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Introduction to Data Science


Introduction to Data Science (Brief Overview)

Definition:

Data Science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Key Components of Data Science:

  1. Data Collection
    • Gathering raw data from various sources like databases, sensors, web scraping, surveys, etc.
  2. Data Cleaning & Preprocessing
    • Removing noise, handling missing values, and converting raw data into a usable format.
  3. Exploratory Data Analysis (EDA)
    • Using statistics and visualization to understand patterns, trends, and anomalies in data.
  4. Statistical Analysis & Machine Learning
    • Applying models and algorithms to predict outcomes or classify data.
  5. Data Visualization
    • Creating visual representations (charts, graphs) to communicate findings clearly.
  6. Interpretation & Decision Making
    • Drawing conclusions from data and supporting business or scientific decisions.

Common Tools & Technologies:

  • Programming: Python, R
  • Libraries: Pandas, NumPy, Scikit-learn, Matplotlib
  • Platforms: Jupyter Notebooks, Google Colab
  • Data handling: SQL, Excel

Applications of Data Science:

  • Healthcare (disease prediction)
  • Finance (fraud detection)
  • Marketing (customer segmentation)
  • Sports (performance analytics)
  • Social Media (trend analysis)

Let me know if you want this in a specific format (e.g., syllabus, slides, PDF content, in Telugu, etc.) or if you're referring to a curriculum or course content by a particular board or institute.

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