    • To provide a path for individuals from diverse fields to rapidly transition to data science careers
    • Enable established IT and computing professionals to upgrade technical management and development skills
    • Prepare graduates to apply data science techniques for knowledge discovery and dissemination to assist researchers or decision-makers in achieving organizational objectives

Who is the Ideal Student for this program?

A data scientist is someone who makes value out of data. Such individuals proactively fetch information from various sources and analyze it for better understanding about how the business performs, and build AI tools that automate certain processes within the company.

 

All courses are conducted on campus. To succeed in this program, students should be self-disciplined, self-directed and comfortable scheduling their own coursework

Career Outlook

The undergraduate program provides comprehensive education in data science to further develop the knowledge and skills to prepare for a career in data science.

Students will qualify for jobs such as data analyst, data science/analytics manager, database administrator, big data engineer, data mining engineer, machine learning engineer, data architect, data warehouse architect, commercial intelligence manager, competitive intelligence analyst, consultant, strategic business and technology intelligence, manager of market intelligence, director of enterprise strategy, and director of global intelligence.

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Please Click here or just give us a call at (202) 644-7200 to speak with an admissions advisor.




Meet the Department Chair

Pipop

Pipop Nuangpookka, PhD

[email protected]

Dr. Pipop Nuangpookka is the Chair of Information Management Science Program and Director of Distance Education at Bay Atlantic University.

Dr. Pipop Nuangpookka has been working as an Analyst/Programmer, Infrastructure Technologies, and Infrastructure Security since the year 2005. While working in the IT industry as a technical person, He started teaching in Computer Science, Information Technology, and Cybersecurity disciplines at several universities in Washington, DC, and Northern Virginia area in 2007. The various courses include Introduction to Computer Science, Computer Network, Computer Programming Languages (i.e., Java, Python, C, C++, ML, and Prolog), Structure of Programming Languages, Software Engineering, Data Structures & Algorithms, Database Management System (i.e., MS SQL Server, Oracle, MySQL, and SQL/PLSQL), Web Application Development and Security, Technology Management, and Technical Capstone Project. Dr. Nuangpookka graduated with a Bachelor’s Degree in Business Administration from Payap University, Thailand, in 1999, and a Master’s Degree in Computer Science in 2004 from Marymount University, Arlington, Virginia. He also earned a Doctor of Science Degree in Cybersecurity at Marymount University in 2020. His research interests are Non-Destructive Method of Detecting Hardware Attack, Personally Identifiable Information (PII) Security Controls, Web Server Protection, and Bilingual Translator Programming. He is a co-author of scholarly papers published in the Journal of Computing Sciences in Colleges/ACM digital library, including “Hardware-Tampering Security Risks in the Supply Chain” and “Fileless Malware and Programmatic Method of Detection.”

Courses

Students must earn a total of 120 college credit hours to receive this degree. Of these credit hours, 60 credits are core courses, 42 general education credits, and 18 elective credits. Students must meet their core requirements as well as their general education requirements.

 

In addition, students must meet the following criteria:

    • Students enrolled in the undergraduate program must maintain a Cumulative Grade Point Average (CGPA) of at least 2.0 out of 4.0 to qualify for the BA degree, to remain in good standing, and to graduate.
    • The Maximum Time Frame (MTF) for completion of the BA program is 180 credits.
    • An undergraduate student may transfer up to 60 credit hours earned at accredited institutions.
    • No degree credit is received by an undergraduate for any failing grade (a grade less than D, or 1.00 out of 4.00 grade points)
DATA SCIENCE CORE REQUIREMENTS (20 Courses)
BUSN/INTL 240
Law & Ethics
3 Credits
In this course, students will learn about the role of ethics in international relations and international law, and areas in which the law fails to cover ethics. The course will begin by examining instances of unethical practices in diplomacy, war, and international business from the nineteenth century to present. It will examine various laws that have been introduced during the last two centuries in efforts to curtail unethical behavior and laws that have allowed nations to exploit weaker world regions. Students will complete major simulations in order to practice applying ethics to international law and diplomacy and will propose new policies to encourage ethical diplomaticrelations.
CMPS 122
Introduction to Programming I
3 Credits
(Prerequisite CMPS 110) An introductory course in programming, CMPS 122 exposes students to the concepts involved in using higher-level object-oriented programming language. The course will explain the programming process and give students lots of hands-on experience writing small programs during labs.
CMPS 202
Data Structures and Algorithms I
3 Credits
(Prerequisite CMPS 122) The objective of this course is introducing algorithms, algorithm complexities, basic data structures, data organizations, sorting and searching algorithms. This course will also focus on the implementation details of the algorithms
CMPS 205
Data Structures and Algorithms II
3 Credits
(Prerequisite CMPS 202) The objective of this course analyzing time and space requirements of important algorithms and structures. Various data structures such as stacks, queues, trees and graphs will be introduced and analyzed. This course will also focus on the implementation details of the algorithms.
CMPS 211
Computer Networks
3 Credits
An introduction to the design and analysis of computer communication networks. Topics include application layer protocols, Internet protocols, network interfaces, local and wide area networks, wireless networks, bridging and routing, and current topics.
CMPS 222
Programming II
3 Credits
(Prerequisite CMPS 122) This course offers a continuation of the programming skills learned in CMPS 112. Students will learn more advanced applications of a programming language through lab work and independent assignments.
CMPS 226
Introduction to Data Science
3 Credits
A first course in data science. Introduces data science as a field, describes the roles and services that various members of the community play and the life cycle of data science projects. Provides an overview of common types of data, where they come from, and the challenges that practitioners face in the modern world of “Big Data.” Provides an introduction to the interdisciplinary mixture of skills that the practice requires
CMPS 230
Information Visualization
3 Credits
Introduces the foundation and the state of the art of information visualization. Explores and reflects on the design, application, and evaluation of a diverse range of information systems. Demonstrates how a number of common types of information can be visually, intuitively and interactively represented. Provides a first-hand experience of visualizing a variety of realistic data types.
CMPS 318
Database Management Systems
3 Credits
Main objective is understanding database management systems and creating efficient database schemas according to normalization theory. This course covers E-R modelling, database design, relational databases, SQL, relational languages, query optimization, query processing and XML.
CMPS 322
Machine Learning and Pattern Recognition
3 Credits
The objective of the course is to introduce the fundamental mathematical tools needed to analyze algorithms, basic algorithm design techniques, advanced data structures, and important algorithms from different problem domains.
CMPS 337
Information Retrieval Systems
3 Credits
The theoretical underpinnings of information retrieval are covered to give the student a solid base for further work with retrieval systems. Emphasis is given to the process of textual information for machine indexing and retrieval. Aspects of information retrieval covered include document description, query formulation, retrieval algorithms, query matching, and system evaluation.
CMPS 438
Exploratory Data Analytics
3 Credits
(Prerequisite CMPS 226) In this course students learn the essential exploratory techniques for summarizing and analyzing data. The course discusses how to install and configure software necessary for a statistical programming environment. It covers practical issues in statistical computing, which includes programming in R and how to use R for effective data analysis. The course covers the plotting systems in R and some of the basic principles of constructing data graphics.
ISIT 112
Introduction to Information Technology
3 Credits
This course introduces basic issues in information science, including the nature of information, information technology, information security, information policy, information ethics, and the relationships between information technologies and the information context.
ISIT 224
Information Systems Analysis and Design
3 Credits
The goal of this course is to examine the system and the concepts of information system. Students learn analysis and design of the information system.
MATH 110
Introduction to Statistics
3 Credits
This is an introductory course that assumes no prior knowledge of statistics but does assume some knowledge of high school algebra. Basic statistical concepts and methods are presented in a manner that emphasizes understanding the principles of data collection and analysis rather than theory. Much of the course will be devoted to discussions of how statistics is commonly used in the real world
MATH 128
Linear Algebra
3 Credits
Linear algebra is the study of linear systems of equations, vector spaces, and linear transformations. Solving systems of linear equations is a basic tool of many mathematical procedures used for solving problems in science and engineering.
MATH 131
Calculus I
3 Credits
This is an introductory course to provide students with an introduction to Calculus. The course covers topics such as rules of differentiation, the chain rule and implicit differentiation; derivatives of trigonometric, exponential, logarithmic, and inverse trigonometric functions; the Mean Value theorem; and indeterminate forms and L’Hopital’s rule.
MATH 132
Calculus II
3 Credits
(Prerequisite MATH 131) This course builds on skills learned in MATH 140. It covers subjects such as techniques of integration; applications of integration; conics, parametric curves, and polar curves; partial differentiation; and multiple integration.
MATH 140
Discrete Mathematics
3 Credits
The aim of the course is to give students the necessary background in discrete mathematical structures. Basic algorithms on discrete structures will be taught.
MATH 212
Numerical Analysis
3 Credits
Numerical Analysis helps on transforming functions, derivatives, integrals, and differential equations as strings of numbers that can be calculated in the computer. At most important issue in Numerical Analysis is an understanding of the speed of convergence of the series expansions for the method used to approximate or solve a problem.
DATA SCIENCE CORE ELECTIVES (6 Courses)
CMPS 315
Operating Systems
3 Credits
This course examines the important problems in operating system design and implementation. The operating system provides an established, convenient, and efficient interface between user programs and the bare hardware of the computer on which they run. The operating system is responsible for sharing resources (e.g., disks, networks, and processors), providing common services needed by many different programs (e.g., file service, the ability to start or stop processes, and access to the printer), and protecting individual programs from interfering with one another. The course will start with a brief historical perspective of the evolution of operating systems over the last fifty years and then cover the major components of most operating systems. This discussion will cover the tradeoffs that can be made between performance and functionality during the design and implementation of an operating system. Particular emphasis will be given to three major OS subsystems: process management (processes, threads, CPU scheduling, synchronization, and deadlock), memory management (segmentation, paging, swapping), and file systems; and on operating system support for distributed systems.
CMPS 332
Analysis of Algorithms
3 Credits
(Prerequisite CMPS 205) Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this course is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The course provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the course covers a wide array of central topics that have not been addressed by previous courses. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.
CMPS 410
Introduction to Artificial Intelligence
3 Credits
(Prerequisite CMPS 332) The objective of this course is to give the student the ability to apply artificial intelligence techniques, including search heuristics, knowledge representation, planning, reasoning and learning to various problems.
CMPS 426
Bioinformatics
3 Credits
(Prerequisite MATH 110) This course covers computational techniques for mining the large amount of information produced by recent advances in molecular biology, such as genome sequencing and microarray technologies. The methods by which computers are used to manipulate and analyze sequences and structures will also be taught. The outline of the course is arranged to give fundamental concepts of bioinformatics to the students.
CMPS 433
Game Programming
3 Credits
(Prerequisite CMPS 205) This course will support students the emerging trends, and frameworks of gamification, why it has a great potential to apply in IT projects, and how to use it effectively. The course allows students to develop a set of practical skills in using game elements using industrial case studies. Students will understand practical ways for improving a software development business particularly by understanding ways of creating an effective IT solution and exploring the intangible value in business landscapes. Unity game engine will be used as the development environment.
CMPS 477
Image Processing
3 Credits
This course is an introduction to the fundamental concepts and techniques in basic digital image processing and their applications to solve real life problems. The topics covered include Digital Image Fundamentals, Image Transforms, Image Enhancement, Restoration and Compression, Morphological Image Processing, Nonlinear Image Processing, and Image Analysis. Application examples are also included.
CMPS 480
Big Data
3 Credits
(Prerequisite CMPS 318) This course will provide insight into the basics of using "Big Data" to quantify operational implications of management choices. You will learn statistical models, mostly using R software, and analyze them to provide insight regarding the assumptions, value drivers, and risks present in a business situation. You will use your statistical models to explore different ways to think about uncertainty, guide decision-making, and persuasively communicate analytical results. Later in the course, by using the statistical tools learned, we will examine simple, introductory methods to text mining, building search engines and recommendation tools.
ISIT 328
Data Warehouse Design
3 Credits
This course aim is teaching the data warehouse design. At the end of semester, students will learn database concepts and data warehouse concepts.
ISIT 350
Advanced Web Application Design
3 Credits
This course teaches advanced web application design using Java ServerFaces web framework. Understanding managed beans, page navigation rules, expression language, data validation and conversion, AJAX support, application security, building custom components and related topics will be covered within the scope of this course.
ISIT 355
Advanced Mobile Application Development
3 Credits
Technology continues to evolve and provide us with increasingly powerful mobile devices. Thus, applications that can run on a browser must also be written such that they are compatible with mobile devices, the majority of which are now web-enabled. Meanwhile, there is an increasing demand for native applications that can be downloaded to and run on mobile devices. This course will address these trends, teaching you to think about the unique design and deployment issues that must be taken into consideration when developing applications for mobile devices.
ISIT 362
Social Network Analysis
3 Credits
The course presents mathematical methods and computational tools for Social Network Analysis (SNA). SNA was pioneered by sociologist, but recently became an interdisciplinary endeavor with contributions from mathematicians, computer scientists, physicists, economists etc., who brought in many new tools and techniques for network analysis. In this course we will start with basic statistical descriptions of networks, analyze network structure, roles and positions of nodes in networks, connectivity patterns and methods for community detection. In the second part of the course we will discuss processes on networks and practical methods of network visualization.
ISIT 370
Agile Project Management
3 Credits
(Prerequisite MGMT 200) This course covers an introduction to agile project management, fundamental principles and practices about agile project development and management.
MGMT 200
Introduction to Project Management
3 Credits
Introduction to Project Management utilizes a simulated team project to manage a project’s life cycle. Emphasis is placed on activity networks, managing resources, and creating control mechanisms that minimize risk. Project leadership is explored in the context of building effective project teams and maintaining stakeholder relationships. Students will learn and apply basic project management concepts including triple constraint, planning, scheduling, work breakdown structures and project control.
GENERAL ELECTIVES: HUMANITIES
ENGL 121
English Composition 1
3 Credits
This course is required for students with moderate scores on the BAU English composition test. ENGL 121 develops the student’s ability to organize ideas and use critical thinking skills. The course will also review English grammar and writing mechanics. Students will learn to construct persuasive arguments and critical essays. They will practice personal reflection; analyze literature, film, and journalism; participate in the peer-review and editing processes; and learn about proper use of citations. Course materials may vary by professor.
ENGL 122
English Composition 2
3 Credits
This course is open to students with high scores on the BAU English composition test. ENGL 122 develops the student’s ability to organize ideas and use critical thinking skills. The course will also review English grammar and writing mechanics. Students will learn to construct persuasive arguments and critical essays. They will practice personal reflection; analyze literature, film, and journalism; participate in the peerreview and editing processes; and learn about proper use of citations. Course materials may vary by professor.
ENGL 123
Academic Writing
3 Credits
This course is open to students with high scores on the BAU English composition test, or students who have completed ENGL 121. Academic writing and research abilities are essential for college students and professionals. During this course, students will hone their research skills and complete a short research paper on a subject of their own choice. Throughout the course, students will participate in peer-review, learn to create research paper outlines and drafts, learn to use citations properly, and learn about research and writing resources at BAU and around D.C.
FREN 101
Elementary French 1
3 Credits
An introduction to the French language for students with no prior experience. Students will practice reading, writing, listening, and speaking French. Cultural instruction on the Francophone world will also prove a foundational aspect of this course.
FREN 121
Elementary French 2
3 Credits
(Prerequisite FREN 101) A continuation of the reading, writing, listening, and speaking abilities introduced in FREN 101. Students will learn more about Francophone cultures. By the end of this course, students will be able to carry a conversation in French.
SPAN 101
Elementary Spanish 1
3 Credits
An introduction to the Spanish language for students with no prior experience. Students will practice reading, writing, listening, and speaking Spanish. Cultural instruction on Spain and Latin America will also prove a foundational aspect of this course.
SPAN 121
Elementary Spanish 2
3 Credits
(Prerequisite SPAN 101) A continuation of the reading, writing, listening, and speaking abilities introduced in SPAN 101. Students will learn more about Spanish and Latin American cultures. By the end of this course, students will be able to carry a conversation in Spanish.
TURK 101
Elementary Turkish 1
3 Credits
An introduction to the Turkish language for students with no prior experience. Students will practice reading, writing, listening, and speaking Turkish. Instruction on Turkish culture will also prove a foundational aspect of this course.
TURK 121
Elementary Turkish 2
3 Credits
(Prerequisite TURK 101) A continuation of the reading, writing, listening, and speaking abilities introduced in FREN 101. Students will learn more about Turkish culture. By the end of this course, students will be able to carry a basic conversation in Turkish.
GENERAL ELECTIVES: MATHEMATICS & THE SCIENCES
ENVS 105
Introduction to Environmental Science
3 Credits
According to the US National Oceanographic and Atmospheric Agency, 2016 was the warmest year on record. According to NASA, it was the warmest year for the last 125,000 years. How has human activity affected the climate so dramatically? This and other vital questions about pollution, how the environmental system operates, and the interaction between the oceans, the atmosphere, and the land will be addressed in this course.
CMPS 110
Introduction to Computer Science
3 Credits
An introduction to computer programming, the concepts involved in the use of higher-level language, and the program development process. The goal of this course is sufficiency in the design and implementation of programs of significant size of complexity. It will cover topics such as algorithms, file I/O, and basic data structures. This course is quite demanding, because of the length of programming exercises assigned.
MATH 103
College Mathematics
3 Credits
Mathematical calculations underlie the development of theories, the evaluation of trends, and the assessment of progress in all aspects of society. It will cover linear, quadratic, and simultaneous equations and the graphing of lines, circles, exponential functions, and polynomial functions.
MATH104
College Algebra
3 Credits
(Prerequisite MATH103) This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines. Linear algebra is a branch of mathematics that studies systems of linear equations and the properties of matrices. The concepts of linear algebra are extremely useful in physics, economics and social sciences, natural sciences, and engineering. Due to its broad range of applications, linear algebra is one of the most widely taught subjects in college-level mathematics (and increasingly in high school).
GENERAL ELECTIVES: SOCIAL SCIENCES
PSYC 101
Introduction to Psychology
3 Credits
This course will provide students with an introduction to the key theories of psychology. The course will discuss topics such as neuroscience and cognition; the processes of learning, perception, and memory; language and social behavior; intelligence, personality, and development; and psychopathology.
HIST 166
Atlantic History
3 Credits
The accidental encounter of Christopher Columbus and the Taíno in 1492 initiated profound changes for the societies surrounding the Atlantic basin--those of the Americas, Europe, and Africa. This course explores those changes from 1492 through the Age of Revolutions. Students will examine major themes in Atlantic history, including the process of European colonization of the Americas; Amerindian-European interactions; the global political, economic, and socio- cultural effects of the Atlantic slave trade and plantation slavery; and the development of revolutionary movements in Haiti, France, and the future United States.
HIST 168
History of Civilizations
3 Credits
This course develops a basic understanding of the history of major world cultures. The course provides a broad picture that deals with the nature and spread of the earliest civilizations in the Ancient Near East and the development of civilization in classical and medieval Europe, concerning their political, social, economic and religious life; focuses on the globalization process of the civilization. The course, therefore, provides an important overview of cultures and meetings between cultures and how these cultures constantly move towards an integrated society.
HIST 170
U.S. History
3 Credits
This course will explore the history of the United States from its origins in the eighteenth century to 9/11. The course will explore topics such as indigenous cultures, colonialism, slavery, and immigration; the Enlightenment and early American democracy; capitalism, plantation labor, and industrialization; abolitionism, the Civil War, and Reconstruction; the World Wars, the Civil Rights Movement, and the Cold War; and, finally, the effects of 9/11 on American society. Overall, students will leave the course with a firm understanding of the complex dynamics of race, gender, migration, politics, and economics in American society. Students will learn to think critically about primary and secondary sources, including works of writing, art, music, and literature, and will conduct independent research. They will also improve their written and oral communication abilities.
SOCI 101
Introduction to Sociology
3 Credits
In this introductory course, students will learn about the field of Sociology and how it helps us understand our world. We will discuss key themes of sociological study, including inequality, racism and ethnicity, gender and sexuality, age stratification, and culture. Students will also learn about a variety of research methodologies
POLS 250
Media Literacy in the Age of Fake News
3 Credits
Media Literacy is the ability to access, analyze, evaluate and create media in a variety of forms, from print to video to the Internet. This course aims at building an understanding of the role of media in society as well as essential skills of inquiry and self-expression necessary for citizens of a democracy. Upon completion of the course, students are expected to become competent, critical and literate in all media forms so that they control the interpretation of what they see or hear rather than letting the interpretation control them.
UNIV 101
First Year Seminar
3 Credits
To help new students make a successful transition to campus, both academically and personally. The course aims to foster a sense of belonging, promote engagement in the curricular and co-curricular life of the university, develop critical thinking skills and help to clarify purpose, meaning and direction.

 

Official High School transcript (must be in English)

Photocopy of government-issued ID (International students need a passport)

2 letters of recommendation (from former teachers, employers, coaches, etc.)

Letter of Intent – 1-2 page essay on ONE of the following topics:

 

    • Describe, who you are, your purpose and goals, your accomplishments, and why you want to get a higher education degree.
    • What is the most meaningful contribution to others you have made in your life? How do you understand the value of it on others?
    • What is the biggest challenge you have had in your life and how have you dealt with it?
    • What character in history do you associate yourself most with and why?

 

Additional Documents (For International Students ONLY)

    • Bank statement showing proof of adequate financial resources
    • Sponsorship letter (if bank statement is not in the applicant’s name)
    • If the applicant has any dependents, passport copies and additional materials as necessary.
    • Proof of English language proficiency

 

If the High School degree transcript is NOT in English, the applicant must provide a certified English translation.

If the transcript does not clearly indicate the degree awarded, the applicant must provide a notarized copy of the high school diploma.

 

Proof of English language proficiency*

All applicants whose first language is not English must submit proof of English language proficiency to Bay Atlantic University (BAU).  The requirement is waived if:

 

    • the applicant has completed four years of education at an English-language secondary school
    • the applicant has completed Mentora College’s 400C course with a passing grade

 

All other applicants must establish proficiency by providing an official score report of one of our approved standardized English proficiency tests (TOEFL, IELT, TOEIC)

 

EXAM SCORES:

TOEFL (PBT, CBT, IBT): 525, 194, 70

IELTS: 5.5

TOEIC: 650

BAU Placement Test:70 (offered on campus)

Duolingo: 75

Pearson (PTE): 48

Mentora College Intensive English Program: Pass 400C level

 

*For assistance of information on applying, please contact our Admissions team at [email protected]

**For our Frequently Asked Questions, please visit https://bau.edu/faq/

 

Graduation Requirements

The Bachelor of Science in Data Science is earned by completing the program course requirements of 120 credit hours. Of these credit hours, 63 credits are major or core courses, 42 general education credits, and 18 pure elective credits. Students must meet their core requirements as well as their general education requirements. In addition, students must meet the following criteria:

LEARN MORE ABOUT OUR GRADUATION REQUIREMENTS
1.  Students enrolled in the undergraduate program must maintain a Cumulative Grade Point Average (CGPA) of at least 2.0 out of 4.0 to qualify for the BA degree, to remain in good standing, and to graduate.
2. The Maximum Time Frame (MTF) for completion of the BA program is 180 credits.
3. An undergraduate student may transfer up to 60 credit hours earned at accredited institutions.
4. No degree credit is received by an undergraduate for any failing grade (a grade less than D, or 1.00 out of 4.00 grade points).

 

Tuition & Fees

Note: Tuition rates are subject to change and additional fees may vary by program. Please call at (202) 644-2725 for more information.

Per Credit Hour                                                                     Yearly Tuition

$595                                                                                              $17,850






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