Mastery Level: Mastering Data Science 

2. Certified Analytics Professional (CAP)

✅ Requirements:

• A combination of education and experience:

  • Bachelor’s degree + 5 years analytics experience or

  • Master’s degree + 3 years experience

• Endorsement from a supervisor or manager

• Adherence to a code of ethics

📘 Exam Focus:

• Seven domains of the analytics process:

  1. Business problem framing

  2. Analytics problem framing

  3. Data

  4. Methodology selection

  5. Model building

  6. Deployment

  7. Model lifecycle management

🧭 Positioning:

• Mid to senior-level professionals

• Validates applied analytics capability and professional experience

Course Summary
This advanced course is designed for those wanting to deepen their Data Science expertise. Over four weeks, participants will master techniques in deep learning, model deployment, and advanced analytics. By the end of this course, learners will be capable of implementing and managing full-scale data science projects in professional settings.

Course Outline

• Week 1: Deep Learning Fundamentals
  • Introduction to neural networks
  • Building deep learning models (TensorFlow, Keras)
  • Applications of deep learning (image, text, voice data)
• Week 2: Model Deployment and Optimization
  • Deploying models using cloud platforms (AWS, Google Cloud)
  • Real-time data processing and model monitoring
  • Optimizing models for performance and scalability
• Week 3: Advanced Predictive and Prescriptive Analytics
  • Prescriptive models for decision-making
  • Advanced time series analysis
  • Real-world case studies
• Week 4: Final Capstone Project
  • End-to-end project development and deployment
  • Presenting solutions to real business challenges
  • Feedback and review

Learning Objectives

• Master deep learning techniques and their applications
• Learn to deploy models in real-world settings
• Use advanced analytics to provide data-driven solutions
• Build and lead data science projects in professional environments

Who Should Attend

• Data scientists ready to advance to the expert level
• Professionals managing large-scale data projects
• Individuals wanting to master the end-to-end data science pipeline