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Data Science & Machine Learning

Data Science & Machine Learning

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Data Science & Machine Learning Exam

Learning Data Science and Machine Learning means mastering how to transform raw data into meaningful insights and using intelligent systems to automate decisions. Data science helps you explore, visualize, and understand information, while machine learning builds predictive models that adapt and improve with more data. This synergy allows us to handle everything from fraud detection in banking to personalized medicine in healthcare.
In simple terms, data science is the detective that investigates the data, and machine learning is the smart assistant that learns and acts based on that information. Together, they empower innovation and shape the future of technology and decision-making across multiple industries.


Who should take the Exam?

This exam is ideal for:

  • Data Analysts
  • Machine Learning Engineers
  • Data Scientists
  • Business Intelligence Professionals
  • AI/ML Researchers
  • Software Developers exploring AI
  • Students aspiring to enter data-related careers

Skills Required

  • Analytical and problem-solving mindset
  • Python programming knowledge 
  • Statistics and mathematics
  • Interest in automation and AI technologies

Course Outline

  • Domain 1 - Introduction to Data Science
  • Domain 2 - Data Handling & Preparation
  • Domain 3 - Exploratory Data Analysis (EDA)
  • Domain 4 - Machine Learning Fundamentals
  • Domain 5 - Supervised Learning Models
  • Domain 6 - Unsupervised Learning Models
  • Domain 7 - Model Deployment & Real-World Use
  • Domain 8 - Advanced Topics (Introductory Level)
     

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Data Science & Machine Learning FAQs

Finance, healthcare, retail, IT, education, logistics, and more.

Python, Pandas, NumPy, Scikit-learn, and visualization libraries like Matplotlib.

Analysts, developers, students, and anyone interested in data-driven decision-making.

Data science focuses on analyzing and understanding data, while machine learning builds models that learn and predict.

Basic Python knowledge is helpful, but you’ll also learn through guided examples.

Customer predictions, fraud detection, product recommendations, and healthcare diagnostics.

It combines theory, practical projects, and business applications.

It introduces basics of deep learning and neural networks but focuses on practical ML models.

A basic understanding of statistics and linear algebra is useful.

Yes, it equips you with skills in high demand across industries.