CBSE Class 10th AI (Artificial Intelleigence) | 12. Introduction to Data Science by Abraham | Learn Smarter
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Knowledge
12. Introduction to Data Science

Data science is a pivotal field that combines statistics, computer science, and domain knowledge to glean insights from data. The data science lifecycle guides the process from problem definition to model monitoring. Various tools and applications span multiple industries, emphasizing the significance of ethical considerations in data handling.

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Sections

  • 12

    Introduction To Data Science

    Data Science transforms raw data into valuable insights by employing statistical and computational techniques.

    Learning Practice

  • 12.1

    What Is Data Science?

    Data Science is the interdisciplinary field focused on extracting insights from structured and unstructured data using statistical and computational methods.

    Learning Practice

  • 12.2

    Importance Of Data Science

    Data science is crucial for informed decision-making and advancements across multiple fields.

    Learning Practice

  • 12.3

    Lifecycle Of Data Science

    The Data Science Lifecycle outlines the structured approach taken in a data science project, spanning eight key steps from problem definition to model maintenance.

    Learning Practice

  • 12.3.1

    Problem Definition

    Problem definition is the initial step in the data science lifecycle, focusing on clearly identifying the problem to be solved.

    Learning Practice

  • 12.3.2

    Data Collection

    Data Collection is the process of gathering information from various sources to address a specific problem or question in data science.

    Learning Practice

  • 12.3.3

    Data Cleaning And Preparation

    Data cleaning and preparation is the process of removing errors, handling missing values, and transforming raw data into a usable format for analysis.

    Learning Practice

  • 12.3.4

    Data Analysis And Exploration

    Data analysis and exploration involve identifying patterns, trends, and correlations in data using various statistical techniques and visualizations.

    Learning Practice

  • 12.3.5

    Model Building

    Model building is a critical step in the data science lifecycle where machine learning algorithms are applied to create predictive models.

    Learning Practice

  • 12.3.6

    Evaluation

    This section discusses the evaluation step of the Data Science Lifecycle, emphasizing the testing of models to ensure they effectively solve defined problems.

    Learning Practice

  • 12.3.7

    Deployment

    Deployment is the final step in the Data Science Lifecycle where the developed model is made available for real-world use.

    Learning Practice

  • 12.3.8

    Monitoring And Maintenance

    This section emphasizes the importance of monitoring and maintaining data science models to ensure their ongoing effectiveness in real-world applications.

    Learning Practice

  • 12.4

    Key Terms In Data Science

    This section defines key terms essential for understanding the field of data science.

    Learning Practice

  • 12.5

    Tools Used In Data Science

    This section discusses the essential tools and technologies utilized in data science, including programming languages, libraries, and software platforms.

    Learning Practice

  • 12.5.1

    Programming Languages

    Programming languages are essential tools in data science, facilitating data processing and analysis.

    Learning Practice

  • 12.5.2

    Libraries

    This section discusses key libraries used in data science for various functions including data manipulation, visualization, and machine learning.

    Learning Practice

  • 12.5.3

    Software And Platforms

    This section discusses the various software and platforms commonly used in data science.

    Learning Practice

  • 12.6

    Applications Of Data Science

    Data science is applied across various industries to extract meaningful insights and enhance decision-making.

    Learning Practice

  • 12.7

    Careers In Data Science

    This section explores various career paths available in the field of data science, highlighting the roles and responsibilities associated with each position.

    Learning Practice

  • 12.8

    Ethics In Data Science

    Ethics in data science encompasses key considerations like data privacy, bias, transparency, and accountability for the responsible use of data.

    Learning Practice

References

Chapter_12_Intro.pdf

Class Notes

Memorization

What we have learnt

  • Data Science transforms raw...
  • The Data Science Lifecycle ...
  • Python, R, and specific lib...

Final Test

Revision Tests