Applied Data Science Program

In this 12-week program, you will be able to upgrade your data analytics skills by learning the theory and practical application of 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.

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.

• Subhodeep Dey - Data Scientist (Project Lead), UnitedHealth Group (India)
• Joseph Deutsch – Data Scientist, Capital One (United States)
• Kalle Bylin - Product Engineer, Modyo (Colombia)
• George Liu – Senior Data Scientist, Chatter Research (Canada)
• Vaibhav Verdhan - Principal Data Scientist, Johnson & Johnson (Ireland)

Master the skills you need with a data science mentor?

Mentors of the Applied 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 Microsoft, Cognizant, Mu Sigma, 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 Pre-work
As an aspiring 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 data science, such as data handling, feature engineering, and predictive analysis. It helps you best understand the concepts during the live virtual 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 7.5 hours of video content along with practice quizzes. 

Contact Great Learning for more information at adsp.mit@mygreatlearning.com or call +1 617 468 7899 / +91 9606 053 237.

• Understand the intricacies of data science techniques and their applications to real-world problems.
• Implement various machine learning techniques to solve complex problems and make data-driven business decisions.
• Explore the realms of Machine Learning, Deep Learning, and Neural Networks, and how they can be applied to areas such as Computer Vision.
• Develop strong foundations in Python, mathematics, and statistics for data science.
• Understand the theory behind recommendation systems and explore their applications to multiple industries and business contexts.
• Build an industry-ready portfolio of projects to demonstrate your ability to extract business insights from data.

Program Curriculum

MIT Professional Education's Applied Data Science Program, with curriculum developed and taught by MIT faculty, is delivered in collaboration with Great Learning.  Unique to the Applied 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 12 weeks long:

• 2 weeks for foundations
• 6 weeks of core curriculum, including practical applications
• 1 week for project submissions
• 3 weeks for a final, integrative Capstone project

Week 1&2 - Module 1

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 - Module 2

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 - Module 3

Machine Learning

• Introduction to Supervised Learning -Regression
• Model Evaluation- Cross Validation and Bootstrapping
• Introduction to Supervised Learning-Classification

Week 5 - Module 4

Practical Data Science

• Decision Trees
• Random Forest
• Time Series (Introduction)

Week 6 - Learning Break

Week 7 - Module 5

Deep learning

• Intro to Neural Networks
• Convolutional Neural Networks
• Graph Neural Networks

Week 8 - Module 6

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 10-12 - Module 7 

Capstone Project

• Week 10: Milestone 1
• Week 11: Milestone 2
• Week 12: Synthesis + Presentation

• 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