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

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

• Data Analyst (Entry-Level)
• Data Analytics Intern
• Business Intelligence Assistant
• Junior Reporting Analyst
• Marketing Data Coordinator
• Data Visualization Specialist
• SQL Reporting Assistant
• Data Quality Analyst
• Insights and Reporting Trainee
• Excel and Dashboard Developer
• ETL Support Technician
• Analytics Project Assistant
• Junior Research Analyst
• Operations Data Assistant
• AI Data Insights Intern
• Privacy Compliance Assistant (Analytics)
• Data Cleaning and Processing Technician
• Customer Data Analyst
• BI Support Technician
• Analytics QA Intern