IT Specialist Data Analytics Self Paced (INF-202)

Course Summary

This IT Specialist Data Analytics course provides a gentle introduction to the responsible collection and reporting of data, the concepts of data manipulation, data analytics, prediction from data, and data visualization. It aims to provide learners with an understanding of the fundamentals of data and to equip them with the skills necessary to manipulate, analyze, and visualize data using various information and communications technology tools. This course consists of lessons accompanied by videos to help learners achieve their learning goals. Upon completing this course, learners should be able to explain basic statistical terminology and data analytics concepts, manipulate simple data sets, make simple predictions from data, and explain insights from data using meaningful and appealing visualization. Overall, this course covers the entire data analysis process, from understanding the basic principles to reporting the results of data analysis. The knowledge and skills garnered by learners during the course will be assessed through case studies, lab assignments, and quizzes.

Audience:

This course is designed to equip learners — interns, apprentices, and entry-level data analysts with the foundational knowledge and skills necessary to perform entry-level data manipulation, analysis, visualization, and communication. With the Data Analytics certificate, you could be considered for positions such as entry-level data analysts or researchers, data analytics apprentices or interns, operations research interns, market researchers, and business analysts.

Methodology:

180-day access to:
  • Lessons
  • Video learning
  • MeasureUp Practice Test for IT Specialist INF-202. Practice Mode with remediation and Certification mode to simulate the test day experience.

Duration:

3-4 Days / 18 h learner will learn at their own pours of primary content. Eachace.

Required course materials:

Self-paced Pearson CertPREP IT Specialist Data Analytics (INF-202) courseware with 180-day access.

Course Outcome :

Upon successful completion of this course, students should be able to:
  • Explain the basics of data.
  • Manipulate data.
  • Analyze data.
  • Create and use visualization from data to explain insights.
  • Explain data privacy laws and best practices for responsible data handling.

Course Outline:

Lesson 1: Data Basics
  • Skill 1.1: Define the concept of data.
  • Skill 1.2: Describe basic data variable types.
  • Skill 1.3: Describe basic structures used in data analytics.
  • Skill 1.4: Describe data categories.
  • Summary
  • Labs
  • Quiz
Lesson 2: Data Manipulation.
  • Skill 2.1: Import, store, and export data.
  • Skill 2.2: Clean data.
  • Skill 2.3: Organize data.
  • Skill 2.4: Aggregate data.
  • Summary
  • Labs
  • Quiz
Lesson 3: Data Analysis.
  • Skill 3.1: Describe and differentiate between types of data analysis.
  • Skill 3.2: Describe and differentiate between data aggregation and interpretation metrics.
  • Skill 3.3: Describe and differentiate between exploratory data analysis methods.
  • Skill 3.4: Evaluate and explain the results of data analyses.
  • Skill 3.5: Define and describe the role of artificial intelligence in data analysis.
  • Summary
  • Labs
  • Quiz
Lesson 4: Data Visualization and Communication.
  • Skill 4.1: Report data.
  • Skill 4.2a and 4.3a: Create and derive conclusions from visualizations that compare one or more categories of data.
  • Skill 4.2b and 4.3b: Create and derive conclusions from visualizations that show how individual parts make up the whole
  • Skill 4.2c and 4.3c: Create and derive conclusions from visualizations that analyze trends.
  • Skill 4.2d and 4.3d: Create and derive conclusions from visualizations that determine the distribution of data.
  • Skill 4.2e and 4.3e: Create and derive conclusions from visualizations that analyze the relationship between sets of values.
  • Summary
  • Labs
  • Quiz
Lesson 5: Responsible Analytics Practice.
  • Skill 5.1: Describe data privacy laws and best practices:
  • Skill 5.2: Describe best practices for responsible data handling:
  • Skill 5.3: Given a scenario, describe the types of bias that affect the collection and interpretation of data.
  • Summary
  • Labs
  • Quiz