Unit 4: Data and Analysis-SLO

 

4.1 Scope of Data Science

• 4.1.1 Explain the following key concepts of data science:

  • a. data science

  • b. data and dataset

  • c. data analysis

  • d. statistics and probability

  • e. mathematics

  • f. machine learning

  • g. deep learning

  • h. data mining

  • i. data visualization

  • j. big data

  • k. predictive model

  • l. natural language processing (NLP)

  • m. image processing

• 4.1.2 Discuss the scope and application of data science.

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4.2 Data Types, Data Collection, and Data Storage

Student Learning Outcomes Students should be able to:

• 4.2.1 Explain the concept of data and its types (qualitative and quantitative) emphasizing their characteristics and importance.

• 4.2.2 Evaluate the process of data collection using websites, sensors, and surveys, highlighting its significance and ethical considerations through real-world examples.

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4.3 Big Data and Applications of Big Data in Real World Business

Student Learning Outcomes Students should be able to:

• 4.3.1 Describe the following concepts of big data within the context of technology and society:

  • a. big data

  • b. 3 Vs of big data

  • c. big data analytics

  • d. data visualization and interpretation

• 4.3.2 Describe big data challenges in business.

• 4.3.3 Explain the application of big data in the following business domains:

• a. healthcare

• b. internet of things (IoT)

• c. manufacturing

• d. government

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