Data Science Methodology (SKO 8108)

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Course

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Data Science Methodology (SKO 8108)

Data Science Methodology (SKO 8108)

Self-Paced

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Duration

10 hours
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Fee

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NOS 8108

Fast forward yourself and get in line with emerging data science methodologies that are in use and are making waves or rather predicting and determining which wave is coming and which one has just passed. This NASSCOM approved course will satisfy NOS 8108, PCs 1-8.

N8108

PC1. Identify the objective of the analysis

PC2. Establish the purpose, scope, and target audience to report the business outcomes

PC3. Define the delivery mode and format (such as excel sheets, reports, APIs) to report the business outcomes

PC4. Summarize the defined business outcomes into a narrative

PC5. Select suitable visualizations to represent the defined business outcomes

PC6. Present outcomes through selected visualizations using standard templates and agreed language standards

PC7. Publish visualizations for consumption across all agreed formats

Despite the recent increase in computing power and access to data over the last couple of decades, our ability to use the data within the decision making process is either lost or not maximized at all too often, we don't have a solid understanding of the questions being asked and how to apply the data correctly to the problem at hand. This course has one purpose, and that is to share a methodology that can be used within data science, to ensure that the data used in problem solving is relevant and properly manipulated to address the question at hand. Accordingly, in this course, you will learn:

  • The major steps involved in tackling a data science problem.
  • The major steps involved in practicing data science, from forming a concrete business or research problem, to collecting and analyzing data, to building a model, and understanding the feedback after model deployment.
  • How data scientists think!

    • Module 1: From Problem to Approach

    • Business Understanding
    • Analytic Approach

    Module 2: From Requirements to Collection

    • Data Requirements
    • Data Collection

    Module 3: From Understanding to Preparation

    • Data Understanding
    • Data Preparation

    Module 4: From Modeling to Evaluation

    • Modeling
    • Evaluation

    Module 5: From Deployment to Feedback

    • Deployment
    • Feedback

  • It is self-paced.
  • It can be taken at any time.

  • Passion for Data Science

    • Course Outline

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    Data Science Methodology (SKO 8108)

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