The LICQual Level 4 Diploma in Data and Al – Data Analyst is an industry-recognized qualification designed for learners who want to build a strong foundation in data analytics while gaining the skills to work with artificial intelligence tools. As businesses increasingly rely on data-driven decisions, the demand for skilled data analysts with knowledge of AI continues to grow, making this program the perfect stepping stone for career advancement.
This Level 4 Diploma in Data Analyst focuses on practical and professional skills needed in today’s digital economy. Learners will develop expertise in collecting, cleaning, analyzing, and interpreting data to support business strategies. The course also integrates AI-driven techniques, enabling learners to understand predictive modeling, automation, and machine learning applications within data analysis. This unique blend of data analytics and AI makes graduates stand out in competitive job markets.
By choosing the Data Analyst Diploma Level 4, students gain access to a curriculum that combines theoretical knowledge with real-world case studies. From data visualization and database management to AI-assisted analytics, the program equips learners with the technical and analytical mindset employers are seeking. The focus on problem-solving and practical tools ensures that learners not only understand concepts but can apply them effectively in professional settings.
The Diploma in Data Analytics and AI is suitable for aspiring data analysts, IT professionals, business managers, or graduates looking to enter the field of data science. It is also ideal for those already working in data-related roles who want to advance into AI-driven analytics. Completion of this qualification opens opportunities in fields such as business intelligence, data science, market research, and AI-enhanced data analysis.
If you are aiming to future-proof your career and build globally relevant expertise, the LICQual Level 4 Diploma in Data and AI – Data Analyst provides the perfect combination of skills and knowledge. With a strong focus on employability, compliance with international standards, and hands-on learning, this diploma empowers you to excel as a professional data analyst in the modern digital world.
Course Overview
Qualification Title
LICQual Level 4 Diploma in Data and Al – Data Analyst
Total Units
6
Total Credits
60
GLH
300
Qualification #
LICQ2200565
Qualification Specification
To enroll in the LICQual Level 4 Diploma in Data and Al – Data Analyst, applicants must meet the following criteria:
|
Qualification# |
Unit Title |
Credits |
GLH |
|---|---|---|---|
|
LICQ2200565-1 |
Data Analysis Principles and the Role of a Data Analyst |
10 |
50 |
|
LICQ2200565-2 |
Advanced Data Collection, Preparation, and Cleaning Techniques |
10 |
50 |
|
LICQ2200565-3 |
Statistical Analysis and Data Modelling for Business Insights |
10 |
50 |
|
LICQ2200565-4 |
Data Tools and Programming for Analysts (e.g., Excel, SQL, Python, or R) |
10 |
50 |
|
LICQ2200565-5 |
Data Visualization, Dashboard Design, and Communication of Insights |
10 |
50 |
|
LICQ2200565-6 |
Data Governance, Ethics, and Compliance in Analytics Projects |
10 |
50 |
By the end of this course, learners will be able to:
Data Analysis Principles and the Role of a Data Analyst
- Explain advanced data analysis principles and their application in generating actionable business insights.
- Analyze the responsibilities of a data analyst in interpreting data, supporting decision-making, and driving organizational strategy.
- Apply data analysis frameworks to identify business problems, formulate hypotheses, and propose data-driven solutions.
- Evaluate the impact of a data analyst’s role on organizational performance and strategic outcomes.
Advanced Data Collection, Preparation, and Cleaning Techniques
- Implement advanced data collection methods, including APIs, web scraping, and automated data feeds, to gather comprehensive datasets.
- Apply sophisticated data preparation techniques, such as normalization and transformation, to ensure data quality and usability.
- Utilize advanced data cleaning methods to address missing values, outliers, and inconsistencies in complex datasets.
- Assess the effectiveness of data collection and cleaning processes in preparing high-quality data for analysis.
Statistical Analysis and Data Modelling for Business Insights
- Apply statistical analysis techniques, such as regression, hypothesis testing, and correlation, to derive meaningful business insights.
- Develop data models, including predictive and descriptive models, to support strategic decision-making.
- Analyze the accuracy and reliability of statistical models in addressing specific business challenges.
- Evaluate the impact of data modeling outcomes on business strategies and operational efficiency.
Data Tools and Programming for Analysts (e.g., Excel, SQL, Python, or R)
- Utilize advanced features of data analysis tools, such as Excel, SQL, Python, or R, to perform complex data manipulations and analyses.
- Write efficient SQL queries and Python/R scripts to process, analyze, and extract insights from large datasets.
- Apply programming techniques to automate repetitive data tasks and streamline analytical workflows.
- Assess the suitability of different data tools and programming languages for specific analytical tasks and business needs.
Data Visualization, Dashboard Design, and Communication of Insights
- Create advanced data visualizations, including interactive dashboards, using tools like Tableau, Power BI, or Python libraries.
- Design user-friendly dashboards that effectively communicate key insights to diverse stakeholders.
- Apply storytelling techniques to present data-driven insights clearly and persuasively in business contexts.
- Evaluate the effectiveness of visualizations and dashboards in conveying actionable insights and supporting decision-making.
Data Governance, Ethics, and Compliance in Analytics Projects
- Explain the principles of data governance, including data quality, accessibility, and lifecycle management, in analytics projects.
- Apply ethical frameworks to ensure responsible data use, addressing privacy, bias, and transparency concerns.
- Implement compliance measures to align analytics projects with legal regulations, such as GDPR, CCPA, or industry-specific standards.
- Evaluate the impact of data governance and ethical practices on the credibility and success of analytics initiatives.
The LICQual Level 4 Diploma in Data and AI – Data Analyst is designed for individuals who want to gain advanced skills in data analytics and AI-driven decision-making. This course is ideal for IT professionals, business managers, aspiring data analysts, and anyone looking to enter the growing field of data science. Learners will acquire practical skills and industry-recognized knowledge to boost employability and excel in data-driven roles.
IT and Data Professionals
- IT specialists looking to transition into data analytics roles
- Data engineers aiming to enhance their AI skills for better insights
- Professionals managing large datasets requiring analysis and reporting
- Analysts seeking to integrate AI techniques into daily workflows
- System administrators wanting to understand data trends and visualization
Business and Operations Managers
- Managers responsible for data-driven decision-making
- Professionals aiming to improve operational efficiency using analytics
- Leaders wanting to implement data-informed business strategies
- Decision-makers seeking actionable insights from organizational data
- Managers targeting better forecasting and reporting techniques
Aspiring Data Analysts
- Graduates aiming to enter the field of data analytics
- Students interested in combining data analysis with AI knowledge
- Individuals seeking industry-recognized qualifications in data science
- Career changers looking to move into analytics-focused roles
- Those wanting to develop strong technical and analytical skills
Compliance and Risk Management Professionals
- Compliance officers managing data integrity and security
- Risk managers seeking insights from analytics to mitigate organizational risks
- Professionals monitoring adherence to regulations through data analysis
- Specialists aiming to understand AI’s role in compliance frameworks
- Employees responsible for auditing and reporting on organizational data
Marketing and Business Intelligence Professionals
- Professionals analyzing customer behavior and market trends
- BI specialists looking to integrate AI for predictive analytics
- Marketing analysts needing data-driven campaign insights
- Professionals seeking to improve reporting efficiency using analytics tools
- Individuals aiming to make data-backed strategic decisions
Students and Career Changers
- Graduates looking to pursue careers in data science and AI
- Professionals transitioning from IT, business, or finance into analytics
- Individuals aiming to develop a competitive edge in the job market
- Learners wanting hands-on experience with modern data tools
- Career changers seeking globally recognized qualifications
International and Remote Professionals
- Global learners seeking a recognized Level 4 qualification in data analytics
- Professionals working across borders needing AI and data governance skills
- Consultants providing data-driven solutions internationally
- Individuals aiming to advance careers in multinational organizations
- Remote workers seeking practical and employable analytics skills
Assessment and Verification
All units within this qualification are subject to internal assessment by the approved centre and external verification by LICQual. The qualification follows a criterion-referenced assessment approach, ensuring that learners meet all specified learning outcomes.
To achieve a ‘Pass’ in any unit, learners must provide valid, sufficient, and authentic evidence demonstrating their attainment of all learning outcomes and compliance with the prescribed assessment criteria. The Assessor is responsible for evaluating the evidence and determining whether the learner has successfully met the required standards.
Assessors must maintain a clear and comprehensive audit trail, documenting the basis for their assessment decisions to ensure transparency, consistency, and compliance with quality assurance requirements.