Data Science

Data Science

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  1. Fundamental Concepts
    • Data Understanding and Preparation
      • Data Collection Methods
      • Data Cleaning and Preprocessing
      • Data Exploration and Visualization
    • Statistical Analysis
      • Descriptive Statistics
      • Inferential Statistics
      • Hypothesis Testing
  2. Data Manipulation and Analysis
    • Data Manipulation Tools
      • SQL for Data Querying
      • Pandas for Data Manipulation
    • Data Analysis Techniques
      • Exploratory Data Analysis (EDA)
      • Time Series Analysis
      • Dimensionality Reduction Techniques
  3. Data Visualization
    • Visualization Tools and Libraries
      • Matplotlib and Seaborn for Python
      • Tableau for Business Intelligence
    • Advanced Visualization Techniques
      • Interactive Visualizations with Plotly
      • Geospatial Data Visualization
  4. Machine Learning Integration
    • Supervised vs Unsupervised Learning
    • Model Selection and Evaluation
    • Feature Engineering
  5. Big Data Technologies
    • Introduction to Big Data Platforms
      • Apache Hadoop Ecosystem
      • Spark and its Ecosystem
    • Real-time Data Processing
      • Kafka for Stream Processing
      • Stream Analytics
  6. Ethics and Data Governance
    • Data Privacy and Security Measures
    • Ethical AI Practices
    • Data Governance Frameworks
  7. Advanced Topics and Trends
    • The Role of Data Science in AI
    • Emerging Technologies and Their Impact
    • Case Studies in Data Science Across Industries
Data Science
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Marva

I share my insights and experiences on how to be a thriving software developer while still leading a fulfilling life.

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