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  2. PEARSON
  3. Information Technology 2016/2017 NQF
  4. Unit 5 - Data Modelling

Unit 5 Data Modelling

PEARSON (PEARSON)

Here are the best resources to pass Unit 5 Data Modelling. Find Unit 5 Data Modelling study guides, notes, assignments, and much more.

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Unit 5: Data Modelling Assignment A

  • Unit 5: Data Modelling Assignment A

  • Other • 3 pages • 2023
  • A distinction work for your unit 5 assignment A
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Model + Work : unit 5

  • Model + Work : unit 5

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    • Model work and actual work altogether for assignment BC - Unit 4
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Model Design For Assignment BC

  • Model Design For Assignment BC

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    • The model design for the assignment BC
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Test Plan For Unit 5 Assignment BC

  • Test Plan For Unit 5 Assignment BC

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    • Test plan for Unit 5: Data modelling Assignment BC
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Unit 5: Data modelling Assignment BC

  • Unit 5: Data modelling Assignment BC

  • Other • 25 pages • 2023
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    • A Distinction work for Unit 5: Data Modelling In BTEC IT Level 3.
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Level 3 IT Unit-5 Data Modelling Learning aim A (Distinction level work)

  • Level 3 IT Unit-5 Data Modelling Learning aim A (Distinction level work)

  • Summary • 45 pages • 2023
  • Distinction level work for the level 3 IT Unit 5 Spreadsheet/ Data Modelling unit part A. Most of the content is in the notes section of each slide.
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Unit 5 Data Modelling Assignment 1 and 2 all criteria met (Distinction)

  • Unit 5 Data Modelling Assignment 1 and 2 all criteria met (Distinction)

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  • Unit 5 Data Modelling Assignment 1 and 2 all criteria met (Distinction)
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Unit 5 Data Modelling assignment 2 (Distinction)

  • Unit 5 Data Modelling assignment 2 (Distinction)

  • Presentation • 21 pages • 2023
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    • Unit 5 Data Modelling assignment 2 (Distinction)
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Essay Unit 5 - Data Modelling

  • Essay Unit 5 - Data Modelling

  • Essay • 18 pages • 2023
  • A+ data programming assignment involves working with large datasets to extract insights using programming languages like Python, R or SQL in a specific domain like healthcare, finance or social media. You will need to use data analysis techniques, such as data cleaning, data transformation, data visualization, and statistical analysis, to extract insights from the data. It requires writing clean, efficient, and well-documented code.
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