Artificial Intelligence (AI)

School Level:

  • Intro to AI: Basic concepts, history of AI, types of AI (narrow vs. general).
  • Python Programming Basics: Required for AI development.
  • Machine Learning Fundamentals: Introduction to supervised and unsupervised learning, simple algorithms like linear regression.
  • Project-Based Learning: Simple AI projects like chatbot development or image recognition.

College Level:

  • Advanced Python and Libraries: NumPy, Pandas, TensorFlow, PyTorch.
  • Deep Learning: Neural networks, CNNs, RNNs, NLP.
  • AI Ethics and Society: Discussion on AI’s impact on society, ethics in AI development.
  • Capstone Project: Develop an AI application or research project, like predictive analytics or autonomous systems.

Data Science

School Level:

  • Data Analysis Basics: Introduction to data, types of data, basic statistics.
  • Excel for Data Analysis: Pivot tables, basic data visualization.
  • Introduction to Programming for Data: Python or R basics.
  • Mini-Projects: Analyze datasets like sports statistics or weather data.

College Level:

  • Statistical Methods: Probability, regression, hypothesis testing.
  • Data Manipulation: Advanced SQL, data cleaning with Python/Pandas.
  • Machine Learning for Data Science: More complex models, feature engineering.
  • Data Visualization: Tools like Tableau, Matplotlib, or Seaborn.
  • Big Data: Introduction to Hadoop, Spark, or cloud platforms like AWS for data processing.
  • Capstone: Real-world data analysis project, possibly in collaboration with local businesses.

Digital Marketing

School Level:

  • Fundamentals of Marketing: Marketing mix, consumer behavior.
  • Digital Marketing Basics: SEO, SEM, content marketing.
  • Social Media Marketing: Platforms, strategies, analytics.
  • Practical Workshops: Create campaigns for school events.

College Level:

  • Advanced Digital Marketing Strategies: PPC, email marketing, affiliate marketing.
  • Marketing Analytics: Using Google Analytics, social media metrics.
  • Content Strategy: SEO for content, storytelling in marketing.
  • E-commerce: Basics of setting up and running an online store.
  • Digital Strategy Development: Case studies, strategic planning.
  • Internship/Project: Work with local businesses or manage a digital campaign.

Web Designing

School Level:

  • HTML & CSS Basics: Structure and style of web pages.
  • Introduction to User Experience (UX): Basic principles of design and user interaction.
  • Web Design Tools: Use of platforms like Wix or WordPress for no-code solutions.
  • Project: Design a simple personal or school project website.

College Level:

  • Advanced HTML5, CSS3, JavaScript: Responsive design, animations.
  • User Interface (UI) Design: Figma, Adobe XD, or Sketch for wireframing and prototyping.
  • Front-End Frameworks: Introduction to React, Vue.js, or similar for dynamic web apps.
  • Back-End Basics: Node.js or Python for server-side scripting.
  • UX Research: User personas, journey mapping, usability testing.
  • Portfolio Development: Students build a professional web design portfolio.
  • Capstone: Design and develop a comprehensive website or web application.

Cross-Curricular Elements:

  • Interdisciplinary Projects: Combining elements from AI, data science, digital marketing, and web design for holistic projects.
  • Soft Skills: Communication, project management, teamwork, especially in group projects.
  • Ethics and Responsibility: Discussions on privacy, data security, and ethical considerations in technology use.

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