The Pearson VUE Certified IT Specialist Data Analytics Prep course provides a comprehensive introduction to key data analytics concepts and techniques, preparing learners for certification and real-world data roles. Explore the basics of data types, structures, and categories, and learn how to import, clean, organize, and aggregate data for analysis. Develop an understanding of data analysis types, metrics, and exploratory methods, including how to interpret and explain insights effectively.

Gain hands-on skills in visualizing data through charts and graphs that highlight trends, relationships, comparisons, and distributions. Learn to communicate findings clearly and responsibly while understanding the role of AI in data analysis. The course also emphasizes ethical data practices, including privacy laws, bias detection, and responsible data handling. Ideal for aspiring data analysts, business intelligence professionals, and students pursuing the Pearson VUE Data Analytics certification.

Certificate

All participants will receive a Certificate of Completion from Tertiary Courses after achieved at least 75% attendance.

Funding and Grant

HRD Corp Claimable Course for Employers Registered with HRD Corp

Course Code: M1830

Topic 1 Data Basics

• Define the concept of data

• Describe basic data variable types

• Describe basic structures used in data analytics

• Describe data categories

Topic 2 Data Manipulation

• Import, store, and export data

• Clean data

• Organize data

• Aggregate data

Topic 3 Data Analysis

• Describe and differentiate between types of data analysis

• Describe and differentiate between data aggregation and interpretation metrics

• Describe and differentiate between exploratory data analysis methods

• Evaluate and explain the results of data analyses

• Define and describe the role of artificial intelligence in data analysis

Topic 4 Data Visualization and Communication

• Report data

• Create and derive conclusions from visualizations that compare one or more categories of data

• Create and derive conclusions from visualizations that show how individual parts make up the whole

• Create and derive conclusions from visualizations that analyze trends

• Create and derive conclusions from visualizations that determine the distribution of data

• Create and derive conclusions from visualizations that analyze the relationship between sets of values

Topic 5 Responsible Analytics Practice

• Describe data privacy laws and best practices

• Describe best practices for responsible data handling

• Given a scenario, describe the types of bias that affect the collection and interpretation of data