Learning Outcomes
This module covers a range of topics and skills relating data analytics. The main learning outcomes for the module are;
- Understand and explain the purpose and outputs of data integration activities.
- Explain how and why data from multiple sources can be integrated to provide a unified view
- Understand and describe how programming languages for statistical computing (SQL) can be applied to data integration activities, improving speed and data quality for analysis.
- Describe how to evaluate and improve data quality when preparing data for analysis
- Describe big data, and explain the challenges associated with processing large data volumes, including how programming can assist with processing big data
- Be able to describe different testing methods and requirements to ensure that unified datasets are correct, complete and up to date.
- Explain the capabilities of the statistical language R and programming language, python when used to manipulate data and process data.
- Explain how statistical programming languages are used in preparing data for analysis and within analysis projects.
Curriculum
- 11 Sections
- 57 Lessons
- 10 Weeks
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- Process and Tools used for Data Integration 1A7
- Process and Tools used for Data Integration 1B6
- Process and Tools used for Data Integration 1C3
- Process and Tools used for Data Integration 1ABC Additional Resources6
- Process and Tools used for Data Integration Part 24
- Process and Tools used for Data Integration Part 2B6
- Process and Tools used for Data Integration Part 2C5
- Process and Tools used for Data Integration Part 2ABC Additional Resources7
- Industry Standard Tools and Methods for Data Analysis5
- Industry Standard Tools and Methods for Data Analysis - Part 24
- Industry Standard Tools and Methods for Data Analysis - Part 1, 2 Additional Resources7