MS in Data Science Programme
- A world-class master's degree in data science from
Northwestern University School of Professional Studies
Enquire
+1 847 416 2122
Awards & recognitions
Northwestern Master's in Data Science Programme is awarded the
2023 UPCEA International Program of Excellence Award
Why Choose this Programme?
World Class Education
- Northwestern was ranked in top 6 U.S. universities (U.S. News & World Report 2025)
- Designed and delivered by experienced faculty with industry experience
- Physical residencies in Chennai and Gurgaon GLIM campus
Comprehensive Learning
- Learn popular programming languages, frameworks and libraries in a hands on environment
- 100% Live classes
- AI as a specialisation
- Capstone project to showcase acquired skills
- 32 credits earned in 18 months
Career Assistance
- Dedicated career assistance for enrolled students
- 1:1 Career mentorship, access to job boards and more
Benefits of the Immersion Sessions During the Programme
Physical Residency
Two-day in-person residencies, twice in the programme held either in Gurugram, Bengaluru, or Chennai.
Networking
An opportunity to build connections and interact with fellow students and professors in an environment reminiscent of a traditional campus.
Academics
Collaborate with your team members to present your projects in real time in front of your peers and the professors.
Career Support
Highly interactive career mentoring sessions and fireside chats with industry speakers during the event.
MS in Data Science Programme from Northwestern University
Careers Empowered
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Akash Gupta
Associate Architect
Advisory AI Engineer
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Heidi Song
Data Engineer II
Data Engineer
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Vardaan Agarwal
AI Developer
AI Developer
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Rakib Ahsan
Lead Software Engineer
Senior Software Engineer
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Chaitanya Murali
Principal Analytics Specialist
Manager, Data Science - Analytics
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Sivakumar Saravanasubramanian
Program Lead and Solution Architect
Lead Data Scientist and AWS Data Solutions Architect
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Akshay Chaudhari
CFD Manager
Project Manager
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Srihari Narasimhachar
Solutions Architect
CEO and Data engineering & ML Architect.
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Shalesh Nath Sharma
Sr. Data Analyst
Analyst II in IT Sector
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Nadiya Noorudeen
BI Analyst
Lead Operational Performance Analyst
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Ajinkya Lakhepatil
MS in Data Science
Ph.D. in aerospace
Get the most elite Master’s degree from Northwestern University
Among US universities in 2025
*U.S. News & World Report 2025 Best College Rankings
World University Rankings 2025
*Times Higher Education (THE) World University Rankings 2025
Curriculum
225+ hrs
Learning content
10+
Languages & Tools
- Apply linear programming methods to real-world models
- Analyze and interpret mathematical models
- Calculate and analyze derivatives and integrals of real-world models
- Evaluate and interpret probabilistic models
- Solve applications involving multivariate calculus
- Optimize outcomes modelled by graphs and trees
- Perform statistical analysis
- Interpret and evaluate statistical information
- Prepare technical reports
- Use the R programming language for data analysis
- Define key terms, concepts, and issues in data preparation and database management systems
- Discuss the nature of data: structured data vs. unstructured data, small data vs. big data, clean data vs. messy data
- Review and interpret Entity Relationship Diagrams (ERDs)
- Discuss the architectures and technologies for relational database systems, data warehousing, information retrieval, and search engines
- Use a high-level programming language for data extraction, preparation, and exploration
- Compare and contrast SQL and NoSQL databases, and demonstrate the ability to interact with both types
- Construct indexes for information retrieval
- Store and retrieve geospatial data and plot data on interactive maps
- Discuss standards for data mining and Business Process Modeling and Notation (BPMN).
- Explain concepts related to data mining, machine learning, and process mining.
- Use no-code/low-code platforms for predictive analytics and machine learning.
- Apply analytical and machine learning techniques for decision support.
- Identify data sources and requirements for business process analytics.
- Leverage no-code/low-code platforms for business process analytics.
- Implement business process analytics to address real-world business challenges.
- Analyze requirement specification documents for proposed projects or applications.
- Evaluate data sources and determine appropriate data exchange formats for specific projects or applications.
- Create comprehensive design documents based on analysis findings.
- Implement application designs following detailed design documentation.
- Deploy and test applications according to their implementation plans.
- Build and execute various programs and experiments using the Go programming language in a personal development environment.
- Conduct comparative analyses of results from programs and experiments developed in the Go language.
- Demonstrate skills and techniques in optimization, simulation, and decision analysis
- Select and recommend appropriate modeling techniques based on a business problem
- Formulate and develop decision-analytic solutions to a given business problem using software
- Present the results of decision-analytic solutions in both oral and written forms
- Demonstrate a comprehensive understanding of data management concepts, including data quality, integrity, usability, consistency, availability, and security.
- Propose effective methods for managing enterprise data provenance.
- Identify and address ethical, legal, and technical challenges in data management.
- Assess cybersecurity and network security risks and propose appropriate mitigation strategies.
- Compare and contrast data privacy and cybersecurity policies in the United States and the European Union.
- Design a data governance framework that incorporates best practices in data management.
- Apply an ML framework for model building
- Build and interpret regression models
- Build and interpret classification models
- Apply and interpret regularization and data reduction methods
- Build and interpret tree-based models for regression and classification
- Build and interpret unsupervised/semi-supervised learning models
- Build and interpret different types of neural network models
- Identify the role of natural language processing (NLP) and text analytics within the data sciences.
- Extract entities and concepts, and identify, characterize, and apply methods for entity and concept co-resolution.
- Select and apply clustering and classification algorithms, as well as other machine learning techniques, including supervised, unsupervised, and generative methods.
- Apply and evaluate methods for sentiment analysis and link analysis.
- Develop basic ontological schemas for interpretation during text analytics and use them to support context-dependent NLP.
- Use deep learning-based language models to perform common NLP tasks and improve performance.
- Identify key phases in the evolution of artificial intelligence (AI), including the emergence of deep learning.
- Distinguish between supervised, unsupervised, and reinforcement learning methodologies.
- Describe the structure and functionality of neural networks, including deep learning architectures.
- Apply neural networks and deep learning techniques to solve classification and regression problems, including supervised learning with backpropagation.
- Utilize probability-rule-based generative models in deep learning.
- Explain the importance of AI and deep learning techniques in real-world applications, such as computer vision and natural language processing.
- Determine the appropriate level of abstraction for applying computer vision to business problems.
- Build computer vision models from scratch using TensorFlow 2.0.
- Apply computer vision models to edge-based machine learning hardware, including Intel Movidius.
- Implement computer vision models on edge-based machine learning hardware integrated with managed ML systems using AWS DeepLens and AWS SageMaker.
- Utilize cloud-native computer vision APIs.
Term 1
MATH FOR DATA SCIENTISTS
Students will learn techniques for building and interpreting mathematical models of real-world phenomena across multiple disciplines, including linear algebra, discrete mathematics, probability, and calculus, emphasizing applications in Data Science and data engineering. This course provides students with a solid understanding or review of these fields of mathematics before enrolling in courses that assume knowledge of mathematical concepts.
LEARNING OUTCOMES
APPLIED STATISTICS WITH R
This course covers the fundamentals of statistical analysis, which form the foundation for all subsequent courses. Students will learn to evaluate statistical information, perform data analysis, and interpret and communicate analytical results. The course also teaches the use of the R programming language for statistical analysis, data visualization, and report generation. Topics include descriptive statistics, measures of central tendency, exploratory data analysis, probability theory, discrete and continuous distributions, statistical inference, correlation, multiple linear regression, contingency tables, and chi-square tests.
LEARNING OUTCOMES
Term 2
DATABASE SYSTEMS
In this course, students will explore the fundamental concepts of database management and data preparation. Three database models will be discussed: relational databases (SQL), document-oriented databases (NoSQL), and graph databases (Cypher). With a focus on applications in large-scale data analytics projects, the course introduces relational database systems, the relational model, the normalization process, and structured query language (SQL). The course covers topics related to data integration and cleaning, as well as database programming for extract, transform, and load (ETL) operations. Students will learn NoSQL technologies for working with unstructured data and document-oriented information retrieval systems. They will learn how to index and score documents for effective and relevant responses to user queries. Students will also learn how to construct and query graph database applications using the graph query language. The course provides hands-on programming experience for data preparation and data extraction using various data sources and file formats.
LEARNING OUTCOMES
LEARNING OUTCOMES
BUSINESS PROCESS ANALYTICS
This course introduces data-driven management methods, including business process workflows, mining, modeling, and simulation; activity-based costing; constrained optimization; and predictive analytics. It emphasizes the importance of data from business operations, captured in time-stamped activity logs with associated costs, as critical information for effective business management. By analyzing business activities, students gain insights into business intelligence and process improvements, including robotic process automation and digital transformation. Through detailed case studies and hands-on experience with commercial and open-source analytics platforms, students learn how to leverage data and models to guide management decisions.
LEARNING OUTCOMES
LEARNING OUTCOMES
Term 3
FOUNDATIONS OF DATA ENGINEERING
This course introduces key concepts and technologies in data engineering. It covers design principles, development processes, and the management of information systems, emphasizing containerized microservices and cloud-native applications. Topics include data exchange formats, concurrency control for interacting processes, data communication protocols, standards for designing application programming interfaces (APIs), distributed processing, and information systems architecture. Students gain hands-on experience with the automated deployment and scaling of batch, interactive, and streaming data pipelines. They learn to design, implement, and maintain data-intensive applications in both cloud and on-premises environments. This programming-intensive course culminates in a full-stack development project.
LEARNING OUTCOMES
LEARNING OUTCOMES
DECISION ANALYTICS
This course covers fundamental concepts, solution techniques, modeling approaches, and applications of decision analytics. It introduces commonly used methods of optimization, simulation, and decision analysis techniques for prescriptive analytics in business. Students will explore linear programming, network optimization, integer linear programming, goal programming, multi-objective optimization, non-linear programming, metaheuristic algorithms, stochastic simulation, queuing models, decision analysis, and Markov decision processes. Students will develop a contextual understanding of techniques useful for managerial decision support and will implement decision-analytic techniques using a state-of-the-art analytical modeling platform. This is a problem- and project-based course.
LEARNING OUTCOMES
Term 4
DATA GOVERNANCE, ETHICS, AND LAW
This course introduces key concepts in data management, including data quality, integrity, usability, consistency, availability, and security. It explores the lineage of data, also known as data provenance, and examines ethical, legal, and technical considerations related to data acquisition, dissemination, and privacy protection. The course also provides an overview of cybersecurity management, covering topics such as network, system, and database security, along with encryption and blockchain technologies. Additionally, it addresses laws protecting intellectual property, including discussions on copyrights, patents, and contracts.
LEARNING OUTCOMES
PRACTICAL MACHINE LEARNING
LEARNING OUTCOMES
Term 5
NATURAL LANGUAGE PROCESSING
This course provides a comprehensive review of text analytics and natural language processing (NLP), with a focus on recent developments in computational linguistics and machine learning. Students will work with unstructured and semi-structured text from online sources, document collections, and databases. Using methods from artificial intelligence and machine learning, they will learn how to parse text into numeric vectors and transform higher-dimensional vectors into lower-dimensional representations for analysis and modelling. Applications include speech recognition, semantic processing, text classification, relevant search, recommendation systems, sentiment analysis, and topic modelling. This is a project-based course with extensive programming assignments.
LEARNING OUTCOMES
ARTIFICIAL INTELLIGENCE AND DEEP LEARNING
This course offers an introduction to the field of artificial intelligence (AI), exploring probability-rule-based generative models and discriminative models that learn from training data. It examines the applications of AI and deep learning in fields such as computer vision and natural language processing. Students will learn best practices for building supervised learning models, including deep neural networks for classification and regression tasks. The course also covers feature engineering, autoencoders, unsupervised and semi-supervised learning strategies, and reinforcement learning. This is a project-based course with extensive programming assignments.
LEARNING OUTCOMES
Term 6
COMPUTER VISION
This course provides a comprehensive review of specialized deep learning methods for computer vision, including convolutional and recurrent neural networks. Students work with raw image data, such as digital photographs, handwritten documents, X-rays, and sensor images. The course emphasizes processing image data by converting pixels into numeric tensors for analysis and modelling. Real-world applications explored include visual exploration, image classification, facial recognition, remote sensing, and medical diagnostics. This is a project-based course with extensive programming assignments.
LEARNING OUTCOMES
CAPSTONE PROJECT
A comprehensive three-month project where students apply the Data Science and Artificial Intelligence skills they have gained to address a real-world problem as part of a team.
MS Degree from Northwestern University
Languages and Tools covered
Learner Testimonials
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What I like about the course is its goal of explaining the fundamentals of data as science.Live online classes conducted by notable Northwestern University professors and excellent learning references, materials, and teaching assistants helps us, students, understand the syllabus.
Read more
Guruparan Muruguvannan
Senior Manager - Engineering, VMWare
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The structure seems completely thought through with great attention to detail. Faculty support during the live sessions focusing on concepts, practical implications, and the much needed hands-on experience are a few things that impressed me so far.
Read more
Pallav Kumar
Senior Manager, Sigmoid Analytics
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I like the way the course is running with concepts covered in depth and practical examples. I love all professors for their knowledge, crystal clear vision and apt points of clarification.
Read more
Sandeep Betanapalli
Linux Software Engineer at Doodle Labs
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The assignments are well formulated and help us to put to test, our understanding of the topic that we study. Discussion boards force us to think out of the box and give our opinions on various interesting articles. Overall, my experience has been excellent, and I’m looking forward to the rest of this program!
Read more
Prithvi Harish
Enrolled learner
Program Fees
Program Fees:
13,000 USD
Benefits of learning with us
- MS degree from Northwestern University
- Online, 100% Live sessions
- 18 months
- Hands-on learning with assignment and project work. Emphasis on learning by doing
- Fee payable in six quarterly instalments of $2,167 each
Frequently Asked Questions
Program Details
Why choose this Master's in Data Science programme?
The Master's in Data Science programme from Northwestern University stands out with its remarkable benefits. The exclusive Northwestern advantage, clubbed with the amazing benefits this course has to offer, makes this the right choice for professionals.
Here are a few of the benefits of the programme:
World Class Education:
- Northwestern was ranked in the top 6 U.S. universities (U.S. News & World Report 2025)
- Designed and delivered by experienced faculty with industry experience
- Physical residencies in Chennai and Gurgaon GLIM campus
Comprehensive Learning:
- Learn popular programming languages, frameworks, and libraries in a hands-on environment
- 100% Live classes
- AI as a specialisation Capstone project to showcase acquired skills
- 32 credits earned in 18 months
Career Assistance:
- Dedicated career assistance for enrolled students
- 1:1 Career mentorship, access to job boards, and more
Besides those mentioned above, there are a lot more benefits that this course offers. Do check out the fee, syllabus, and more.
What are the learning outcomes and goals of this programme?
The learning outcomes and goals of this master's in data science programme are:
The integration of data science and business strategy has created a demand for professionals who can make data-driven decisions that propel their organizations forward. You can build the essential analysis and leadership skills needed for careers in today's data-driven world in Northwestern SPS’s online Master of Science in Data Science Programme.
Programme Goals
- Articulate analytics as a core strategy of data science
- Transform data into actionable insights
- Develop statistically sound and robust analytic solutions
- Demonstrate leadership
- Formulate and manage plans to address business issues
- Evaluate constraints on the use of data
- Assess data structure and data lifecycle
Can I take this Master's in data science degree course from anywhere in the world?
Yes, candidates from any part of the world can take up the MS in Data Science programme. As this course is offered completely online, you can enroll and learn Data Science from the highly reputed faculty of Northwestern University.
How much time will I need to dedicate to the degree every week?
This is a master’s level degree and will be rigorous in nature. While the time needed will vary depending on prior knowledge, students should plan to spend around 15 - 20 hours every week.
Will every subject have a final examination?
All faculty will decide on the grading mechanism of their respective subjects. The evaluation criteria will be shared by them at the start of every course.
Which topics are going to be covered as part of the Master's in Data Science programme?
The curriculum of this programme encompasses all the fundamentals of Data Science. Throughout this course, you will learn about several concepts like Applied Statistics, Mathematics, Database Systems, Machine Learning, Data Governance, Python, and more.
You will also complete coursework in Artificial Intelligence, comprising Natural Language Processing, Artificial Intelligence, and Deep Learning.
What is the duration of a Data Science Master’s Degree from Northwestern University?
The duration of the MS data science online programme is 18 months, 6 terms of 3 months each.
Do I have to know programming to enroll in a master's degree from Northwestern University?
While knowledge of a programming language is not a prerequisite, the course will extensively use Python and R programming languages to demonstrate concepts and for project work. If applicants have no background in programming, then it would be helpful if they started learning Python and R prior to the start of the course.
What are the highlights of the master's degree in data science?
- Flexibility: This programme encompasses online learning with live sessions. The live sessions of this programme are organised to cause minimal disruptions to your personal and professional lives.
- Committed Student Support: Learners are empowered with constant support from several industry experts who help them in every step of their learning journey.
- Career Assistance: Great Learning provides outstanding placement opportunities to its learners upon the successful completion of this course. Each learner is provided with comprehensive career services that assist them in landing their desired job roles.
- Learn the most in-demand tools: Upon taking up this programme, learners gain an understanding of the most in-demand tools such as R, Python, TensorFlow, and more.
How is an online MS degree different from classroom-based learning?
While in terms of curriculum design and academic & career outcomes, this online degree is the same as a classroom programme, the key difference comes in how the curriculum is delivered. Classroom-based learning relies on more in-person interactions where students attend lectures in a classroom, interact with faculty over office hours, have study sessions with TAs, and other academic activities. In an online programme, all this interaction shifts to happen over the internet.
This shift allows for greater convenience, flexibility, and adherence to social distancing measures, making online programs an attractive option. Thus, an online programme offers more convenience, flexibility, and safety (by providing social distancing) compared to a classroom programme.
What tools and languages will I learn in this online data science master's programme?
The tools and languages that you will learn in this programme are:
- Python
- R Programming
- SQL
- Machine Learning
- Pandas
- Predictive Learning, and more.
Who is the faculty?
The MS in data science faculty members are industry experts and leaders in their fields, bringing decades of professional experience and insights. Renowned faculties from Northwestern University will be teaching, enriching the learning experience.
Fee and Payment
What is the fee for the degree?
The tuition for this degree is $13,000. This can be paid in 6 equal installments of $2,167 each (payable at the start of every term). Please note that apart from the tuition, students will also need to budget for the following expenses:
- A fee of $35 each for online proctored examinations (3 in total)
- The cost of travelling, boarding/lodging, and other expenses for the residency sessions (approximately $500 per residency) will have to be borne by the students.
What is the application fee for enrolling in this MSc Data Science online programme?
A $75 non-refundable application fee is required. Pay online with a credit card.
Are there any discounts or scholarships available for the degree?
While there are no scholarships, enrolled students have the flexibility to pay the fees in installments. We also have a tie-up with financial institutions to provide education loans.
At this point, there is no discount or other financial aid available.
Admissions and Eligibility
What are the eligibility criteria for this Master's in data science programme?
- Students should have completed a 4-year U.S. bachelor’s degree or equivalent.
- No need to give the GRE or GMAT test to qualify for this MS in data science online programme.
- If the medium of instruction were not English, then the student would need to give an English language proficiency test like IELTS or TOEFL.
- While knowledge of mathematics and statistics will be useful, it is not a prerequisite.
What is the application process?
The application process to enroll in this Master's in Data Science programme:
Step 1: Submit application form
Apply online through Northwestern University
application portal.
Step 2: Application review
The admissions committee at Northwestern University will carefully review a submitted application and communicate their decision
Step 3: Join program
An admission offer will be made to selected candidates. Secure your seat by paying the admission fee.
Do I need to submit an SOP for this online Master's in data science programme?
Yes, you need to submit a 300 - 550-word statement of purpose outlining how the Degree Programme will help them meet their academic and professional goals.
How to submit recommendation letters for admission?
You need to enter the names and email addresses for two recommenders. Reference requests are sent to your recommenders via email.
What is the application process for International students?
Applicants need to demonstrate their English proficiency through an official transcript for a bachelor’s degree or higher from an accredited U.S. university or an official TOEFL/IELTS score.
I have completed a 3-year bachelor's degree. Will I be eligible to apply? OR, if I do not have an engineering degree, am I eligible to apply?
- (For students who did not complete their degree in the U.S.)- If your transcript evaluation states that your degree is equivalent to a 4-year U.S. bachelor's degree, then you can apply for this programme.
- (If the student completed their bachelor's degree in the U.S.)- If you have a 4-year bachelor’s degree, then you can apply.
Are there any benefits for early applicants?
Yes, there are benefits for early applicants. Since each cohort has a limited number of spots, applying early gives you the best possible chance of securing admission to the program. Whereas late applications may face increased competition or limited availability.
Is the degree only open to working professionals? Can currently unemployed students take the degree?
While the course is designed to be convenient for working professionals, any student who fulfills the eligibility criteria can do this degree programme.
Do I need to clear an English language proficiency test to apply?
If your bachelor’s degree was taught in English (as confirmed in your transcript evaluation report), then you don’t need to take any further steps. If your transcript is being evaluated by a NACES member agency, make sure to ask them to state this in their report clearly. This is especially important if you’re applying for an online master's degree in data science or a similar program.
If your degree was not taught in English, you will need to provide an English language proficiency test score, such as the IELTS or TOEFL.
What is the last date to apply?
We have multiple slots starting every year. Please connect with your admissions counsellor to know more.
How do I share my academic transcript with the admissions team?
Applicants can contact their Admissions Counselors for guidance on how to complete the transcript evaluation process. Applicants may also submit their transcripts directly to an evaluation agency accredited by the National Association of Credential Evaluation Services (NACES).
Please contact an admissions counsellor to review the process for submitting mark sheets for initial evaluation.
What academic documents do I need to submit to receive an admissions decision?
Applicants will be required to submit scanned copies of their mark sheets or their official transcripts, and a copy of their degree certificate.
Career-Related Queries
What are the several benefits offered by Northwestern University to the learners upon taking up this programme?
Becoming a Northwestern Alumni offers some excellent benefits:
- Learners gain access to global alumni events.
- Learners gain access to free learning events, library resources, and much more.
- Learners also gain access to Northwestern University's email address.
- Learners also get to join a community of award winners and thought leaders in several domains such as Science, Technology, Politics, Arts & Entertainment, and more.
What career transitions can I go for if I enroll in this Master's in Data Science programme by the Northwestern School of Professional Studies?
If you enroll in the Master’s in Data Science program at Northwestern School of Professional Studies, you can pursue various career transitions, such as moving from Associate Architect to Advisory AI Engineer. Other common transitions include advancing from Data Engineer to Senior Data Engineer, AI Developer to Senior AI Developer, Lead Software Engineer to Senior Software Engineer, and Principal Analytics Specialist to Manager of Data Science – Analytics. This program prepares you for growth and leadership roles in the data science and AI fields.
General Queries
What is a Master's degree in data science?
A Master's in data science is a relatively new graduate program that combines core concepts from computer science, probability and statistics, machine learning, and data visualization, among other disciplines. In this program, you’ll develop essential skills in machine learning, data mining, data visualization, and cloud computing, as well as critical thinking, problem-solving, and communication.
Is an MS in data science worth it?
A master's degree opens up leadership roles in data science. Companies tend to prefer a higher qualification for a Senior Data Scientist, a Data Engineer, or a Machine Learning Engineer. And this program will give you the skills necessary to fast-track your career and take on impactful projects.
Can I get a job after an MS in data science?
Data science offers diverse career options, including roles like data scientist, data analyst, machine learning engineer, and data engineer.
How should I choose the right data science Master’s program?
- Identify your career goals and the specific data science fields or industries you want to enter
- Research MS in data science programs for curriculum alignment
- Look for programs offering practical experience, such as internships or real-world projects
- Evaluate faculty expertise and their research interests
- Consider program format preferences: in-person, online, or hybrid courses
- Assess program flexibility if you are a working professional
In collaboration with
The Northwestern University is collaborating with Great Learning to deliver the Post Graduate Program in User Experience. Great Learning is an ed-tech company that has empowered learners from over 170+ countries in achieving positive outcomes for their career growth.
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Master’s in Data Science Programme from Northwestern University School of Professional Studies
Numerous programmes are available across the globe to offer a Master’s Degree in Data Science. Nevertheless, working professionals have several reasons to register in this MS in Data Science programme from Northwestern University, collaborating with Great Learning. Check out the reasons drafted below:
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Northwestern University is ranked as one of the world's highest-ranked institutions.
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According to the U.S. News and World Report 2022 Best College Rankings, Northwestern University is ranked #9 globally. According to the Times Higher Education (THE) World University Rankings 2022, Northwestern University is ranked #24 globally.
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Northwestern University School of Professional Studies is one of the 12 official schools and colleges affiliated with Northwestern University, providing a world-class education to students whose academic pursuits need to be balanced with professional and personal commitments.
Benefits of Pursuing the MS in Data Science Online Programme from Northwestern University
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Pursue the online Data Science Master’s course and learn cutting-edge tools and techniques from seasoned, industry-relevant faculty members.
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Join the distinguished community of faculty and alumni, recipients of the Nobel Prize, MacArthur Genius Grant, Pulitzer Prize, Grammy Award, Academy Award, Emmy Award, Tony Award, and Guggenheim Fellowship, among several other honorary and professional accolades.
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Pursue the course online with live sessions and part-time learning mode, and learn popular programming languages, frameworks, and libraries in a hands-on environment.
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Obtain comprehensive career services, like 1:1 career mentorship, placement process, and more.
Northwestern Alumni Benefits
Becoming a Northwestern Alumni comes with excellent benefits —
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You can acquire lifelong membership, from global alumni events to free learning events, library resources, and a Northwestern email address.
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You will also be affiliated with a community of award-winners and world leaders in science, technology, arts, politics, and entertainment, including the likes of Stephen Colbert, George R.R. Martin, Virginia Rometty, Rahm Emanuel, and others.
Details about MS in Data Science Online Course
In this comprehensive MS in Data Science online course, the students will grasp all the essential skills required to master the Data Science field. Let’s go through in-depth details about the programme:
MSc Data Science Learnings:
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The integration of Data Science and Business Analytics has created a demand for professionals who can make data-driven decisions that propel their organizations forward.
-
You will build the critical data analysis and leadership skills necessary for careers in today's data-driven world.
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You will articulate analytics as a core strategy of Data Science and develop statistically sound and robust analytic solutions.
-
You will be able to transform data into actionable insights.
-
You will be able to demonstrate leadership and formulate and manage plans to address business issues.
-
You will evaluate constraints on the use of data and assess data structure and data lifecycle.
MSc Data Science Syllabus:
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It commences with the fundamentals, such as Mathematics and Statistics for Data Science, R and Python Programming.
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Later, students will learn Machine Learning techniques, including Supervised and Unsupervised Learning algorithms, Database Systems and Data Preparation, Data Governance, and Decision Analytics.
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Lastly, students will learn Artificial Intelligence, Deep Learning, Natural Language Processing, and Computer Vision.
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Upon completion of learning Data Science, students will implement a Capstone Project.
[Explore MSc in Data Science Syllabus]
MSc Data Science Eligibility:
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Students must have completed a 4 year U.S. Bachelor’s degree or equivalent.
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Students with a 3 year Bachelor’s Degree, which is equivalent to a 4 year U.S. Bachelor’s degree, are also eligible to enroll in this programme. Please contact an admissions counselor for further information.
Master’s in Data Science Course Duration
This Master’s course is for 18 months with online live sessions from world-renowned faculty members from Northwestern University.
MS in Data Science Programme Fees
This MS programme costs USD 13,000, which the students can pay through 6 quarterly installments of USD 2,167 each.
Secure an Online Master’s Degree in Data Science from Northwestern University
After successfully completing this course, you will secure an MS Degree in Data Science from Northwestern University.
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