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The Master of Science in Data Science program prepares you for a career in data science by teaching you how to apply statistical methods in solving real-world problems. Core coursework includes data modeling; data management; and mining of continuous, categorical, and multivariable data. Advanced specializations focus on artificial intelligence and optimization, business analytics, database analytics, or health analytics. The program culminates in a three-month capstone where real data from sponsoring organizations (or, alternatively, publicly available data) will be used in a team project to demonstrate your mastery in data acquisition, cleaning, analysis, modeling, visualization, and reporting.
The Database Analytics specialization prepares professionals for developing, implementing, and maintaining the tools needed to make efficient and effective use of big data. Instruction and coursework will focus on databases, data marts, data warehouses, machine learning, and analytic programming for applications in AI and optimization.
For the Master of Science in Data Science degree with a specialization in Database Analytics, you must complete seven foundation courses, five specialization courses, and three capstone courses. Completion of all foundation and specialization courses is required prior to starting the capstone course sequence.
Foundation Course Listings
An introduction to statistical modeling and data analysis. This course uses R programming to explore data variation, model data, and evaluate models. You’ll also learn to analyze and evaluate different types of regression models and error analysis methods.
In this course, you’ll learn to apply data analytics to facilitate modern knowledge discovery techniques. Coursework will focus on different forms of data, gap analysis, model building, and interpretation as foundations for analytical study.
This course applies the data management process to analytics. You’ll explore and learn the processes of acquiring and auditing data, assembling data into a modeling sample, performing basic data integrity checks, cleansing data, feature engineering, and data visualization.
An examination of data mining methods and predictive modeling. Through a variety of case studies and practical industry applications, you’ll explore design objectives, data selection and preparation, classification and decision tree methods, and predictive modeling.
In this course, you’ll apply methods for analyzing continuous data for knowledge discovery. Analytic continuous data concepts and methods are developed with practical skills in exploratory data analysis. Areas of focus will include descriptive statistics, goodness-of-fit tests, correlation measures, single and multiple linear regression, and analysis of variance and covariance. Coursework will use case studies and real world data to leverage statistical assessment and interpretation.
This course explores and applies methods for analyzing categorical data for knowledge discovery. Analytic categorical data analysis concepts and methods are developed with practical skills in exploratory data analysis. Areas of focus include descriptive statistics of discrete data, contingency tables, and methods of generalized linear models. Instruction will use case studies and real world data to leverage statistical assessment and interpretation.
An examination of advanced applications of data analytics for knowledge discovery. This course explores several advanced techniques in data analytics, including methods for longitudinal data, factor and principal components analysis, multivariate logistic regression, and multivariate analysis of variance (ANOVA). Coursework will use case studies and real world data to leverage statistical assessment and interpretation.
An examination of database design and implementation for analytical applications in big data. Coursework will focus on requirements collection, conceptual and logical database design, normalization, an introduction to SQL (Structured Query Language), and designing a data mart.
In this course, you’ll learn to design and develop a data warehouse application for big data. Topics of focus include: user requirement collection, dimensional modeling, ETL (extract, transform, and load) procedures, information access and delivery, and optimization and maintenance of a data warehouse.
An in-depth exploration of data manipulation with SQL (Structured Query Language). This course examines views, triggers, sequences, reporting, sub-queries, query optimization, and the use of SQL for data warehouse manipulation.
This advanced data mining course focuses on various machine learning and artificial intelligence techniques. Topics of study include classification rules, association rules, instance-based learning, semi-supervised learning, and multi-instance learning.
An introduction to and investigation of advanced topics in AI (artificial intelligence) and optimization in various state-of-the-art applications.
The first of three capstone courses, this class comprises the first stage of your master’s thesis project. Through your research of analytic project design, problem framing, team-building, collaboration, and technical presentation, you’ll propose a data science project to advisors and stakeholders. Your team’s submission should include strategic and technical aspects of data acquisition, data cleaning, and analytic methodology.
This course is a continuation of your master’s-level research in analytic project implementation, technical writing, and project presentation. Your team’s data science project will include strategic and technical aspects of data acquisition, data cleaning, and analytic methodology for presentation to your project advisors and stakeholders.
In this course, your team will complete and present your master’s-level data science project. The finished project will include strategic and technical aspects of data analysis and visualization, and will be presented in a written thesis to your project advisors and stakeholders.
Students earning the Master of Science in Data Science with a Database Analytics specialization will learn to:
- Design data marts
- Analyze complex database queries for real-world analytical applications
- Design medium-to-large data warehouses
- Evaluate machine-learning methods and strategies for advanced data mining
- Integrate components of data science to produce knowledge-based solutions for real-world challenges using public and private data sources
- Evaluate data management methods and technologies to improve integrated data use
- Construct data files using statistical and data programming to solve practical problems in data analytics
- Design and implement an analytic strategy for a potential issue relevant to the community and stakeholders
- Develop team skills to research, develop, and evaluate analytic solutions to improve organizational performance
Successful completion and attainment of National University degrees do not lead to automatic or immediate licensure, employment, or certification in any state/country. The University cannot guarantee that any professional organization or business will accept a graduate’s application to sit for any certification, licensure, or related exam for the purpose of professional certification.
Program availability varies by state. Many disciplines, professions, and jobs require disclosure of an individual’s criminal history, and a variety of states require background checks to apply to, or be eligible for, certain certificates, registrations, and licenses. Existence of a criminal history may also subject an individual to denial of an initial application for a certificate, registration, or license and/or result in the revocation or suspension of an existing certificate, registration, or license. Requirements can vary by state, occupation, and/or licensing authority.
NU graduates will be subject to additional requirements on a program, certification/licensure, employment, and state-by-state basis that can include one or more of the following items: internships, practicum experience, additional coursework, exams, tests, drug testing, earning an additional degree, and/or other training/education requirements.
All prospective students are advised to review employment, certification, and/or licensure requirements in their state, and to contact the certification/licensing body of the state and/or country where they intend to obtain certification/licensure to verify that these courses/programs qualify in that state/country, prior to enrolling. Prospective students are also advised to regularly review the state’s/country’s policies and procedures relating to certification/licensure, as those policies are subject to change.
National University degrees do not guarantee employment or salary of any kind. Prospective students are strongly encouraged to review desired job positions to review degrees, education, and/or training required to apply for desired positions. Prospective students should monitor these positions as requirements, salary, and other relevant factors can change over time.
It depends on the job; some working data scientists have a bachelor's or graduated from a data science bootcamp. According to a 2022 Burtch Works study open_in_new, over 90% of data scientists they surveyed hold a graduate degree. Learn about 23 schools with master's in data science programs.Is a Masters in data science analytics worth it? ›
While earning a master's degree in data science comes with certain costs—in terms of both tuition and time—it can be a worthwhile investment when you're interested in furthering your abilities to work with and parse data.Which is better for Masters data science or Data Analytics? ›
Data analytics is a better career choice for people who want to start their career in analytics. Data science is a better career choice for those who want to create advanced machine learning models and algorithms.Is a Masters in data science and Data Analytics the same? ›
To sum them up in a few words, Data Science explores and tests new methods to use and interpret data, while Data Analytics focuses on analysing datasets and finding insights and solutions to problems.Is Masters in data science tough? ›
Data science is a difficult field. There are many reasons for this, but the most important one is that it requires a broad set of skills and knowledge. The core elements of data science are math, statistics, and computer science. The math side includes linear algebra, probability theory, and statistics theory.What percentage of data scientists have a masters degree? ›
|Data Scientist Degree||Percentages|
Yes, yes, it would be hard. If you've had no training in statistics, or even college math, and no experience with databases or programming, you should take some of those classes and see how it goes before considering a career in data science. Reason 3: The job won't pay as much as you're expecting.Can a masters in data science get you a job? ›
Jobs that data science master's land
Graduates from Syracuse's program take on a variety of roles: data analysts, data scientists, machine learning engineers, data engineers, quality specialists, data visualization designers, business intelligence analysts, and data mining analysts.
An MBA in data science helps students to convert data into key business insights and incorporate in-demand tools and technologies, preparing them for the modern business landscape. On the other hand, M.Sc in data science will equip the learners with tools and techniques, making them part of the current industry trends.Which pays more data science or data analytics? ›
As per Glassdoor, the average salary of a data analyst in India is 6 Lac rupees per annum. In India, the average salary of a Data Scientist is 9 Lac rupees per annum.
Do Data Analysts Code? Some Data Analysts do have to code as part of their day-to-day work, but coding skills are not typically required for jobs in data analysis.Which pays more big data or data science? ›
A senior business data analyst can expect to earn on average $85,000 and an entry-level business data analyst can earn around $55,000. Data science vs. data analytics salary: The salary of both data science and data analytics professionals is almost the same, with small variation in the entry-level trends.Which is easier data science or data analytics? ›
Data analytics is a field best for beginners working with data, but if you already have some professional experience in the world of data science, it's certainly worth exploring that opportunity.What degree is best for data analytics? ›
For example, a bachelor's degree in computer science, statistics, or information systems can give you the foundational technical skills you need as a data analyst. As data collection, management and analysis becomes more complex and technology advances, many employers are in search of candidates with master's degrees.Should I get a PhD or Masters in data science? ›
A 2021 Burtch Works study found that 48 percent of data science professionals hold a PhD, a five percent increase from 2020. However, a PhD in data science is not necessary to succeed. Many professionals in this field hold a master's degree and earn competitive salaries.What is the hardest masters degree in science? ›
- Biomedical Science.
Although data pre-processing is often considered the worst part of a data scientist's job, it is crucial that models are built on clean, high-quality data. Otherwise, machine learning models learn the wrong patterns, ultimately leading to wrong predictions.Do data scientists code a lot? ›
Traditionally, data science roles do require coding skills, and most experienced data scientists working today still code. However, the data science landscape continues to change, and technologies now exist that allow people to complete entire data projects without typing code.What is the minimum GPA for data science Masters? ›
Applicants are required to have a GPA of at least 3.0 for the last 60 graded semester credits or the last 90 graded quarter credits of their schooling (U.S.), or roughly the equivalent of the last two years of graded study (undergraduate and/or graduate).What is the average salary for Masters in data science from us? ›
With a Bachelor's degree, the average salary of a Data Scientist in the USA can be 104,000 USD per year. A master's degree in data science can significantly boost career professionals' salaries by about 59%, i.e., 165,000 USD per year.
Indeed, domain expertise is one of the most sought-after skills for data analysts. So despite industry ageism, a recent study by Zippia showed that the average age of data analysts in the U.S. is 43 years old.Does Masters in Data Analytics require coding? ›
Coding is required. For working professionals who code: Coding is required in Data Science, and you can pick it up. There is a learning curve in Data Science because, along with code, you will also need to unlearn and relearn mathematics and business. The data science bootcamp can help here.Is Data Analytics a hard degree? ›
Because the skills needed to perform Data Analyst jobs can be highly technically demanding, data analysis can sometimes be more challenging to learn than other fields in technology.How long does a Masters in Data Analytics take? ›
It takes 1.5-2 years, on average, to earn a master's degree. However, depending on the program and whether you attend school full time or part time, it could take anywhere from seven months to seven years. Attending school part time can give you the option of continuing to work while you earn your master's degree.Can I make six figures as a data analyst? ›
A low six-figure salary is really the limit of most data analytics roles, though. To earn more, you'll need to transition into a more senior position, such as a data scientist or finance manager.What can I expect from a masters in data science? ›
Data science coursework
At the master's level, you'll likely be expected to complete core classes in machine learning, modeling, statistics, and databases, as well as elective courses related to data science. You may also take courses in programming languages, such as Python, R, or SQL.
The simple answer is no, you don't need formal education in this field to start your career. However, you'd still have to have completed a level of higher education in order to successfully land a data science position.What pays more data science or software engineering? ›
The average yearly salary for data scientists is $120,103 . The average yearly salary for software engineers is $102,234 . Software engineers also receive an average of $4,000 in bonuses each year. Your salary may vary depending on your experience, skills, training, certifications and your employer.Is data scientist job stressful? ›
Data Science can be a stressful job because it has its challenges. But whether it is truly a stressful job or not is pretty subjective, depending on the circumstances, working environment, and the project. People with a passion for the job enjoy it while others may experience undeniable stress.Should I get an MS in data science or computer science? ›
Part of the reason for the confusion is that both of these streams have some similarities between them. But, Computer Science is a more comprehensive degree, it can be applied towards many software-related fields and other computer-related areas. Whereas Data Science will mainly stick to the analytics sector.
Most data scientists have a master's degree or a Ph. D. in computer science, mathematics, statistics, information science, or other relevant areas like bioinformatics (depending on the industry requirements for a specialized skill set).What masters degree should I get if I want to be a data scientist? ›
Top Master's Degrees for Data Science
in Data Analytics is the best fit for professionals who want to connect data with how it can lead to creating a competitive business advantage.
Becoming a data scientist generally requires a very strong background in mathematics and computer science, as well as experience working with large amounts of data. In addition, it is often helpful to have experience with machine learning and statistical modeling.Does having a masters in data science help? ›
Pursuing a master's degree in data science can lead to attractive career opportunities and provide important—and meaningful—real-world experiences through research projects and capstone courses.Does Masters in data science require coding? ›
Coding is required. For working professionals who code: Coding is required in Data Science, and you can pick it up. There is a learning curve in Data Science because, along with code, you will also need to unlearn and relearn mathematics and business. The data science bootcamp can help here.What GPA do you need for data science Masters? ›
✔ 3.0 Minimum GPA from last 2 years of graded full-time study. Applicants are required to have a GPA of at least 3.0 for the last 60 graded semester credits or the last 90 graded quarter credits of their schooling (U.S.), or roughly the equivalent of the last two years of graded study (undergraduate and/or graduate).How long does it take to get a masters in Data Analytics? ›
Answer: It can take anywhere from 12 to 36 months or more to complete an online master's in data analytics or data science program, depending on the structure of the program, the number of courses taken per term or semester, and whether or not the program offers courses year round.Why get a master's in data analytics? ›
A master's degree in data analytics can help you make connections between the work you've done, and the work you want to do, while teaching important interpersonal skills such as presentation, communication and leadership. Opening the door to new employment opportunities.Do I need a Masters or PhD to become a data scientist? ›
Since a PhD asserts the highest level of training and education, those who hold them are in the very highest demand for top data science jobs. However, you can certainly become a data scientist without a PhD, and a master's degree should also take you quite far, asserting a very high level of competence and ability.Is Masters in data analytics easy? ›
Yes, yes, it would be hard. If you've had no training in statistics, or even college math, and no experience with databases or programming, you should take some of those classes and see how it goes before considering a career in data science. Reason 3: The job won't pay as much as you're expecting.
Data scientists use algorithms and machine learning to improve the ways that data supports business goals. Data analysts collect, store, and maintain data and analyze results.What is the difference between data science and data analyst? ›
A data scientist may design the way data is stored, manipulated and analyzed. Simply put, a data analyst makes sense out of existing data, whereas a data scientist works on new ways of capturing and analyzing data to be used by the analysts.