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DATA ANALYTICS ITS PEARSON CERTIFICATION

DATA ANALYTICS ITS PEARSON CERTIFICATION

GH₵3300

GH₵3500

Candidates for this exam are seeking to prove introductory knowledge of how to responsibly manipulate, analyze, and communicate findings of data analysis. Candidates should have at least 150 hours of instruction or hands-on experience with data manipulation, analysis, visualization, and communication. They should be familiar with general data concepts, data-related laws, and responsible analytics practices.

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Has discount
Expiry period 6 Months
Made in English
Last updated at Sun Oct 2025
Level
Advanced
Total lectures 17
Total quizzes 0
Total duration Hours
Total enrolment 1
Number of reviews 0
Avg rating
Short description Candidates for this exam are seeking to prove introductory knowledge of how to responsibly manipulate, analyze, and communicate findings of data analysis. Candidates should have at least 150 hours of instruction or hands-on experience with data manipulation, analysis, visualization, and communication. They should be familiar with general data concepts, data-related laws, and responsible analytics practices.
Outcomes
  • Data Concepts: Understanding data types, data structures, and the data ecosystem.
  • Analytical Thinking: Developing the ability to think analytically and apply data to solve problems.
  • Data Analytics Process: Learning the steps involved in a data analytics project, from data gathering to communicating findings.
  • Data Sourcing: Identifying and collecting data from various structured and unstructured sources.
  • Data Cleaning & Transformation: Skills for cleaning raw data, handling anomalies, and transforming it into a usable format.
  • Programming Languages: Proficiency in tools like Python (with libraries such as pandas and NumPy) or R for data manipulation and analysis.
  • SQL (Structured Query Language): Learning to work with relational databases and write queries to manage and retrieve data.
  • Tools: Using software like Tableau, Power BI, or built-in Excel features to create insightful charts and dashboards.
  • Techniques: Choosing the right visualizations to effectively communicate data insights and tell a story.
  • Descriptive Statistics: Understanding how to summarize and interpret data using basic statistical methods.
  • Inferential Statistics: Learning about parameter estimation, hypothesis testing, and A/B testing.
Requirements
  • Laptop or Desktop
  • Internet
  • Classroom

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