Learning Outcomes

• Become a Data Scientist and get hired
• Master Machine Learning and use it on the job
• Deep Learning, Transfer Learning, and Neural Networks using TensorFlow 2.0
• Use modern tools that big tech companies like Google, Apple, Amazon, and Meta use
• Present Data Science projects to management and stakeholders
• Learn which Machine Learning model to choose for each type of problem
• Real-life case studies and projects to understand real-world applications
• Learn best practices in Data Science Workflow
• Implement Machine Learning algorithms
• Learn Python programming using the latest Python 3
• Improve Machine Learning Models
• Pre-process, clean, and analyze large datasets
• Build a portfolio of work to enhance your resume
• Set up a Developer Environment for Data Science and Machine Learning
• Understand Supervised and Unsupervised Learning
• Apply Machine Learning to Time Series data

Learning Content

• Introduction
• Machine Learning and Data Science Framework
• Data Science Environment Setup
• Pandas: Data Analysis
• Matplotlib: Plotting and Data Visualization
• Scikit-learn: Creating Machine Learning Models
• Supervised Learning: Classification + Regression
• Milestone Project 1: Supervised Learning (Classification)
• Milestone Project 2: Supervised Learning (Time Series Data)
• Data Engineering

Learning Outcomes

• Identify challenges, overcome barriers, foster teamwork, and leverage diverse perspectives for effective cross-functional collaboration.
• Learn techniques to enhance communication, establish clear channels, and promote active listening and feedback within cross-functional teams.
• Strengthen problem-solving skills, analyze different perspectives, and make informed decisions within collaborative efforts.
• Utilize techniques for collaborative problem-solving, decision-making, consensus-building, and informed choices as a team.

Learning Content

• Cross Functional Collaboration
  • This course focuses on the principles and strategies for collaborating effectively with colleagues from different functional areas. Students will learn to overcome challenges, foster teamwork, and leverage diverse perspectives to achieve common goals.
  • The course covers various techniques to improve communication and information sharing across departments and develop strategies for managing conflict and resolving disputes.
  • Through case studies and group exercises, students will enhance their problem-solving skills and learn how to make informed decisions through collaborative efforts.
  • The course also emphasizes the importance of building a culture of trust and cooperation among team members. By the end of the course, students will be equipped with the necessary skills to collaborate successfully with colleagues from different backgrounds and functional areas, leading to enhanced productivity and overall organizational success.

Learning Outcomes

• Basics of analytics terminology
• How data is used to make business decisions
• Identify the ideal analytical methodology for your specific needs
• Understand ways to collect, analyze, and visualize data
• Descriptive Analytics and how they are embedded in most organizations
• An understanding of how predictive models can improve your ability to make decisions in an uncertain world
• Prescriptive Analytics and how it helps to formulate recommendations of what you should do
• What is Data Management: Architecture, Quality and Privacy
• Master fundamental concepts and practices of the analytics life cycle and understand the best practices for each stage

Learning Content

• Welcome
• What is Analytics?
• Analytics Landscape
• Descriptive Analytics
• Predictive Analytics
• Prescriptive analytics
• Data Management
• Data-Analtyics Life cycle
• Course Wrap-up

Learning Outcomes

• Systems Engineering - This course provides a broad overview of how systems engineering helps you develop complex projects that meet program objectives in an efficient way.

Learning Content

• How system architectures are developed & described
• How to communicate the needs, requirements, & constraints
• How to optimize the design through trade studies, & how to know the system does what it’s supposed to in the end

Learning Outcomes

• Understand the concept and need for a circular economy
• Analyze circular business models
• Apply circular design and innovation principles
• Understand policies and networks supporting circularity
• Engage in building circular societies
• Create and implement ideas for circularity

Learning Content

• Module 1: Materials
  • Explores where materials come from
  • Builds a rationale for why society needs more circularity
• Module 2: Circular Business Models
  • In-depth exploration of circular business models
  • Discusses ways for businesses to create economic and social value
• Module 3: Circular Design, Innovation and Assessment
  • Presents topics like functional materials and eco-design
  • Methods to assess environmental impacts
• Module 4: Policies and Networks
  • Explores the role of governments and networks
  • Policies and sharing best practices to enable circularity
• Module 5: Circular Societies
  • Examines new norms and forms of engagement
  • Social systems and institutions for circularity