Data is getting created at a rapid pace. It is estimated that more than 2 quintillion bytes of data have been created each day in the last two years. As organizations experience an overflow of data, they are sparing no effort to extract meaningful insights to make smarter business decisions. In order to help you unravel the true worth of data, MIT Professional Education offers the Applied AI and Data Science Program (formerly known as the Applied Data Science Program), which aims to prepare AI-powered decision makers for the future.
MIT Professional Education's Applied AI and Data Science Program, with a curriculum developed and taught by MIT faculty, is delivered in collaboration with Great Learning.
THIS program MAY BE TAKEN AS A STANDALONE PROGRAM OR AS PART OF THE PROFESSIONAL CERTIFICATE PROGRAM IN MACHINE LEARNING & ARTIFICIAL INTELLIGENCE. COMPLETING THE COURSE WILL CONTRIBUTE 5 DAYS TOWARDS THE CERTIFICATE.
In this 14-week program, you will be able to upgrade your artificial intelligence and data science skills by learning the theory and practical application of prompt engineering, agentic AI, ethical AI, supervised and unsupervised learning, time-series analysis, neural networks, recommendation engines, regression, and computer vision, to name a few.
Upon successful fulfillment of requirements, you will receive a certificate of completion from MIT Professional Education at the end of the program.
Certificate of Completion from MIT Professional Education
Program Mentors
In today’s fast-changing business landscape, acquiring new knowledge and skills for real estate finance and development through traditional, concept-led courses can be time-consuming and ineffective. With mentored learning, you can accelerate your learning, increase productivity, have a better grasp of the subject and discover new problem-solving perspectives.
The program mentors coach you to work on hands-on industry relevant projects by data science and machine learning experts via live and personalized mentoring and learning sessions to give you a practical understanding of core concepts.
- Omar Attia - Senior Research Engineer, Apple
- Bhaskarjit Sarmah - Director, BlackRock
- Vibhor Kaushik - Senior Machine Learning Scientist, Amazon
- Nirmal Budhathoki - Senior Data Scientist, Microsoft
- Mohit Khakaria - Senior Machine Learning Engineer, Ford Motor Company
- Rohit Dixit - Senior Data Scientist, Siemens Healthineers
- Vaibhav Verdhan - Senior Director Global, AstraZeneca
- Udit Mehrotra - Data Scientist, Google
- Amish Suchak - Data Science Team Lead, XSOLIS
- Nirupam Sharma - Data Science Vice President, Big Village
- Deepa Krishnamurthy - Director, AI Solutions Engineering, Koru
- Marco De Virgilis - Actuarial Data Scientist Manager, Arch Insurance Group Inc.
- Cristiano Santos De Aguiar - Biomedical Machine Learning Engineer, Oncoustics
- Matt Nickens - Senior Manager, Data Science, CarMax
- Saber Fallahpour - Principal Data Scientist, Altair
- Asim Sultan - Senior Machine Learning Engineer, RiskHorizon AI
Master the skills you need with a data science mentor?
Mentors of the Applied AI and Data Science program come with years of experience from top organizations and help you understand best practices and deliver the actionable know-how you need to succeed in your new role. You will be able to quickly gauge the topics taught by MIT Faculty, and gain insights into how these topics are applied at an organizational level. You will also solve real business problems with guidance from your mentors and will be ready to hit the ground running as soon as you walk out of the program.
How do mentorship sessions work?
Mentoring sessions occur in small groups that are called micro classes. You will be grouped with learners with similar years of experience and backgrounds so that the mentors can determine the right pace of teaching, level of techniques, and relevant case studies, to use in order to maximize the benefit. When learning in groups, you can also garner how a practical skill like data science is applied to different industry-specific problems. In addition, you will work with the mentors every weekend to brush up on topics and revisit the concepts you covered earlier.
Mentors take you from conceptualization to implementation by explaining complex concepts and guiding you through hands-on assignments. Here are a few ways mentors add to your learning experience:
- Build tangible skills through interactive, hands-on coding walkthroughs
- Gain Industry expertise from experienced data scientists at globally renowned companies such as Apple, Microsoft, Blackrock, etc.
- Connect theoretical concepts to actual practical examples of how these analytic techniques are used across various industries
- Engage with your mentor on a deeper level and get support and guidance when making the transition into a data science career
- Prepare for your interviews and foster data-driven problem-solving within teams
Data Science and AI Pre-work
As an aspiring AI and data science professional, Python and statistics play a valuable part in your toolkit. The pre-work lets you acquire foundational knowledge in these subjects and have a better understanding of concepts that are mandatory in AI and data science, such as Python programming, statistics, the data science lifecycle, and the evolution of AI and Generative AI,/u>. It helps you best understand the concepts during the live online sessions with MIT faculty. Quickly cope with the latest data science practices and implement them as you walk through the program.
Pre-work consists of 11 hours of video content along with practice quizzes.
About Great Learning
Great Learning is a global EdTech company offering professional and higher education programs in blended, classroom, and online modes across technology, data and business domains.
The MIT Professional Education's Applied AI and Data Science Program, with a curriculum developed and taught by MIT faculty, is delivered in collaboration with Great Learning.
Contact Great Learning for more information at aaidsp.mit@mygreatlearning.com or call +1 617 468 7899 / +91 9606 053 237.
- Explore the core concepts behind Artificial Intelligence, Generative AI, and Data Science, and their real-world applications.
- Learn how to transform and structure data to build more accurate and reliable Machine Learning models.
- Use a range of techniques to solve data-driven challenges and support decision-making across business functions.
- Explore how different modern AI techniques can be implemented across various business applications.
- Complete a portfolio of hands-on projects, including a 3-week Capstone, that showcases your ability to apply Data Science techniques to meaningful business scenarios.
Program Curriculum
MIT Professional Education's Applied AI and Data Science Program, with curriculum developed and taught by MIT faculty, is delivered in collaboration with Great Learning. Unique to the Applied AI and Data Science Program is a dedicated Program Manager, provided by Great Learning, who will be your single point of contact for all academic and non-academic queries in the program. They will keep track of your learning journey, give you personalized feedback, and the required nudges to ensure your success.
The program is 14 weeks long:
- 2 weeks for foundations
- 8 weeks of core curriculum, including practical applications
- 1 week for project submissions
- 3 weeks for a final, integrative Capstone project
Week 1&2 - Foundations for Data Science
- Python Foundations - Libraries: Pandas, NumPy, Arrays and Matrix handling, Visualization, Exploratory Data Analysis (EDA)
- Statistics Foundations: Basic/Descriptive Statistics, Distributions (Binomial, Poisson, etc.), Bayes, Inferential Statistics
Week 3 - Data Analysis & Visualization
- Exploratory Data Analysis, Visualization (PCA, MDS and t-SNE) for visualization and batch correction
- Introduction to Unsupervised Learning: Clustering includes - Hierarchical,
- K-Means, DBSCAN, Gaussian Mixture
- Networks: Examples (data as a network versus network to represent dependence among variables), determine important nodes and edges in a network, clustering in a network
Week 4 - Machine Learning
- Introduction to Supervised Learning -Regression
- Model Evaluation- Cross Validation and Bootstrapping
- Introduction to Supervised Learning-Classification
Week 5 - Revision Break
Week 6 Practical Data Science
- Decision Trees
- Random Forest
- Time Series (Introduction)
Week 6 - Learning Break
Week 7 - Deep learning
- Intro to Neural Networks
- Convolutional Neural Networks
- Graph Neural Networks
Week 8 - Recommendation Systems
- Intro to Recommendation Systems
- Matrix
- Tensor, NN for Recommendation Systems
Week 9 - Project Week
Time for participants to finish and submit their projects
Week 10- Generative AI Foundations
Week 11 - Business Applications of Generative AI
Week 12 - 14 - Capstone Project
- Professionals who are interested in a career in Data Science and Machine Learning.
- Professionals interested in leading Data Science and Machine Learning initiatives at their companies.
- Entrepreneurs interested in innovation using Data Science and Machine Learning.
Prerequisites: Basic knowledge of Computer Programming and Statistics
Testimonials
