Africa College of Technology

CompTIA Data+

Categories: CompTIA, Data
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About Course

CompTIA Data+ is a certification designed for professionals in the early stages of their data analytics careers. It focuses on enhancing skills in developing and promoting data-driven decision-making in businesses. While Data+ is particularly beneficial for data-specific roles, professionals in non-IT fields can also gain value from its emphasis on analytics processes and data knowledge.

By obtaining CompTIA Data+, your team members gain the confidence to effectively utilize data analysis. With the increasing significance of data analytics, there is a growing demand for job roles that can provide context and effectively communicate essential business insights. The ability to collect, analyze, and report on data empowers organizations to prioritize goals and make informed business decisions.

What sets CompTIA Data+ apart is its comprehensive coverage of fundamental data analytics skills, hands-on assessment, and vendor neutrality. By being vendor neutral, this certification equips analysts with the necessary skills for diverse job roles, regardless of the specific programs and tools used. In contrast, vendor-specific certifications limit your team to working with a single platform, such as SAS, Tableau, or Microsoft.

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What Will You Learn?

  • This certification confirms that certified professionals possess the necessary abilities to facilitate data-driven decision-making in businesses. It demonstrates their knowledge and skills in transforming business requirements into data-driven decisions by effectively mining, manipulating, and analyzing complex data sets. Throughout the entire data lifecycle, they adhere to governance and quality standards.
  • Upon completing the CompTIA Data+ course, participants will acquire the following skills and knowledge:
  • - Recognize fundamental concepts of data schemas and differentiate between common data structures and file formats.
  • - Elucidate concepts related to data acquisition, including the reasons for data cleansing and profiling, and employ techniques for data manipulation.
  • - Apply appropriate descriptive statistical methods and summarize various types of analysis.
  • - Convert business requirements into suitable visualizations.
  • - Summarize key concepts of data governance and implement data quality control principles.

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