Level 4 Diploma in Data Analytics and Visualization
Level 4 Diploma in Data Analytics and Visualization
- Gain proficiency in data analysis techniques and tools.
- Learn to create impactful visualizations for clear communication of data insights.
- Develop critical thinking and problem-solving skills using data-driven approaches.
- Master tools like Excel, Tableau, Power BI, and Python for data analysis and visualization.
- Understand the fundamentals of data analytics and its applications.
- Perform data cleaning, transformation, and analysis using advanced tools.
- Design and create interactive dashboards and reports.
- Interpret data insights to support business decisions.
- Develop the ability to communicate complex data in a simplified and visual format.
- Introduction to Data Analytics
- Overview of data types and sources.
- The role of data analytics in modern business.
- Data Preparation and Cleaning
- Techniques for cleaning and transforming raw data.
- Managing missing values and ensuring data quality.
- Statistical Methods for Data Analysis
- Descriptive and inferential statistics.
- Using Python and Excel for statistical analysis.
- Data Visualization Techniques
- Principles of effective data visualization.
- Tools like Tableau, Power BI, and Python libraries (Matplotlib, Seaborn).
- Interactive Dashboards and Storytelling
- Creating dynamic dashboards with Power BI and Tableau.
- Communicating data insights through storytelling.
- Advanced Topics in Data Analytics
- Predictive analytics and machine learning basics.
- Case studies on real-world data applications.
- Advance to the Level 5 Diploma in Advanced Data Analytics and Predictive Modeling.
- Pursue roles such as Data Analyst, Business Intelligence Analyst, or Visualization Specialist.
- Lay the foundation for certifications like Microsoft Power BI Data Analyst or Tableau Desktop Specialist.
- Practical Training: Focus on hands-on projects and real-world data.
- Expert Mentors: Learn from experienced data professionals.
- Career-Oriented Curriculum: Designed to align with industry demands.
- Globally Recognized Certification: Enhance your employability and career growth.
Study Units
- Introduction to Data Analytics
- Overview of data types and sources.
- The role of data analytics in modern business.
- Data Preparation and Cleaning
- Techniques for cleaning and transforming raw data.
- Managing missing values and ensuring data quality.
- Statistical Methods for Data Analysis
- Descriptive and inferential statistics.
- Using Python and Excel for statistical analysis.
- Data Visualization Techniques
- Principles of effective data visualization.
- Tools like Tableau, Power BI, and Python libraries (Matplotlib, Seaborn).
- Interactive Dashboards and Storytelling
- Creating dynamic dashboards with Power BI and Tableau.
- Communicating data insights through storytelling.
- Advanced Topics in Data Analytics
- Predictive analytics and machine learning basics.
- Case studies on real-world data applications.
By completing this course, learners will:
- Understand the fundamentals of data analytics and its applications.
- Perform data cleaning, transformation, and analysis using advanced tools.
- Design and create interactive dashboards and reports.
- Interpret data insights to support business decisions.
- Develop the ability to communicate complex data in a simplified and visual format.
The Level 4 Diploma in Data Analytics and Visualization is perfect for:
Aspiring Data Analysts and BI Professionals
Individuals aiming to build a career in data analysis, business intelligence, or data-driven decision-making.
Working Professionals and Managers
Business professionals and team leaders who want to leverage data to improve processes, strategies, and outcomes.
Graduates and IT Enthusiasts
Those with a background in IT, business, or related fields seeking to add highly in-demand data skills to their portfolio.
Entrepreneurs and Business Owners
Individuals looking to use data analytics to drive business growth, customer insights, and market strategies.
Career Switchers
Professionals from non-technical backgrounds who are passionate about entering the data analytics industry.
Our assessment process is designed to ensure every learner achieves the required level of knowledge, skills, and understanding outlined in each course unit.
Purpose of Assessment
Assessment helps measure how well a learner has met the learning outcomes. It ensures consistency, quality, and fairness across all learners.
What Learners Need to Do
Learners must provide clear evidence that shows they have met all the learning outcomes and assessment criteria for each unit. This evidence can take different forms depending on the course and type of learning.
Types of Acceptable Evidence
Assignments, reports, or projects
Worksheets or written tasks
Portfolios of practical work
Answers to oral or written questions
Test or exam papers
Understanding the Structure
Learning outcomes explain what learners should know, understand, or be able to do.
Assessment criteria set the standard learners must meet to achieve each learning outcome.
Assessment Guidelines
All assessment must be authentic, current, and relevant to the unit.
Evidence must match each assessment criterion clearly.
Plagiarism or copied work is not accepted.
All learners must complete assessments within the given timelines.
Where applicable, assessments may be reviewed or verified by internal or external quality assurers.
Full learning outcomes and assessment criteria for each qualification are available from page 8 of the course handbook.
