MS in Data Science Programme at School of Professional Studies, Evanston
Become a highly paid data scientist with the “MS in Data Science Programme” degree course.
Online
18 Months
13,000 USD Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Video and Text Based |
Course Overview
The “MS in Data Science Programme” is an online Master’s degree course offered by Northwestern University School of Professional Studies in association with Great Learning. According to U.S. News & World Report 2020, Northwestern University has been ranked in the top 10 U.S. universities, so it’s a great opportunity to study from.
The “MS in Data Science Programme” syllabus is a judicious blend of not only foundation data science theory courses but also popular programming frameworks languages, and libraries in a hands-on real environment. Along with curriculum learning, candidates also have a Capstone project to showcase their skills. The course has been curated by experienced faculty with relevant industry experience.
The “MS in Data Science Programme” offers students enrolled in a dedicated placement process, access to job boards, One to one career support guidance and more to get placed with jobs in their dream companies. Showcasing this degree can be beneficial for the candidates as they will have a competitive edge over their peers while getting hired.
The Highlights
- Online, Part-Time Learning format
- 18 Months program duration
- Live Classroom Sessions
- World-Class Curriculum
- Comprehensive Learning Modules
- Degree by Northwestern University & Great Learning
- Real World capstone project
- 1:1 Career mentorship
- Dedicated placement assistance
- Northwestern University Alumni Status
Programme Offerings
- 18 Months program
- Best Curriculum
- Capstone Project
- Alumni Status
- Career Mentorship
- Degree by Northwestern University
- Live Classroom
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | Northwestern University, Evanston |
MS in Data Science Programme fee structure :
Description | Amount |
MS in Data Science Programme | USD 13,000 |
Eligibility Criteria
Educational Qualification
- Students must have a 4 year U.S. bachelor’s degree or equivalent to it from any recognized U.S University.
- If the medium of instruction is not English for any student then he/she would have to sit for an English language proficiency test like IELTS or TOEFL.
Degree qualifying details
- All the subjects covered under this course will have a grading mechanism that candidates have to acquire, attend all online lectures, do assignments, and finally complete the capstone project to be able to qualify for the degree of completion.
Admission Details
All candidates can follow the steps below to get admitted to the “MS in Data Science Programme” degree by Northwestern University & Great Learning:
Step 1: Visit the official website: https://www.mygreatlearning.com/ms-data-science-programme, and apply online.
Step 2: Northwestern University has an admissions committee that will review each application submitted
Step 3: Selected students will know their selection decision.
Step 4: All selected students will be made an admission offer,
Step 5: And finally they will have to block their seats by paying the admission fee.
Application Details
All candidates will have to apply online, and fill up an application form for admission to the “MS in Data Science Programme” degree course. For an application form, candidates will have to create an account using their name, email address, and set up a password. After this is done they can log in using their credentials, and start filling up the form.
The Syllabus
The course for the “MS in Data Science Programme” is a curation of fundamental courses, Artificial Intelligence, Analytics & Modeling course, capstone project, and various tools covered.
- 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
- 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
- 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
- Python, R, SQL, Pandas
- 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.
- 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.
- Describe the fundamentals of generative AI models, including the historical development, working principles, and the various types of models.
- Apply effective prompt engineering techniques to improve the performance and control the behavior of generative AI models.
- Identify and explore diverse applications and use cases where generative AI can be leveraged.
- Demonstrate techniques to customize and optimize generative AI models, recognizing the ethical challenges of generative AI models to ensure responsible data usage, mitigate bias, and prevent misuse.
- Comprehend the concept of explainable AI, recognize its significance, and identify different approaches to achieve explainability in AI systems.
- 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.
Instructors
School of Professional Studies, Evanston Frequently Asked Questions (FAQ's)
The classes are held online, and are an amalgamation of both life, and pre-recorded video lectures.
The lectures are held for a period of 2.5 hours for the “MS in Data Science Programme” degree course.
Though attendance is very important for each session if a student misses any session he/she can study from lectures uploaded in the Learning Management System.
The basic difference between these two modes is the mode of delivery of the lecture, one is face to face, and the other is online.
There are no scholarships available but flexible options for payment of fees are available for the “MS in Data Science Programme” degree.
If a candidates’ transcript evaluation has stated that the degree is similar to a 4 year U.S. bachelor's degree, then he/she can definitely apply.