Learning Outcomes

• Define association rule mining
• Explain mining frequent patterns and rules
• Distinguish between clustering and classification
• Define the apriori algorithm
• List the four V’s of big data
• Explain why social media data can be hard to disambiguate
• Define K-means clustering
• Explain K-medoids
• Describe some solutions for the multi-arm bandit problem

Learning Content

• Module 1: Introduction to Associative Rule Mining
  • Learn about frequent patterns and rules in mining
  • Understand when association rules can be applied
• Module 2: Introduction to Big Data
  • Learn about the four V’s of big data
  • Understand why social media data can be hard to disambiguate
• Module 3: Introduction to Clustering Analysis
  • Understand why clustering is used
  • Learn about different types of clustering
• Module 4: Experimentation and Active Learning
  • Learn about online and offline contexts for creating data
  • Understand the n-arm bandit problem
• Module 5: Course Assessment

Learning Outcomes

• How to design brand-new processes
• The process and stages required to deliver effective processes
• Tools and techniques to support the design process
• How to gather requirements for a new process
• How to graphically design a new process, step by step
• How to define a new process clearly
• Techniques to ensure process goals are well-defined
• How to design processes that scale with business growth
• The design, testing, and completion elements of process design
• How to identify process control points, their impacts, and why they matter

Learning Content

• Introduction
• Fundamentals of process design
• Process requirements
• Design the process
• Finalize the process

Learning Outcomes

• Build valuable applied data storage, integration, and migration skills employers need.
• Gain hands-on experience using industry-specific data tools.
• Demonstrate you understand data-related best practices and can apply methodologies through industry-standard processes.
• Showcase your ability to solve problems related to data processes that you can talk about in interviews.

Learning Content

• Data Integration
  • Overview of data integration and implementation patterns
  • Data connectors and their necessity for effective integration
  • Data integration operations management (DIOM), security, architecture, and tools
• Data Storage
  • Overview of data storage and key concepts
  • File, block, object, and hybrid storage architectures
  • Cloud storage and its ubiquitous use across industries
  • Significance of backups and common backup techniques
• Data Migration and Final Project
  • Data migration concepts and related terminology
  • Migration architecture and processes
  • Physical location and cloud-to-cloud migration
  • Scenario-based project as a junior data migration specialist

Learning Outcomes

• Calculate the environmental impact of systems and products
• Assess impact across the entire product life cycle
• Utilize the Life Cycle Assessment (LCA) methodology
• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate

Learning Content

• Module 1: Product-Oriented Environmental Assessment: Introduction to LCA
• Module 2: LCA Goal Definition
• Module 3: Life Cycle Inventory Part 1
• Module 4: Life Cycle Inventory Part 2
• Module 5: Life Cycle Impact Assessment
• Module 6: LCA Interpretation

Learning Outcomes

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate

Learning Content

• Module 1: Basics of Environmental Science
• Module 2: Biodiversity, Systems, and Feedback
• Module 3: Global Cycles
• Module 4: Basics of Global Change

Learning Outcomes

• Advanced techniques in predictive analysis using artificial intelligence
• Implementation of algorithms like Random Forest, Adaboost Regressor, and Gaussian Mixture Model
• Handling class imbalance and optimizing models using Grid Search
• Detecting patterns with unsupervised learning techniques such as clustering and affinity propagation
• Utilizing classifiers like Logistic Regression, Naive Bayes, and Support Vector Machines for classification tasks
• Logic programming concepts and applications for problem-solving
• Heuristic search methods and their applications in solving complex problems
• Natural language processing techniques including tokenization, stemming, lemmatization, and named entity recognition
• Understanding and building context-free grammars, recursive descent parsing, and shift-reduce parsing
• Application of predictive analysis in various domains for making informed decisions and predictions

Learning Content

• Introduction
• Class Imbalance and Grid Search
• Adaboost Regressor
• Detecting Patterns with Unsupervised Learning
• Affinity Propagation Model
• Clustering Quality
• Gaussian Mixture Model
• Classifiers
• Logic Programming
• Heuristic Search
• Natural Language Processing

Learning Outcomes

• Learn new concepts from industry experts
• Gain a foundational understanding of a subject or tool
• Develop job-relevant skills with hands-on projects
• Earn a shareable career certificate from Google
• Define and explain key concepts involved in data analytics including data, data analysis, and data ecosystems
• Conduct an analytical thinking self-assessment giving specific examples of the application of analytical thinking
• Discuss the role of spreadsheets, query languages, and data visualization tools in data analytics
• Describe the role of a data analyst with specific reference to jobs

Learning Content

• Module 1: "Introducing Data Analytics and Analytical Thinking"
  • Learn how data analysts use tools and skills to inform decisions
  • Overview of the course and program expectations
• Module 2: "The Wonderful World of Data"
  • Learn about the data life cycle and data analysis process
  • Introduction to applications guiding data through analysis
• Module 3: "Set up your data analytics toolbox"
  • Learn concepts to use spreadsheets, query languages, and visualization tools
  • Understand how they work through practical examples
• Module 4: "Become a fair and impactful data professional"
  • Examine businesses and tasks analysts perform
  • Learn how the Google Data Analytics Certificate meets job requirements