UNIT I- Introduction- data science key components, features

UNIT I- Introduction- data science key components, features

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  UNIT I Introduction toData Science
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  Introduction to DataScience • What is Data Science? Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines concepts from statistics, computer science, mathematics, and domain knowledge to interpret and solve real-world problems using data.
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  What is DataScience?
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  Introduction to DataScience • Key Components of Data Science: Data Collection: Gathering data from various sources (databases, sensors, web, etc.) Data Cleaning & Preparation: Handling missing values, outliers, and formatting the data. Exploratory Data Analysis (EDA): Understanding data patterns using statistics and visualizations. Modeling & Algorithms: Building predictive or descriptive models using machine learning. Interpretation & Communication: Explaining findings using storytelling, dashboards, or reports. Deployment: Integrating the model into real-world systems or business workflows.
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  Data Collection • Whatit is: The process of gathering data from various sources such as databases, APIs, sensors, web scraping, or surveys. • Goal: To collect relevant and high-quality data needed to solve a problem or answer a question. • Examples: Transaction logs, social media data, sensor data from IoT devices, stock market feeds.
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  Data Cleaning &Preparation (also known as Data Wrangling) • What it is: Transforming raw data into a usable format by removing errors, handling missing values, and standardizing formats. • Goal: Ensure the data is accurate, complete, and ready for analysis or modeling. • Tasks Involved: • Removing duplicates • Filling or dropping missing values • Correcting data types • Normalizing values • Tools: Python (pandas), R, Excel, OpenRefine
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  Exploratory Data Analysis(EDA) • What it is: Analyzing and visualizing data to discover patterns, trends, correlations, and outliers. • Goal: Gain insights and inform further data processing or modeling steps. • Techniques: • Summary statistics (mean, median, variance) • Visualization (bar charts, histograms, scatter plots) • Tools: Python (matplotlib, seaborn), R (ggplot2), Tableau, Power BI
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  Modeling & Algorithms •What it is: Using statistical models or machine learning algorithms to find patterns or make predictions. • Goal: Build models that can solve specific tasks such as classification, regression, clustering, etc. • Common Algorithms: • Linear regression, Decision trees • K-means clustering, Neural networks • Tools: Python (scikit-learn, TensorFlow), R, Weka
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  Interpretation & Communication •What it is: Translating complex model outputs into understandable insights for stakeholders. • Goal: Make data-driven decisions through clear communication (reports, dashboards, storytelling). • Includes: • Creating visualizations • Writing summary reports • Explaining model performance (accuracy, precision, recall) • Tools: PowerPoint, Tableau, matplotlib, dashboards (e.g., Streamlit, Dash)
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  Deployment • What itis: Integrating the developed model into a production environment where it can be used by end-users or systems. • Goal: Operationalize the model to make real-time or automated decisions. • Steps Involved: • Model versioning and testing • API development and deployment (e.g., Flask, FastAPI) • Monitoring and maintenance
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  Why is DataScience Important? • Helps organizations make data-driven decisions • Powers personalized recommendations (e.g., Netflix, Amazon) • Improves healthcare diagnoses, fraud detection, financial forecasting, etc. • Aids governments in creating effective policies using citizen and economic data
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  Real-World Examples: • Healthcare:Predicting patient readmission rates • Retail: Customer segmentation and demand forecasting • Banking: Credit scoring and fraud detection • Transport: Optimizing delivery routes using GPS data