The document outlines the growing demand for data science professionals across various industries, emphasizing the need for specialized skills in areas like big data, marketing analytics, healthcare analytics, and cyber security. It details the Master of Science in Data Science program at St. John's University, highlighting hands-on learning opportunities and the importance of practical experience in preparing graduates for successful careers. Various career paths and roles for data science graduates are also discussed, showcasing the versatile applications of data analytics in decision-making processes.

1. Career Outlook
and Education
DATA SCIENCE

2. Table of Contents
Data Science Concentration: Big Data Analytics.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Data Science Concentration: Marketing Analytics .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Data Science Concentration: Healthcare Analytics ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Data Science Concentration: Cyber and Information Security.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Pursuing an M.S. in Data Science: The Value of Hands-On Learning.. . . . . . . . . . . . . . . . . . . . . . . . . 8
Pursuing an M.S. in Data Science: Selecting a Program.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
St. John’s University Data Science Program Faculty.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
St. John’s University Data Science Program..... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3. Data science has emerged as a highly versatile and
in-demand career field that spans across numerous
industries. As organizations across sectors recognize the
value of data-driven insights, data scientists play a pivotal
role in extracting actionable information from complex
datasets. Whether it's healthcare, finance, computer
science, artificial intelligence, e-commerce, machine
learning, marketing, or even entertainment, data scientists
are sought after to analyze data, develop predictive
models, and drive informed decision making.
As the reliance on data continues to grow, the demand for skilled data scientists is expected
to skyrocket. The rapid advancement of technology, coupled with the exponential growth of
data, ensures that data science will remain a critical field in the foreseeable future. At St. John’s
University, you can pursue an M.S. in Data Science to join this promising field in a number
of specialty areas including big data, marketing analytics, healthcare analytics, or cyber and
information security.
Predicted data available globally by 2025
175 Zettabytes
Expected career growth 2021–2031
36%

4. Big data refers to the massive volumes of structured and unstructured data that organizations
collect and analyze to extract valuable insights and make informed decisions. With the
advancement of technology, the amount of data generated is growing exponentially—with
estimates of 328.77 million terabytes of data created each day. Analyzing big data involves
processing and interpreting this seemingly unwieldy information to uncover patterns, trends, and
correlations. The field of big data leverages powerful tools and techniques, including machine
learning and artificial intelligence, to derive insights and then transform those insights into
actionable knowledge.
With a degree in data science specializing in big data, you can pursue a number of positions
aligning with your expertise and interests.
Data Science Concentration: Big Data Analytics
1. Big Data Engineer: Responsible for designing, developing, and managing the infrastructure
and systems required to handle and process large-scale datasets. They work on data ingestion,
storage, and data pipeline architecture to ensure efficient data processing and availability.
2. Data Scientist: Analyze and interpret complex datasets using statistical and machine learning
techniques to derive meaningful insights and solve business problems. They develop models,
algorithms, and predictive analytics to guide data-driven decision making.
3. Data Analyst: Examine and interpret data using statistical methods and tools to identify trends,
patterns, and insights. A data analyst cleans and preprocesses data, performs exploratory data
analysis, and creates visualizations to communicate findings effectively.
4. Data Architect: Design and develop the overall data architecture and strategy for big data
solutions. They ensure data integration, scalability, and security, and collaborate with other
teams to define data models and standards.
5. Business Intelligence Analyst: Extract, analyze, and interpret data to provide actionable insights
for business operations and strategy. They develop reports, dashboards, and visualizations to
facilitate data-driven decision making and support business objectives.
6. Data Mining Specialist: Apply data mining algorithms and techniques to analyze large datasets
and extract valuable patterns, relationships, and trends. They identify opportunities for
optimization, fraud detection, customer segmentation, and other data-driven applications.
7. Machine Learning Engineer: Build and deploy machine learning models and algorithms to
automate processes, develop predictive capabilities, and enable intelligent decision making.

5. Marketing analytics, from a data science perspective, drives decisions in marketing management by
providing insights into consumer behavior, preferences, and trends. It enables informed decisions
on product development, pricing, promotions, and target audience segmentation. The impact of
marketing analytics on marketing holistically has been significant, transforming decision making
from intuition-based to evidence-based strategies. It fosters personalized marketing, customer
relationship management, and real-time monitoring, leading to improved customer satisfaction,
increased return on investment, and agility in decision making. Ultimately, marketing analytics
empowers companies to stay competitive and achieve sustainable growth.
With a degree in data science specializing in marketing analytics, you can pursue a career that
combines data analysis skills with a focus on marketing strategies and consumer insights.
Data Science Concentration: Marketing Analytics
1. Marketing Analyst: Conduct data-driven analysis to evaluate marketing campaigns, measure
their effectiveness, and identify areas for optimization. They provide insights on customer
behavior, segmentation, and targeting strategies to drive marketing decisions.
2. Digital Marketing Analyst: Analyze digital marketing data, such as website traffic, social media
metrics, and online advertising performance. They use data insights to improve digital marketing
strategies, optimize customer acquisition, and enhance user experience.
3. Customer Insights Analyst: Utilize data analysis techniques to uncover patterns and trends
in customer behavior and preferences. They provide actionable insights to support customer
segmentation, targeting, and retention strategies.
4. Marketing Data Scientist: Apply statistical modeling and machine learning techniques to analyze
marketing data and develop predictive models. They work on customer churn prediction,
lifetime value modeling, market forecasting, and other advanced analytics projects.
5. Marketing Operations Manager: Oversee the implementation and management of marketing
technology platforms and data infrastructure. They ensure data quality, integration, and
governance to enable effective marketing analytics and reporting.
6. Marketing Research Analyst: Conduct research studies, surveys, and market analysis to gather
data and insights on consumer preferences, market trends, and competitive landscapes. They
analyze and interpret research findings to inform marketing strategies and product development.
7. Marketing Strategy Consultant: Provide strategic guidance to businesses based on data analysis
and market insights. They help develop marketing strategies, optimize pricing and positioning,
and identify growth opportunities through data-driven decision making.

6. Data science excels in healthcare analytics by serving at-risk populations. By analyzing
comprehensive and diverse data sources, including patient demographics, medical histories,
genetic information, lifestyle factors, and social determinants of health, data scientists develop
sophisticated predictive models. Healthcare organizations then allocate their efforts and resources
more effectively, leading to more efficient healthcare delivery for at-risk populations.
Improving the quality of the patient experience is another area where data science makes a
significant impact. Through advanced analytics techniques, data scientists analyze patient feedback,
sentiment analysis, and other data sources to gain insights into patient preferences and satisfaction
levels. By addressing pain points, streamlining processes, and personalizing care experiences,
healthcare providers create a more patient-centric environment that fosters trust, engagement, and
better overall experiences for patients.
Data science empowers healthcare professionals in tailoring treatment plans using predictive
analytics. By mining large volumes of patient data, including medical records, lab results, imaging
data, and genetic information, data scientists identify patterns and correlations that aid in
predicting treatment responses and outcomes. By considering individual patient characteristics,
including genetic predispositions and previous treatment responses, healthcare professionals
optimize therapies, increase treatment efficacy, and reduce the risk of adverse events. This
approach can significantly improves patient outcomes, increases treatment success rates, and
enhances patient well-being.
With a degree in data science specializing in healthcare analytics, you can prepare for a number of
career paths, including:
Data Science Concentration: Healthcare Analytics
1. Healthcare Data Analyst: Responsible for collecting, cleaning, and analyzing healthcare data to
derive insights and support decision-making.
2. Healthcare Data Scientist: Develops predictive models, conducts risk assessments, and provides
insights for clinical decision support systems and population health management.
3. Healthcare Informatics Specialist: Collaborates with healthcare professionals to design and
implement data-driven solutions for improved patient care and operational efficiency.
4. Healthcare Business Intelligence Analyst: Analyzes healthcare data to identify key performance
indicators, develop dashboards, and generate reports.
5. Healthcare Analytics Manager: Oversees the analytics team and leads projects focused on
healthcare data analysis.
6. Healthcare Predictive Modeler: Develops predictive models and algorithms using healthcare
data to forecast disease prevalence, patient readmission rates, or treatment response.

7. Data Science Concentration: Cyber and
Information Security
Data science significantly impacts cyber and information security by enabling threat detection
and prevention, as well as enhancing security analytics and incident response. By analyzing large
datasets, data scientists develop algorithms and models to detect anomalies and identify potential
security breaches. They also leverage data mining and visualization techniques to uncover patterns
and trends, empowering security teams to prioritize alerts and respond effectively to incidents.
Through data-driven approaches, organizations proactively protect their systems and improve
their overall security posture. The combination of data science skills with expertise in cyber and
information security allows professionals to contribute to the development and implementation of
robust security measures, threat detection, incident response, and proactive security strategies in an
increasingly data-driven security landscape.
1. Security Data Scientist: Combines data science expertise with cybersecurity knowledge to
analyze security data, develop machine learning models for threat detection, and perform
data-driven security assessments.
2. Security Analyst: Utilizes data science techniques to analyze security logs, network traffic,
and system data to identify and investigate security incidents, assess vulnerabilities, and
propose countermeasures.
3. Threat Intelligence Analyst: Applies data science methods to gather and analyze threat
intelligence data from various sources, including dark web monitoring, to identify emerging
threats and provide actionable insights to enhance security strategies.
4. Security Operations Center (SOC) Analyst: Utilizes data science tools and techniques to monitor
and analyze security events, conduct anomaly detection, and generate alerts for potential security
breaches. They contribute to incident response and threat hunting efforts.
5. Malware Analyst: Utilizes data science techniques to analyze and reverse-engineer malicious
software, identify patterns, and develop algorithms to detect and mitigate the impact of malware.
6. Security Engineer: Leverages data science to design and implement security systems and
infrastructure, develop data-driven risk assessment models, and build predictive analytics
solutions for proactive threat mitigation.
7. Security Architect: Integrates data science principles into the design and architecture of secure
systems, ensuring the effective collection, analysis, and visualization of security-related data for
decision making and incident response.

8. “The Data Science program at St. John’s has been
instrumental in equipping me with robust skills in statistics,
data wrangling, and data visualizations. The program
inspired me to see the malleability of data, and how I can
shape it into meaningful insights. The educators at St.
John’s have fueled my curiosity and propelled a continual
journey of discovery in data science.”
PETER TADROUS‘20CCPS,‘21GCCPS
Pursuing an M.S. in Data Science: The
Value of Hands-On Learning
Pursuing a master’s degree in data science from St. John’s University provides substantial value
to your career pursuits and preparation—particularly in terms of the hands-on and in-person
learning opportunities it offers. Data science is an applied field, and the master’s program at St.
John’s provides you with invaluable experiences to engage in practical projects, collaborate with
faculty members, and develop proficiency in the tools and techniques essential for data analysis.
The hands-on nature of the program allows you to work with real-world datasets, tackle complex
problems, and gain practical skills that are crucial for success in your future data science career.
Additionally, the face-to-face interactions with faculty members and industry experts facilitate
mentorship, networking, and the opportunity to learn from professionals who have firsthand
experience in the field. This direct engagement not only deepens the understanding of complex
concepts, but also opens doors to internships, research collaborations, and future career prospects.
In 30 credit hours, you earn your master’s in data science with practical knowledge that will help
you be successful in a data science career.
Classes are offered in
the evening so you can
continue working while
you earn your degree.
Earn your degree in
as little as 18 months,
or at a pace that suits
your schedule.
Leverage hands-on
learning and employable
skills.
  

9. Pursuing an M.S. in Data Science:
Selecting a Program
“Transitioning from a medical background to the
field of data science was a significant turning point in
my career. The inclusive and diverse environment at
St. John’s University provided me with the opportunity to
pursue my passion for data science, despite coming from
a nontraditional background. Since completing the M.S.
program, I have witnessed significant growth in my career
as a data scientist.”
SHIQI CHEN‘18GCPS
When selecting a data science master’s program, several factors should be taken into consideration.
First and foremost, it is crucial to evaluate the curriculum and courses offered to ensure they align
with your career goals and interests within the field of data science. Look for programs that offer a
comprehensive and up-to-date curriculum, covering key areas such as statistical analysis, machine
learning, data mining, and programming. Additionally, consider the program’s faculty and their
expertise in the field, as knowledgeable and experienced professors greatly enhance your
learning experience. Furthermore, investigate the program’s resources and facilities, such as access
to state-of-the-art computing infrastructure and data analytics tools, as these play a significant role
in your ability to perform practical projects.
Modern Learning Opportunities
The Institute for Data Science at St. John's University serves as a hub for research activities, with an
emphasis on data mining and analytics. We explore opportunities for data science initiatives among
disciplines within The Lesley H. and William L. Collins College of Professional Studies and across
the University.
Flexible Schedule for Working Professionals
With classes offered in the evening, you are able to continue working while you earn your master’s
degree in Data Science at St. John’s at a pace that fits your schedule.
High Academic Standards
St. John’s University’s high academic standards address the future of the data science industry by
providing cutting-edge resources and learning opportunities. Our esteemed faculty bring their
hands-on knowledge into the classroom to prepare you for the real-world scenarios you will
encounter in the field.

10. St. John’s University Data Science
Program Faculty
Syed Ahmad Chan Bukhari, Ph.D.
Assistant Professor
Research interests: biomedical informatics, sociotechnical artificial intelligence
systems, and clinical decision support systems
Christoforos Christoforou, Ph.D.
Assistant Professor
Research interests: machine learning methods and their applications to
neurocinematics, brain-computer interfacing, and computational neuroscience
Giancarlo Crocetti, Ph.D.
Adjunct Professor
Research interests: natural language processing, biomedical informatics, big data
analytics, and distributed computing
Fazel Keshtkar, Ph.D.
Associate Professor
Research interests: artificial intelligence, natural language processing and generation,
computational linguistics, machine learning, and intelligent tutoring systems
Bonnie MacKellar, Ph.D.
Associate Professor
Research interests: semantic databases, linked open data, medical and health-care
informatics, and software engineering education
Christina Schweikert, Ph.D.
Associate Professor
Research interests: information fusion, data mining, biomedical and healthcare
informatics, and programming languages
Nikhil Yadav, Ph.D.
Associate Professor
Research interests: affective computing research, artificial intelligence, and
machine learning

11. St. John’s University Data Science Program
The 30-credit M.S. in Data Science prepares you for related careers that involve the application
of computational and statistical techniques that are becoming more vital to industry and research.
This is accomplished through coursework in topics such as database management systems, data
mining and machine learning algorithms, data visualization, statistics, text analytics, and big data.
Graduates of the Data Science program obtain a variety of skills required to analyze large datasets
and to develop modeling solutions to support decision making.
The M.S. program in Data Science requires 30 credits that include the following:

Big Data
Analytics

Cyber and
Information Security

Healthcare
Analytics

Marketing
Analytics
Core Courses (9 credits required)
• CUS 510 Database Management Systems
• CUS 610 Data Mining and Predictive Modeling I
• CUS 615 Data Mining and Predictive Modeling II
Data Analysis/Applied Statics Courses (6 credits chosen from the following)
• BUA 602 Business Analytics
• BUA 609 Advanced Managerial Statistics
• BUA 633 Applied Regression and Forecasting Models
Specialization (6 credits in one of the following areas)
• Big Data
0 CUS 680 Distributed Big Data Analytics I
0 CUS 681 Distributed Big Data Analytics II
Capstone Course
• CUS 690 Applied Analytics Project
Concentrations and Specializations
Program Requirements:

12. • Elective Courses (6 credits in one of the following areas)
0 CUS 620 Introduction to Programming for Analytics
0 CUS 625 Data Visualization Programming
0 CUS 635 Web Data Mining
0 CUS 640 Natural Language and Text Processing
0 CUS 675 Database Programming
0 CUS 680 Distributed Big Data Analytics I
0 CUS 681 Distributed Big Data Analytics II
For more information,
please contact
Office of Graduate Admission
718-990-1601
gradhelp@stjohns.edu
• Cyber and Information Security
0 CYB 611 Foundations in Cyber Security
0 CYB 615 Protection of Digital Infrastructure
0 CYB 621 Cybersecurity Laws, Regulations, and Best Practices
0 CYB 625 Principles of Secure Scripting and Cryptography
0 CYB 711 Intrusion Detection and Analysis
0 DFR 711 Cyber-Forensic and Malware Analysis
• Healthcare Analytics
0 HCI 520 Medical and Health Informatics
0 HCI 525 Applied Healthcare Analytics
• Marketing Analytics
0 MKT 600 Decisions in Marketing Management
0 MKT 611 Data-Driven Marketing