The George Washington Institute of Data Science & Artificial Intelligence within ISODS offers the following professional Master of Science (MS) programs in Data Science & Artificial Intelligence starting Fall 2023. In order to begin, students register their intent with the Institute and start earning credentials. The MS programs are the only of their kind, in order to bolster education in low-income populations.

**Courses**: The courses were selected by the ISODS Professional Exam Committee in 14 categories. Students need to complete at least 11 courses with completion certificates. Students can select additional courses from the list of elective courses. All the courses are offered by universities worldwide and available via selected online platforms with low cost.

**Practicum program**: Students need to take the practicum program for 1 semester to graduate from the professional MS programs. During the practicum program, students will have practical experience in 1 or more real-world projects. Students can optionally choose to write a thesis in addition to the practical experience in the program. The practicum program is online and is offered separately as well to students internationally from all over the world.

**I. Professional MS in Data Science**

A. Core courses

Probability (P): 1 of the following (*)

- An Intuitive Introduction to Probability (University of Zurich)
- Introduction to Probability and Data with R (Duke University)
- Introduction to Statistics (Stanford University)

Statistics (STAT): 1 of the following (*)

Linear Algebra (LA): 1 of the following (*)

- Essential Linear Algebra for Data Science (University of Colorado Boulder)
- Mathematics for Machine Learning: Linear Algebra (Imperial College London)

Calculus (CAL): 1 of the following (*)

- Single Variable Calculus (University of Pennsylvania)
- Calculus: Single Variable Part 1 - Functions (University of Pennsylvania)
- Calculus: Single Variable Part 2 - Differentiation (University of Pennsylvania)
- Calculus: Single Variable Part 3 - Integration (University of Pennsylvania)
- Calculus: Single Variable Part 4 - Applications (University of Pennsylvania)

Database Management (DM): 1 of the following

Object-oriented Programming (PRG): 1 of the following (*)

- Python Classes and Inheritance (University of Michigan)
- Python 3 Programming Specialization (University of Michigan)

Data Structures and Algorithms (DSA): 1 of the following specialization (*)

- Data Science Foundations: Data Structures and Algorithms Specialization (University of Colorado Boulder)
- Data Structures and Algorithms Specialization (University of San Diego)

Time Series (TS): 1 of the following

- Practical Time Series Analysis (The State University of New York)
- Sequences, Time Series and Prediction (DeepLearning.AI)

Big Data Analytics (BDA): 1 of the following

- Big Data Analysis with Scala and Spark (École Polytechnique Fédérale de Lausanne)
- Big Data Analysis with Scala and Spark (Scala 2 version) (École Polytechnique Fédérale de Lausanne)

Predictive Analytics (PA): 1 of the following

- Generalized Linear Models and Nonparametric Regression (University of Colorado Boulder)
- Modern Regression Analysis in R (University of Colorado Boulder)
- ANOVA and Experimental Design (University of Colorado Boulder)
- Regression Models

Machine Learning (ML): 1 of the following

B. Electives

Deep Learning 1 (DL1): 1 of the following

- Neural Networks and Deep Learning (DeepLearning.AI)
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization (DeepLearning.AI)
- Convolutional Neural Networks (DeepLearning.AI)

Deep Learning 2 (DL2): 1 of the following

Reinforcement Learning (RL): 1 of the following

Other elective courses

- Other courses in these categories
- Generative AI/Large Language Models related (students need to discuss with the program)
- Research-based courses (with assigned professors)

**II. Professional MS in Artificial Intelligence**

A. Core courses

Probability (P): 1 of the following (*)

- An Intuitive Introduction to Probability (University of Zurich)
- Introduction to Probability and Data with R (Duke University)
- Introduction to Statistics (Stanford University)

Statistics (STAT): 1 of the following (*)

Linear Algebra (LA): 1 of the following (*)

- Essential Linear Algebra for Data Science (University of Colorado Boulder)
- Mathematics for Machine Learning: Linear Algebra (Imperial College London)

Calculus (CAL): 1 of the following (*)

- Single Variable Calculus (University of Pennsylvania)
- Calculus: Single Variable Part 1 - Functions (University of Pennsylvania)
- Calculus: Single Variable Part 2 - Differentiation (University of Pennsylvania)
- Calculus: Single Variable Part 3 - Integration (University of Pennsylvania)
- Calculus: Single Variable Part 4 - Applications (University of Pennsylvania)

Database Management (DM): 1 of the following

Big Data Analytics (BDA): 1 of the following

- Big Data Analysis with Scala and Spark (École Polytechnique Fédérale de Lausanne)
- Big Data Analysis with Scala and Spark (Scala 2 version) (École Polytechnique Fédérale de Lausanne)

Object-oriented Programming (PRG): 1 of the following (*)

- Python Classes and Inheritance (University of Michigan)
- Python 3 Programming Specialization (University of Michigan)

Data Structures and Algorithms (DSA): 1 of the following (*)

- Data Science Foundations: Data Structures and Algorithms Specialization (University of Colorado Boulder)
- Data Structures and Algorithms Specialization (University of San Diego)

Machine Learning (ML): 1 of the following

Deep Learning 1 (DL1): 1 of the following

- Neural Networks and Deep Learning (DeepLearning.AI)
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization (DeepLearning.AI)
- Convolutional Neural Networks (DeepLearning.AI)

Deep Learning 2 (DL2): 1 of the following

B. Electives

Time Series (TS): 1 of the following

- Practical Time Series Analysis (The State University of New York)
- Sequences, Time Series and Prediction (DeepLearning.AI)

Predictive Analytics (PA): 1 of the following

- Generalized Linear Models and Nonparametric Regression (University of Colorado Boulder)
- Modern Regression Analysis in R (University of Colorado Boulder)
- ANOVA and Experimental Design (University of Colorado Boulder)
- Regression Models

Reinforcement Learning (RL): 1 of the following

Other elective courses

- Other courses in these categories
- Generative AI/Large Language Models related (students need to discuss with the program)
- Research-based courses (with assigned professors)

**Requirements**: Students should have a bachelor degree, and should be able to read, write, and listen to English lectures effectively on online platforms such as Coursera.

* undergraduate level courses, can be replaced by previously-taken equivalent courses

More information is TBA. The plan is to start in Spring 2024. For inquiries, please send us an email to This email address is being protected from spambots. You need JavaScript enabled to view it.