What are the core topics covered in the program?

The core topics in this program are: Data Preprocessing and Cleaning Machine Learning Algorithms Big Data Technologies Data Visualisation Predictive Analytics Statistical Modelling Time Series Forecasting Deep Learning Ethics in Data Science

Online MS Data Science Birchwood University |Apply Now

About Master of Science in Data Science from Birchwood University

The Master of Science in Data Science is a rigorous, 18-month program that equips students with both foundational knowledge and advanced skills critical for succeeding in today's rapidly evolving technological landscape. This degree is meticulously designed to cover a broad range of core analytical competencies and technical skills, addressing the diverse demands of industries increasingly reliant on data-driven decision-making.

The Master of Science in Data Science serves as a transformational educational experience for individuals looking to advance their careers in the data-driven world. By combining analytical competencies with technical skills and practical experiences, graduates are well-prepared to take on the challenges of the global industry. Whether aiming for roles in data analysis, machine learning engineering, or data strategy, the knowledge and expertise gained from this program will be instrumental in navigating the complexities of modern data management landscapes.

Why choose a Master of Science in Data Science?
The Master of Science in Data Science opens the door for your career in data-driven businesses. After graduation, you are typically responsible for all aspects of transforming data into value, from designing the technical infrastructure to building advanced machine and deep learning models, as well as improving data quality and evaluating model performance. You may also guide organisations in becoming predictive enterprises by identifying potential use cases, conducting initial project planning, and defining key measures and metrics that determine success.

18-Month Program Duration
100% Online Programs
24×7 Access to the Learning Platform
World-class Curriculum Developed by Industry Experts
The Cohort Experience for Strong Professional Networking
Hands-on Capstone Projects for Real-world Industry Application

Program Objectives

Apply the necessary skills to communicate effectively, thoughtfully, and compassionately within the global analytics field.
Apply, synthesize, analyze, and integrate the knowledge of Data Science, Python, Machine Learning, Artificial Intelligence to arrive at innovative solutions to organizational problems.
Demonstrate the skills to work in multicultural organizations within a globalized society.
Demonstrate the ability to develop, analyze and communicate empirical scholarly work.
Develop the competencies in Data Science.

Program Certificate

Click to Zoom

The actual certificate might slightly differ from the sample certificate

Same Master as On Campus: Receive the same world-class education and global recognition as on-campus doctorates without the need to relocate.

Globally Recognized US Degree: Birchwood University is licensed by the Florida Commission for Independent Education, Florida Department of Education.

Lifetime Alumni Status: Join an alumni network of professionals, many of whom hold key leadership roles across the globe.

MS in Data Science Program Curriculum

Gain insight into the Python Programming language with this introductory course. An essential programming language for data analysis, Python, Programming is a fundamental key to becoming a successful Data Science professional. In this course, you will learn how to write python code, learn about Python's data structures, and create your functions. After the completion of this course, you can represent yourself as an ideal candidate for python Developer.

In this course, students will learn how to manage the Data Effectively using My SQL Work Bench. Students will come to know how to Apply Certain Joins techniques, how to manipulate the data. Will be able comfortably design SQL queries to add data to the database, will be familiar with editing, deleting data from the database, and will be able to describe and develop Relational Algebra and Relational Calculus queries.

Gain insight into the R Programming language with this introductory course. An essential programming language for data analysis, R Programming is a fundamental key to becoming a successful Data Science professional. This course will teach you how to write R code, learn about R's data structures, and create your functions. After the completion of this course, you will be fully able to begin your first data analysis.

This course includes the necessary exploratory techniques for summarizing data. These techniques are typically implemented before formal modeling begins and can help in informing the development of numerous complex statistical models. Exploratory techniques are also essential for eliminating or sharpening potential hypotheses about the world that the data can address. In this course, we will study plotting systems and the basic principles of constructing data graphics. We will also cover some of the standard multivariate statistical techniques used to visualize high-dimensional data.

Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques, including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Machine Learning knowledge.

Implementing models such as support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering, and more in Flask, sending and receiving the requests from deployed machine learning models, building machine learning model APls, and deploy models into the cloud, design testable, version-controlled, and duplicate production code for model deployment.

The Artificial Intelligence course will expand your technical function and become an expert in Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Al concepts and techniques, including, Deep Learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms and prepare you for your role with advanced Al knowledge. Also, it will help in understanding Convolution Neural Networks Convolution, Pooling and Generative Networks Adversarial Networks, and these skills will enable candidates to seek their career in their desired companies.

Data Visualization using Tableau - Tableau Course will help you master the various aspects of the program and gain skills such as building visualization, organizing data, and designing dashboards. You will also learn concepts of statistics, mapping, and data connection. It is an essential asset to those wishing to succeed in Data Science. After learning the tableau tool, one must be able to display and analyze data. Also, it enables the users to create various reports and presentations about data.

Data Visualization using Power Bi - In this course, you will master the various aspects of the program and gain skills such as building visualization, organizing data, and designing dashboards utilizing data visualization using Power Bi. You will also learn concepts of mapping, and data connection; users will be able to provide clear and actionable insights in less than a minute. It also enables the candidate to import data from multiple sources. And learn how to transform the data. It is an essential asset to those wishing to succeed in Data Science.

The capstone project will allow you to implement the skills you learned throughout this program. Through dedicated mentoring sessions, you'll learn how to solve a real-world, industry-aligned Data Science problem, from data processing and model building to reporting your business results and insights. The project is the final step in the learning path and will enable you to showcase your expertise in Data Science to future employers.

Admission and Graduation Requirements

General Admission Requirements:

A copy of a valid government-issued photo identity card.
A copy of an updated resume.
Any document if not in English must be accompanied by a certified translated copy.

Additional Admission Requirements For Master of Science in Data Science:

Submit a 500-word essay (minimum) summarizing the applicant’s interest in the Master of Science in Data Science program outlining your professional aspirations.
Provide an official undergraduate degree transcript verifying the completion of a bachelor’s degree in computer science, engineering, mathematics, statistics, or a related field with a cumulative GPA of 2.5 or higher.
Provide two (2) professional recommendation letters attesting to your academic abilities and professional potential.
Personal Interviews will be conducted with the Director of Education for applicants with a GPA below 2.5.

Application For Admission:

All individuals interested in applying for admission to the university must complete an application and submit a non-refundable registration fee of $150.00 (payable by check, money order, or credit card). Checks and money orders should be made payable to Birchwood University.

Applicants must also provide all required application documents to be considered for admission. Once an admissions decision has been made, the candidate will receive an email with further instructions. Admissions agents will maintain regular contact with applicants to ensure all necessary documents are received by the admissions office.

Post Graduation Requirements:

To graduate from Birchwood University and to receive a degree, the students must:

Complete all credits as stated in the catalog.
Need to earn a minimum cumulative grade point average of 3.0.
Meet satisfactory academic progress.
Fulfill all financial obligations.

Admission Process Overview

Online Application & Assessment

Begin your journey with a simple online application followed by an initial assessment designed to evaluate your readiness for the program. This stage ensures that applicants meet the foundational criteria required for success in the Master of Science in Data Science.

Personal Interview & Document Review

Qualified applicants proceed to a personal interview, allowing the admissions team to assess motivation, clarity of goals, and academic potential. Following the interview, all submitted documents undergo a thorough verification process to confirm eligibility and completeness.

Final Committee Decision

After assessments, interviews, and document validation, the admissions committee reviews each candidate holistically. Successful applicants receive an official admission decision along with detailed instructions for enrollment and next steps.

FAQs

Program FAQs

Grades & Transcripts

Tuition, Fees & Scholarships

Staff & Faculty

Alumni

The Data Science Professional program is designed to equip students with advanced skills in data analysis, machine learning, big data technologies, and predictive analytics, preparing them for leadership roles in the growing field of data science.

The program is designed to be completed in approximately 6 months, with flexible learning options available for working professionals.

The core topics in this program are:

Data Preprocessing and Cleaning
Machine Learning Algorithms
Big Data Technologies
Data Visualisation
Predictive Analytics
Statistical Modelling
Time Series Forecasting
Deep Learning
Ethics in Data Science

The Data Science Professional Program is available online, offering flexibility to study at your own pace while balancing your professional and personal commitments.

Yes, students have access to experienced faculty, online tutorials, peer discussions, and dedicated academic advisors to guide them throughout the program.

Graduates of the program can pursue various roles, including Data Scientist, Machine Learning Engineer, Data Analyst, Business Intelligence Analyst, and more in industries like tech, finance, healthcare, and retail.

Original exam records are kept for the duration of your enrollment and for three years after you last attend; permanent transcripts are retained indefinitely.

After payment and document clearance, official transcripts and diplomas can be issued with a three-business-day processing time.

Birchwood University follows a letter-grade system aligned with standard U.S. higher-education grading. Each letter grade corresponds to a percentage range and a Quality Point Value (QPV) used to calculate GPA.

Letter Grade	Quality Point Value (QPV)	Percentage Range
A+	4.00	95–100%
A	3.75	90–94%
B+	3.50	85–89%
B	3.00	80–84%
C+	2.50	75–79%
C	2.00	70–74%
D+	1.50	65–69%
D	1.00	60–64%
F	0.00	Below 60%

Students may choose a tuition payment plan at no interest; cancellation within three business days allows a full refund (minus registration fee), and withdrawal during the first drop/add week allows full tuition refund except the registration fee.

Yes, the university offers scholarships and payment assistance; students on payment plans must make their first installment before the term begins.

Faculty are more than instructors — they are mentors, researchers, industry experts and advisors that guide students through practical, industry-driven learning.

Staff and faculty organise hackathons, seminars, industry visits, and advisory roles to help students connect theory with real-world challenges.

The university actively engages alumni through professional events, mentoring programs and continuous collaboration to maintain a vibrant global alumni community.

MS in Data Science by Birchwood

Accreditations

The Data Science Landscape

About Master of Science in Data Science from Birchwood University

Master of Science in Data Science Course - Key Highlights

Program Objectives

Program Certificate

Who Can Apply for the Master of Science in Data Science

MS in Data Science Program Curriculum

MDS500 | Python Programming | 4 Credit Hours

MDS510 | Data Base Management System | 4 Credit Hours

MDS520 | R Programming | 4 Credit Hours

MDS530 | Exploratory Data Analysis | 4 Credit Hours

MDS540 | Machine Learning | 4 Credit Hours

MDS550 | Machine Learning Model Deployment | 4 Credit Hours

MDS560 | Artificial Intelligence | 4 Credit Hours

MDS570 | Data Visualization Using Tableau/Power BI | 4 Credit Hours

MDS600 | Capstone Project | 4 Credit Hours

Admission and Graduation Requirements