Best Data Science Course in Delhi - PowerPoint PPT Presentation

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Best Data Science Course in Delhi

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Embarking on a data science course involves delving into a comprehensive curriculum that covers fundamental theories, advanced techniques, and practical applications. It's not just about learning the theories; practical application plays a pivotal role in mastering this field. The course encompasses statistical analysis, machine learning, data visualization, and much more. – PowerPoint PPT presentation

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Title: Best Data Science Course in Delhi


1
Best Data Science
  • Course in Delhi

2
What is Data Science
  • Data science is a multidisciplinary field that
    combines statistical analysis, machine learning,
    programming, domain expertise, and data
    visualization to extract meaningful insights and
    knowledge from structured and unstructured data.
    It involves the use of various tools and
    techniques to gather, process, clean, analyze,
    and interpret data to solve complex problems and
    make informed decisions.
  • Data scientists use their skills in mathematics,
    statistics, and computer science to work with
    large volumes of data from different sources such
    as databases, social media, sensors, and more.
    They apply algorithms and models to uncover
    patterns, trends, correlations, and other
    valuable information that can help businesses,
    organizations, and industries improve processes,
    develop new products, enhance customer
    experiences, and optimize strategies.

3
Key components of data science
  • Statistical Analysis
  • Data Collection
  • Gathering data from various sources such as
    databases, APIs, web scraping, and IoT devices.
  • Data Cleaning and Preprocessing Removing noise,
    handling missing values, transforming data into a
    usable format, and preparing it for analysis.
  • Exploratory Data Analysis (EDA) Understanding
    the structure of the data, identifying patterns,
    correlations, and outliers through statistical
    and visual methods.
  • Applying statistical techniques to validate
    hypotheses, conduct significance tests, and
    derive insights from the data.
  • Machine Learning Using algorithms and models to
    build predictive and prescriptive analytics
    solutions, classification, regression,
    clustering, and recommendation systems.
  • Data Visualization Creating visual
    representations such as charts, graphs, and
    dashboards to communicate findings and insights
    effectively.

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