Data Science Basic | Introduction to Data Science by Diljeet Singh | Learn Smarter
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Introduction to Data Science

Data science is a multidisciplinary field that combines mathematics, statistics, programming, and domain knowledge to extract meaningful insights from both structured and unstructured data. The role of a data scientist encompasses a wide range of tasks from data collection to model deployment, facilitating informed decision-making across various industries. The data science workflow involves several crucial phases including problem definition, data cleaning, exploratory analysis, modeling, and deployment.

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Sections

  • 1

    Introduction To Data Science

    This section provides an overview of data science, highlighting its core components and the role of data scientists.

    Learning Practice

  • 1.1

    Description

    Learning

  • 1.2

    Learning Objectives

    This section outlines the key learning objectives for understanding data science.

    Learning Practice

  • 1.1

    What Is Data Science?

    Data science is a multidisciplinary field that combines mathematics, statistics, programming, and domain knowledge to extract valuable insights from data.

    Learning Practice

  • 1.1.1

    Core Areas Of Data Science

    This section outlines the core areas of data science, emphasizing their importance in achieving the overall goals of data-driven projects.

    Learning Practice

  • 1.2

    Who Is A Data Scientist?

    This section defines the role and responsibilities of a data scientist in the context of data science.

    Learning Practice

  • 1.3

    Real-World Applications Of Data Science

    Data science significantly influences various industries and helps solve real-world problems.

    Learning Practice

  • 1.4

    The Data Science Lifecycle

    The Data Science Lifecycle outlines the stages involved in a data science project, from problem definition to monitoring and maintenance.

    Learning Practice

  • 1.4.1

    Problem Definition

    The Problem Definition stage is crucial in the data science lifecycle, as it identifies the specific business problem or research question that guides subsequent steps.

    Learning Practice

  • 1.4.2

    Data Collection

    This section discusses the importance of data collection within the data science workflow, highlighting various methods and sources.

    Learning Practice

  • 1.4.3

    Data Cleaning And Preprocessing

    Data cleaning and preprocessing involves correcting inaccuracies, managing missing values, and standardizing formats to prepare data for analysis.

    Learning Practice

  • 1.4.4

    Exploratory Data Analysis (Eda)

    EDA is a critical phase in the data science workflow that involves visualizing and understanding data distributions and relationships.

    Learning Practice

  • 1.4.5

    Modeling

    Modeling is a critical step in the data science lifecycle where predictive models are created using machine learning algorithms.

    Learning Practice

  • 1.4.6

    Evaluation

    This section focuses on the evaluation stage of the data science lifecycle, discussing key metrics and methods used to assess model performance.

    Learning Practice

  • 1.4.7

    Deployment

    The deployment phase of a data science project involves making predictive models available for users.

    Learning Practice

  • 1.4.8

    Monitoring And Maintenance

    This section emphasizes the significance of ongoing monitoring and maintenance of data science models to ensure their accuracy and performance.

    Learning Practice

  • 1.5

    Chapter Summary

    This section summarizes key concepts of data science, its lifecycle, and the role of a data scientist.

    Learning Practice

References

Chapter 1_ Introduction to Data Science.pdf

Class Notes

Memorization

What we have learnt

  • Data science combines vario...
  • The role of a data scientis...
  • Data science has impactful ...

Final Test

Revision Tests