Program Title:

Master Data Science & Artificial Intelligence Specialist

Duration:

Approximately 12 weeks (flexible pacing; each module could be 1-2 weeks depending on student availability)

Format:

100% online (asynchronous lectures, readings, activities, and live or recorded Q&A sessions)

Target Audience:

Aspiring data scientists, AI enthusiasts, IT professionals, and anyone interested in leveraging data-driven insights and advanced algorithms to drive innovation and solve complex problems.

Program Outcomes:

Upon completion, learners will be able to:

• Understand and implement industry best practices in data science and artificial intelligence.

• Collect, clean, and preprocess large datasets to extract actionable insights.

• Utilize statistical methods and machine learning algorithms for predictive modeling.

• Develop, train, and deploy AI models for tasks such as image recognition, natural language processing, and recommendation systems.

• Integrate data visualization tools to effectively communicate complex insights.

• Address ethical considerations and ensure compliance with data privacy and AI governance standards.

• Demonstrate leadership and problem-solving skills in real-world data science and AI projects.

Course Structure

The program is divided into 12 modules. Each module includes:

• Video Lectures: Short, engaging lessons delivered by industry experts.

• Readings & Reference Materials: Curated articles, whitepapers, e-books, and official documentation.

• Case Studies & Real-World Scenarios: Practical examples and simulated exercises to illustrate key concepts.

• Interactive Activities & Quizzes: Hands-on labs and assessments to reinforce learning.

• Assignments: Practical tasks that simulate real-world data challenges.

• Discussion Forums: For peer interaction, Q&A, and knowledge sharing.

Detailed Module Breakdown

Module 1: Introduction to Data Science & AI

Objective: Understand the fundamentals of data science and the transformative impact of AI across industries.
Topics Covered: Overview of data science, key terminologies, evolution of AI, and current industry applications.
Activities: Introductory quiz and discussion forum.

Module 2: Data Acquisition, Cleaning & Preprocessing

Objective: Learn effective techniques for collecting, cleaning, and preparing data for analysis.
Topics Covered: Data sourcing, quality assessment, cleaning methods, and data transformation techniques.
Activities: Hands-on lab with real-world datasets.

Module 3: Exploratory Data Analysis & Visualization

Objective: Develop skills to explore and visualize data to uncover patterns and insights.
Topics Covered: Statistical analysis, visualization tools (e.g., Matplotlib, Seaborn), and dashboard creation.
Activities: Practical exercise in creating interactive visualizations and summarizing findings.

Module 4: Statistical Foundations & Machine Learning Basics

Objective: Build a solid foundation in statistics and introductory machine learning concepts.
Topics Covered: Descriptive and inferential statistics, probability theory, and an overview of common machine learning algorithms.
Activities: Quizzes and mini-project on statistical analysis and model evaluation.

Module 5: Supervised Learning Techniques

Objective: Master supervised learning methods for predictive analytics.
Topics Covered: Regression, classification, decision trees, and ensemble methods.
Activities: Hands-on lab building and evaluating supervised learning models.

Module 6: Unsupervised Learning & Clustering

Objective: Explore techniques for uncovering hidden patterns in data without predefined labels.
Topics Covered: Clustering algorithms (K-means, hierarchical clustering), dimensionality reduction, and anomaly detection.
Activities: Practical assignment on clustering analysis and interpreting results.

Module 7: Deep Learning Fundamentals

Objective: Delve into the principles and applications of deep learning.
Topics Covered: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and model optimization.
Activities: Lab exercises on building deep learning models using frameworks such as TensorFlow or PyTorch.

Module 8: Natural Language Processing & Computer Vision

Objective: Explore AI techniques for processing text and image data.
Topics Covered: NLP methods, sentiment analysis, image recognition, and feature extraction techniques.
Activities: Hands-on projects in text analysis and image classification.

Module 9: Model Deployment & Productionizing AI

Objective: Learn how to deploy and maintain AI models in production environments.
Topics Covered: Model evaluation, deployment strategies, cloud-based AI services, and performance monitoring.
Activities: Simulation of deploying an AI model and managing its lifecycle.

Module 10: Ethical, Legal & Compliance Considerations in AI

Objective: Navigate the ethical, legal, and regulatory landscape surrounding AI applications.
Topics Covered: Data privacy laws, bias and fairness in AI, ethical implications, and governance frameworks.
Activities: Discussion forum and case study analysis on ethical AI practices.

Module 11: Emerging Trends & Innovations in Data Science & AI

Objective: Stay ahead of the curve by exploring cutting-edge advancements and future directions in the field.
Topics Covered: Reinforcement learning, generative models, edge AI, and innovative applications in various industries.
Activities: Research assignment and group presentation on emerging trends and their potential impact.

Module 12: Capstone Project

Objective: Apply all learned concepts to develop a comprehensive data science and AI solution.
Activities: Create and present a complete project that integrates data acquisition, analysis, model building, and deployment for a simulated real-world scenario.

Certificate

Industry Recognized Certificate of Complition

Teacher

Mia Anderson