AI-Driven Recycling Specialist

This role focuses on developing and implementing AI and machine learning models to optimize battery disassembly, recycling processes, and state-of-health certification for end-of-life Lithium-ion batteries. Additionally, it involves integrating Digital Product Passports and asset administration shells to enhance traceability, compliance, and efficiency in battery lifecycle management.

Machine Learning and AI Expertise
Proficiency in developing and implementing machine learning models and AI algorithms, particularly for optimization and decision support.

Using Artificial Intelligence in Strategic Analysis and Decision-Making Processes

Target Group
Mid Level Employee

Level
Foundations

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Data Analysis
Strong skills in data gathering, preprocessing, and analysis to handle data from online monitoring and deep testing of LIB cells.

Data Analytics - Mining and Analysis of Big Data

Target Group
Junior (Fresh Employee)

Level
Foundations

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AI-Driven Recycling Specialist Using Artificial Intelligence in Strategic Analysis and Decision-Making Processes

Decision making in projects is a complex and dynamic process that depends on various project parameters and the project environment. Since each project is unique and has specific factors, a thorough analysis of the project context is necessary to develop an objective and reliable decision-making model. Complex projects often hold strategic importance for stakeholders in terms of their business strategy, brand image, and financial and economic opportunities. While front-end planning is essential, not all events and scenarios can be foreseen, especially for long-term projects involving multiple stakeholders. Therefore, strategic analysis is critical in the early phases of the project to identify and analyze major opportunity and risk factors for making informed strategic decisions. However, in the early phases the amount of information available is limited, and predictive tools may be necessary to forecast the project's future.

Provider
Pmi.org
Target
  • Professionals involved in project management, decision-making, and strategic analysis
  • Fresh Learners in integrating AI into their processes
Sector
  • Project management
  • Strategic decision-making
  • Artificial intelligence applications in project contexts
Area
  • Technology
  • Consulting
  • Construction
  • Finance
Method
Online
Certification
Yes
Duration
9:00 a.m – 5:00 p.m
Assessment
No
Cost
  • Member Price: US$881.00 (US$837.00 before 21 July)
  • Nonmember Price: US$1326.00 (US$1260.00 before 21 July)
  • Government Price: US$797.00
  • Student Price: US$383.00

Learning Outcomes

  • Understand the basics of artificial intelligence and its applications in strategic analysis and decision making.
  • Evaluate the potential benefits and risks associated with using artificial intelligence in strategic analysis and decision making.
  • Develop strategies and plans for integrating artificial intelligence into existing decision-making processes.

Learning Content

  • Introduction to Artificial Intelligence (AI)
    • Definition and history of AI
    • Applications of AI in various industries
    • AI tools and techniques for strategic analysis and decision-making
  • Benefits and Risks of AI in Strategic Analysis and Decision-Making
    • Potential benefits of using AI for decision-making
    • Risks and limitations of AI in decision-making
    • Ethical and legal considerations
  • Types of AI Tools and Techniques
    • Predictive analytics and modeling
    • Neural networks and deep learning
    • Visualization tools and techniques
  • Real-World Examples of AI in Strategic Analysis and Decision-Making
    • Case studies from various industries, such as finance, healthcare, and marketing
    • Success stories and best practices for implementing AI in decision-making
  • Integrating AI Into Existing Decision-Making Processes
    • Strategies for incorporating AI into current processes
    • Planning for the implementation of AI
    • Change management and training for stakeholders
  • Evaluating and Communicating Results
    • Analyzing and interpreting the results of AI-based analysis and decision-making
    • Communicating findings and recommendations to stakeholders
    • Evaluating the effectiveness of AI-based decision-making
  • Future Trends and Directions in AI for Strategic Analysis and Decision-Making
    • Emerging AI tools and techniques
    • Potential future applications of AI in decision-making


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AI-Driven Recycling Specialist Data Analytics - Mining and Analysis of Big Data

In this online data analysis course "Data Analytics - Mining and Analysis of Big Data," you will be introduced to the concept of big data and a number of techniques that are used to analyze and interpret it. The course starts by introducing you to big data and listing the four V’s of big data. You will learn about associative rule mining, when association can be applied, and the patterns that arise in mining. In the second module, you will explore clustering analysis, examining the difference between clustering and classification, and the various types of clustering, including K-means clustering and K-medoids. The final module focuses on online and active learning, experimentation, and the contexts of creating data online and offline. You will also delve into the n-arm bandit problem and discover solutions for the multi-arm bandit problem. This free online course is perfect for professionals in data science and analysis and learners eager to master big data mining and clustering techniques.

Provider
alison.com
Target
  • Data science and data analysis professionals
  • Learners interested in big data analysis
Sector
  • Data analytics
  • Mining and analysis of big data
Area
  • Technology
  • Data science and analytics
Method
Online
Certification
CPD Accreditation
Duration
1.5-3 Hours
Assessment
Final Assessment
Cost
Free

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


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