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DATA SCIENCE: TURNING DATA
INTO DECISIONS
• Exploring patterns,
predicting outcomes, and
empowering innovation.
WHAT IS DATA SCIENCE?
• • A multidisciplinary field combining statistics, computer
science, and domain expertise
• • Extracts knowledge and insights from structured and
unstructured data
THE DATA SCIENCE PROCESS
• 1. Define the problem
• 2. Collect data
• 3. Clean and preprocess data
• 4. Analyze and model
• 5. Interpret and communicate
results
KEY TOOLS & TECHNOLOGIES
• • Python, R, SQL
• • Jupyter, Tableau, Power BI
• • Machine Learning libraries (scikit-learn, TensorFlow)
APPLICATIONS OF DATA SCIENCE
• • Healthcare: Disease
prediction
• • Finance: Fraud detection
• • Retail: Recommendation
systems
• • Sports: Performance analytics
DATA SCIENCE VS. RELATED
FIELDS
• • Data Analyst: Descriptive insights
• • Data Engineer: Infrastructure & pipelines
• • Data Scientist: Predictive modeling & insights
CHALLENGES IN DATA SCIENCE
• • Data quality and availability
• • Model interpretability
• • Ethical and privacy concerns
FUTURE TRENDS
• AutoML
• Explainable AI (XAI)
• Real-time analytics
• AI governance and ethics
CAREERS IN DATA SCIENCE
• • Data Scientist
• • Machine Learning Engineer
• • Data Analyst
• • Business Intelligence Developer
THANK YOU / Q&A
• “Data is the new oil.”
• Let’s unlock its power together.

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Data Science for Innovation: From Raw Data to Real-World Insights & Solutions

  • 1. DATA SCIENCE: TURNING DATA INTO DECISIONS • Exploring patterns, predicting outcomes, and empowering innovation.
  • 2. WHAT IS DATA SCIENCE? • • A multidisciplinary field combining statistics, computer science, and domain expertise • • Extracts knowledge and insights from structured and unstructured data
  • 3. THE DATA SCIENCE PROCESS • 1. Define the problem • 2. Collect data • 3. Clean and preprocess data • 4. Analyze and model • 5. Interpret and communicate results
  • 4. KEY TOOLS & TECHNOLOGIES • • Python, R, SQL • • Jupyter, Tableau, Power BI • • Machine Learning libraries (scikit-learn, TensorFlow)
  • 5. APPLICATIONS OF DATA SCIENCE • • Healthcare: Disease prediction • • Finance: Fraud detection • • Retail: Recommendation systems • • Sports: Performance analytics
  • 6. DATA SCIENCE VS. RELATED FIELDS • • Data Analyst: Descriptive insights • • Data Engineer: Infrastructure & pipelines • • Data Scientist: Predictive modeling & insights
  • 7. CHALLENGES IN DATA SCIENCE • • Data quality and availability • • Model interpretability • • Ethical and privacy concerns
  • 8. FUTURE TRENDS • AutoML • Explainable AI (XAI) • Real-time analytics • AI governance and ethics
  • 9. CAREERS IN DATA SCIENCE • • Data Scientist • • Machine Learning Engineer • • Data Analyst • • Business Intelligence Developer
  • 10. THANK YOU / Q&A • “Data is the new oil.” • Let’s unlock its power together.