Data Analysis and AI Specialist

Data Analysis and AI Specialist + Internship

The 200-hour, 8-week internship offers hands-on experience in a professional setting, enabling you to apply the knowledge and skills gained in the Data Analysis and AI Specialist program. During the internship, you will gain industry experience, work on practical data analysis and AI-related tasks, and engage with professionals in the field. This practicum bridges the gap between classroom learning and career readiness.

AI Capstone

The AI Capstone is the final course in the AI-focused curriculum, designed to provide you with hands-on experience in solving real-world problems using artificial intelligence. You will apply your knowledge of AI and machine learning techniques to a project that involves the design, development, and deployment of an AI-based solution. At the end of the course, you will have a project that you can showcase to faculty, peers, and industry professionals.

Computer Vision

This course provides an in-depth introduction to computer vision, focusing on the use of machine learning techniques to interpret and analyze visual data. More specifically, this course covers foundational and advanced topics in computer vision, from image classification and object detection to cutting-edge applications like image generation and captioning. You will gain hands-on experience with tools like Keras for building machine learning models and will explore the latest techniques in deep learning and transformer architecture for vision tasks.

Career Planning and Preparation - Level 1

This course will introduce you to and provide practice in using the tools required for a successful job search. The concepts covered in this course will help you maintain a career-focused approach throughout your studies so that you are better prepared to conduct a job search after graduating. Specifically, you will learn how to identify your soft and hard skills and how to articulate your abilities in a clear and concise Elevator Pitch that will appeal to employers and resonate with industry contacts. You will learn about the job search resources available to you including using career websites, creating LinkedIn profiles, accessing the “hidden” job market, and networking. You will examine sample résumés and cover letters and begin the process of creating your own professional résumés and cover letters that align with current conventions for content, organization, and formatting. You will also learn about the role of references, thank you letters, workplace philosophies, and strategies for success including maintaining a professional image and using proper etiquette when communicating with potential employers and industry contacts.

Career Planning and Preparation – Level 2

This course builds on concepts and skills introduced in the Career Planning and Preparation Level 1 course. In this subsequent course, you will update and refine your résumé and LinkedIn Profile. You will continue writing cover letters and learn the value of customizing cover letters to specific job postings. You will have the opportunity to apply this knowledge as you conduct a job search and write a cover letter tailored to an ideal job post. Through research, you will create a list of top employers and target current industry opportunities. You will learn about current methods for applying to job postings using technology. You will also gain an understanding of the job interview process, typical interview questions and possible responses, and expectations of both the interviewer and interviewee. In addition, you will engage in practical application of the interview process through role-play. Topics such as negotiating salary, self-management, and on-the-job success for placements and post-graduate employment will be also covered.

Data Analysis and Visualization Capstone

This capstone course is designed to provide you an opportunity to apply your skills in data analysis, statistical methods, and data visualization to a substantial, real-world project. You will tackle a complex data problem, using advanced analysis techniques to uncover insights, build predictive models, and communicate findings through compelling visualizations. The focus will be on turning data into actionable insights for decision-making, and you will be expected to design and implement your analysis with careful attention to ethical issues, data integrity, and clarity in communication. At the end of the course, you will have a project that you can showcase to faculty, peers, and industry professionals.

Ethics and Communication for Data Science and AI

In the rapidly evolving fields of data science and artificial intelligence (AI), professionals must not only possess strong communication skills but also navigate complex ethical challenges. This course explores ethics and communication in the practice of data analysis and AI development, equipping you with the tools to engage in responsible, transparent, and impactful work.

Data Analysis with Python

This course is designed to introduce you to the powerful tools and techniques available in Python for data analysis and visualization. You will learn how to collect, clean, and prepare data for analysis. Additionally, you will leverage the core Python libraries and techniques that form the foundation of data analysis, including Pandas for data manipulation, Seaborn for visualization, as well as the tools for building predictive models and presenting insights from data.

Data Analysis with SQL

In this course you will learn the essential skills and techniques required to analyze and manipulate data using SQL, one of the most widely used languages for data management and analysis. More specifically, this course covers the full process of preparing, analyzing, and interpreting data using SQL. You will gain hands-on experience in structuring SQL queries, profiling data, cleaning and reshaping datasets, and performing advanced analytical tasks such as time series analysis, cohort analysis, and anomaly detection.

Statistics for Data Analysis

This course offers a thorough introduction to the statistical techniques essential for analyzing data and drawing meaningful conclusions. You will explore both the statistical methods commonly used in data analysis, including sampling distributions, hypothesis testing, regression analysis, classification, and statistical machine learning.

Data Visualization

In this course, you will learn the principles and techniques of data visualization, a crucial skill for anyone working with data. Data visualization is not only about creating charts, but also about effectively conveying information and insights to audiences through compelling, informative visuals. This course will provide you with a comprehensive understanding of how to visualize data for both exploration and explanation, enabling you to create visuals that tell impactful stories, communicate insights effectively, and support informed decision-making.

Digital Literacy for Professionals

This course introduces the fundamental concepts and principles of learning and working in a digital environment. This course will cover the following elements: using devices and handling information, creating and editing information, communicating and collaborating, and being safe and responsible online.

Introduction to Python Programming

This course offers a comprehensive introduction to Python programming. In addition to core Python programming concepts, you will develop good programming practices, ensuring the programs are robust and reliable. By the end of the course, you will have gained the practical skills and theoretical understanding needed to effectively work with Python in future courses.

Introduction to SQL Databases

This course provides a comprehensive introduction to the fundamentals of SQL databases, focusing on the essential skills required to design, query, and manage relational databases. You will explore key concepts in database design, including how to create and structure tables to ensure efficient data storage and retrieval. By the end of the course, you will have gained the practical skills and theoretical understanding needed to effectively work with SQL data in future courses.

Machine Learning

This course offers a comprehensive introduction to machine learning, equipping you with the fundamental concepts and techniques used to build and deploy machine learning models. You will learn the core principles behind different types of machine learning, including supervised and unsupervised learning, classification, and regression, as well as advanced topics such as neural networks, deep learning, and reinforcement learning. The course emphasizes practical, hands-on experience with popular machine learning libraries like Scikit-Learn and PyTorch, allowing you to implement algorithms, evaluate models, and apply machine learning techniques to real-world problems.

Predictive Analytics

This course introduces you to the field of predictive analytics, focusing on the application of machine learning techniques to solve business problems and generate actionable insights. You will learn how to harness data to make informed predictions, improve decision-making, and optimize business outcomes. The course covers key concepts in machine learning such as the Cross-Industry Standard Process for Data Mining (CRISP-DM) lifecycle, data exploration, and feature design, as well as advanced techniques like deep learning and reinforcement learning.

Student Success Strategies

This course stresses the importance of developing non-technical skills to enhance personal, academic, and career success. The course will address strategies that are important for all adult learners, such as managing finances, maintaining health and wellness, understanding learning styles, setting goals, and honing practical study skills (such as memory, reading, and test-taking techniques). In addition, this course emphasizes strategies needed to succeed in your program, such as navigating technology efficiently, interacting and engaging with peers and facilitators/instructors, and managing learning time and space.