Program Overview

Program Overview

The Applied AI & Data Science program at Brown University is designed to strengthen foundational knowledge and learn to build and deploy artificial intelligence and data science models for industry-specific problem-solving. This 12-week, online, self-paced program is developed by Brown-appointed faculty and delivers a cutting-edge curriculum that combines theoretical foundations with hands-on technical application.

Key Takeaways

• Become proficient with industry-standard tools and methodologies to prepare and analyze data effectively
• Learn the essentials of Generative AI and its use cases
• Build machine learning models to enhance decision-making processes
• Develop expertise in both linear and non-linear ML models
• Attain a comprehensive understanding of deep learning techniques
• Engage in practical learning through industry-relevant hands-on projects with access to integrated labs

Program Details

Online Format and Curriculum

This program provides working professionals with a 12-week, self-paced online learning path featuring top-tier video content, practical projects, and optional master classes.

• Program Overview: Gain a complete understanding of the Applied AI & Data science program, understand the course modules and the topics covered, and get to know the program learning path and program faculty.
• Foundations of Data Science: Understand the fundamentals of statistics and basic categories of data science, learn about the background of Python and its functionalities, and dive deep into various Python libraries like NumPy and pandas.
• Understanding the Data: Acquire knowledge about machine learning, including a comprehensive understanding of the diverse algorithms employed within this domain, discover the process of collecting data from multiple sources and transforming it into a practical and usable format, explore and study the various algorithms utilized in machine learning to develop expertise in this area, and dive deep into the intricacies of exploratory data analysis.
• Preparing the Model: Learn how feature engineering is used to prepare a machine learning model, dive deep into various supervised learning techniques, including but not limited to linear and logistic regression, decision trees, and random forests, and delve into unsupervised learning and gain proficiency in various techniques, including K-means clustering, hierarchical clustering, and dimensionality reduction.
• Training the Model: Learn the theoretical and practical aspects of model training, evaluation, and fine-tuning, gain proficiency in assessing the performance of models by utilizing established evaluation measures and matrices commonly employed in the industry, analyze and interpret the results of your models, making informed decisions about their effectiveness and suitability for real-world applications, and master the skill of model fine-tuning to improve efficiency.
• Deep Learning: Gain a solid understanding of deep learning, training artificial neural networks to perform complex tasks, understand artificial neural networks to design, train, and utilize neural networks effectively, learn how deep learning enables the automatic learning of hierarchical representations of data, use CNNs for image recognition, ensuring that participants can apply this architecture to tasks like object detection, classification, and image analysis, become familiar with RNNs, and learn how to create and utilize GANs for content generation.
• Generative AI: Learn about the taxonomy of generative model families and the ways to build generative models, understand the concepts of variational autoencoders, generative adversarial networks (GANs), diffusion models, and transformers, and master the training and analysis of GPT, understanding the broader applications of generative AI in various fields.

Who Should Attend

Examples of target program participants:

• Individuals from across industries who seek to advance in careers like Software Development, IT Products, Machine Learning, Data Consultancy, Data Science, and AI.
• Individuals with prior work experience (preferred but not required).
• Individuals who have a basic understanding of mathematics, statistics, and technical programming.

Core Competencies, Software & Tools, and Practical Applications

Core Competencies

• Machine Learning
• Generative AI
• Supervised and Unsupervised Learning
• Data Analysis
• Model Selection
• Model Training and Evaluation
• Neural Networking
• Generative Pretrained Transformers
• Data Science
• Artificial Intelligence

Software & Tools

• ChatGPT
• Python
• NumPy
• Pandas
• SciPy
• Matplotlib
• Seaborn

Practical Applications

You’ll apply your new skills to solve real-world business challenges through practical, hands-on projects.

1. Retail Sales Analysis: Analyze the company’s sales data for the fourth quarter across Australia, state by state, and help the company make data-driven decisions for the coming year.
2. Employee Turnover Analysis: Utilize predictive analytics to predict the employee turnover time with the HR department data.
3. Loan Repayment Prediction: Create a Deep Learning model that can predict the ability of loan applicants to repay the loan.
4. Identify Credit Card Fraudulent Transactions: Analyze the dataset provided and build a model to identify fraudulent credit card transactions from legitimate ones.

Certificate of Completion

Upon successful completion of the program, you will receive a Certificate of Completion from Brown University School of Professional Studies.

Program Details

• Duration: 12 weeks
• Format: Online, self-paced
• Tuition Cost: $2,995
• Start Date: December 1
• Optional Live Master Classes: Monthly
• Capstone Project: Implement the skills learned throughout the program by solving industry-specific challenges.
• Electives:
  • Master Classes by Brown Faculty: Attend live, online master classes led by Brown faculty to interact with peers and gain insights into the current trends and future outlook of data science.
  • Doubt Clarification and Project Mentoring: Clarify any questions or concerns about the curriculum and projects completed.