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CertPREP Courseware: IT Specialist Data Analytics Self-Paced (INF-202) 180-Day access

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Summary

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Description

This self-paced 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. 

Course components:

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:   18 hours of primary content. Each learner will learn at their own pace. 

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

Course objectives: 

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.

Lesson 1: Data Basics

  • Skill 1.1: Define the concept of data.
    • Define data and information.
    • Differentiate between data and information.
    • Define statistics and its relation to data.
  • Skill 1.2: Describe basic data variable types.
    • Define variables.
    • Identify different data types.
    • Define type checking.
  • Skill 1.3: Describe basic structures used in data analytics.
    • Define tables.
    • Define arrays.
    • Define lists.
  • Skill 1.4: Describe data categories.
    • Differentiate between structured and unstructured data.
    • Identify and use different types of data.
  • Summary
  • Labs
  • Quiz

 

Lesson 2: Data Manipulation.

  • Skill 2.1: Import, store, and export data.
    • Describe ETL processing.
    • Perform ETL with relational data.
    • Perform ETL with data stored in delimited files.
    • Perform ETL with data stored in XML files.
    • Perform ETL with data stored in JSON files.
  • Skill 2.2: Clean data.
    • Perform data cleaning common practices.
    • Perform truncation.
    • Describe data validation.
  • Skill 2.3: Organize data.
    • Describe data organization.
    • Perform sorting.
    • Perform filtering.
    • Perform appending and slicing.
    • Perform pivoting.
    • Perform transposition.
  • Skill 2.4: Aggregate data.
    • Describe the aggregation function.
    • Use aggregation functions like COUNT, SUM, MIN, MAX, and AVG in SQL.
    • Use GROUP BY and HAVING in SQL.
  • Summary
  • Labs
  • Quiz

Lesson 3: Data Analysis.

  • Skill 3.1: Describe and differentiate between types of data analysis.
    • Perform descriptive analysis.
    • Perform diagnostic analysis.
    • Perform predictive analysis.
    • Perform prescriptive analysis.
    • Perform hypothesis testing.
  • Skill 3.2: Describe and differentiate between data aggregation and interpretation metrics.
    • Define data aggregation and data interpretation.
    • Define data interpretation.
    • Describe data aggregation and interpretation metrics.
  • Skill 3.3: Describe and differentiate between exploratory data analysis methods.
    • Find relationships in a dataset.
    • Identify outliers in a dataset.
    • Drill a dataset.
    • Mine a dataset.
  • Skill 3.4: Evaluate and explain the results of data analyses.
    • Perform a simple linear regression.
    • Interpret the results of a simple linear regression.
    • Use regression analysis for prediction.
  • Skill 3.5: Define and describe the role of artificial intelligence in data analysis.
    • Define artificial intelligence, algorithm, machine learning, and deep learning.
    • Discuss how machine learning algorithms help in data analysis.
    • Discuss how artificial intelligence algorithms work in data analysis.
  • Summary
  • Labs
  • Quiz

 

Lesson 4: Data Visualization and Communication.

  • Skill 4.1: Report data.
    • Use tables and charts to display information.
    • Disaggregate data.
  • Skill 4.2a and 4.3a: Create and derive conclusions from visualizations that compare one or more categories of data.
    • Use different types of charts:
    • Column chart.
    • Bar chart.
  • Skill 4.2b and 4.3b: Create and derive conclusions from visualizations that show how individual parts make up the whole.
    • Differentiate between the following types of graphical representations:
      • Pie Chart.
      • Donut Chart.
    • Other variations on bar and column charts such as stacked bar and column charts.
  • Skill 4.2c and 4.3c: Create and derive conclusions from visualizations that analyze trends.
    • Use different types of visualization:
    • Line chart and variants of the line chart.
    • Waterfall chart.
    • Sankey Diagram.
  • Skill 4.2d and 4.3d: Create and derive conclusions from visualizations that determine the distribution of data.
    • Use different types of visualizations:
    • Box and Whisker plot.
  • Skill 4.2e and 4.3e: Create and derive conclusions from visualizations that analyze the relationship between sets of values.
    • Use different types of visualizations:
    • Scatter plot.
    • Bubble chart.
  • Summary
  • Labs
  • Quiz

Lesson 5: Responsible Analytics Practice.

  • Skill 5.1: Describe data privacy laws and best practices:
    • Describe the fair information practice principles.
    • Understand data privacy laws in the US.
    • Understand data privacy laws in Canada.
    • Understand data privacy laws in the EU.
  • Skill 5.2: Describe best practices for responsible data handling:
    • Handle PII, secure data, and protect anonymity within small datasets.
    • Balance the trade-off between interpretability and accuracy.
    • Generalize from a sample to a population.
  • Skill 5.3: Given a scenario, describe the types of bias that affect the collection and interpretation of data.
    • Explain and identify different types of bias that affect the gathering of data.
  • Summary
  • Labs
  • Quiz
COURSEWARE
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