DATA ANALYTICS
This four-phase programme prepares students for entry-level data analytics roles by combining foundational skills with advanced analytical techniques, industry-standard tools, and AI-powered workflows. Students progress from basic data handling to professional-level analysis, reporting, and automation. Every phase blends technical training with soft skills development, ensuring graduates can analyse data effectively, communicate insights clearly, and work collaboratively in professional environments.
Who Can Enrol?
Enrolment is open to students who have completed the foundation levels. If you are an experienced student who wishes to bypass the foundational levels, please contact us to discuss an exception.
PHASE 2: Intermediate Analysis & Statistical Thinking
This phase introduces students to the world of data and its role in decision-making. Learners explore how data is collected, structured, cleaned, and visualised to create meaningful insights. They will gain hands-on experience with spreadsheet tools, simple visualisation software, and basic statistics, all taught in an accessible and engaging way. Through peer programming, students collaborate on small datasets, practising both individual and group problem-solving. By the end of the phase, learners will be able to prepare and present a simple data analysis, applying core principles of accuracy, clarity, and ethical data use.
PHASE 1
LEVEL 1
Level 1: Introduction to Data & Spreadsheets
COURSE OVERVIEW
Students begin by learning what data is, where it comes from, and how it can be used to answer questions or solve problems. They explore basic data types, sources, and collection methods, and gain hands-on experience using spreadsheet software such as Microsoft Excel or Google Sheets. Core skills include entering, formatting, and organising data, as well as using simple formulas and functions. Ethical considerations, such as handling personal information responsibly, are introduced. Peer programming activities involve collaboratively working on shared spreadsheets, reinforcing teamwork and practical problem-solving.
DURATION 12 weeks COURSE FEE £480
PHASE 1
LEVEL 1
Level 2: Data Cleaning & Preparation
COURSE OVERVIEW
Clean and reliable data is essential for accurate analysis. This course teaches students how to identify and fix common data issues, such as duplicates, missing values, and inconsistent formatting. Learners explore spreadsheet tools for data validation, text-to-columns, and find-and-replace, alongside the principles of structured datasets. Peer programming sessions focus on collaboratively cleaning a shared dataset, ensuring all participants contribute to problem-solving and decision-making.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 1
LEVEL 1
Level 3: Basic Data Visualisation
COURSE OVERVIEW
Students learn to transform raw data into clear visual representations using charts and graphs. The course covers when to use different chart types, best practices for readability, and how visualisation supports decision-making. Learners create visualisations in spreadsheets and beginner-friendly tools such as Google Data Studio or Tableau Public. Peer programming allows teams to create visualisations together, comparing results and interpretations.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 1
LEVEL 1
Level 4: Phase 1 Capstone Group Project
COURSE OVERVIEW
This capstone consolidates all skills from Phase 1, requiring students to collect, clean, analyse, and visualise a dataset. Teams will work collaboratively to produce a clear, engaging dashboard or presentation that communicates findings effectively. Peer programming ensures shared responsibility for both technical tasks and design decisions. The project encourages creativity while reinforcing best practices in ethical and accurate data analysis.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
COURSE CONTENT
Phase 1 Level 1
Level 1: Introduction to Data & Spreadsheets
Technical Skills Covered:
- Data types and sources
- Spreadsheet navigation and formatting
- Basic formulas (SUM, AVERAGE, COUNT)
- Sorting and filtering data
- Introduction to data ethics
Soft Skills Covered:
- Organisational skills
- Ethical decision-making with data
- Team collaboration through shared files
Individual Project:
Students prepare a basic spreadsheet of a small dataset, apply formulas, and present findings.
Phase 1 Level 2
Level 2: Data Cleaning & Preparation
Technical Skills Covered:
- Identifying data quality issues
- Removing duplicates and handling missing data
- Data validation tools
- Formatting for consistency
- Introduction to structured data concepts
Soft Skills Covered:
- Attention to detail
- Critical thinking in data preparation
- Collaborative troubleshooting
Individual Project:
Students clean and prepare a provided dataset for analysis, documenting the steps taken.
Phase 1 Level 3
Level 3: Basic Data Visualisation
Technical Skills Covered:
- Selecting appropriate chart types
- Creating bar, line, and pie charts
- Labelling and formatting for clarity
- Introduction to dashboard tools
- Principles of data storytelling
Soft Skills Covered:
- Visual communication
- Creativity in presentation
- Collaboration in design decisions
Individual Project:
Students create a simple dashboard with multiple visualisations from a cleaned dataset.
Phase 1 Level 4
Level 4: Phase 1 Capstone Project
Capstone Group Project:
Teams prepare a complete analysis and visualisation project, from raw data to final presentation.
Soft Skills Integration:
- Innovation and Creativity: Designing effective visual outputs.
- Problem-Solving: Addressing data quality and analysis challenges.
- Time Management: Coordinating team tasks and meeting deadlines.
- Presentation Skills: Communicating insights clearly to an audience.
- Critical Evaluation: Reviewing results for accuracy and clarity.
PHASE 2: Databases, Authentication & Intermediate APIs
In this phase, students build on their foundational skills to perform deeper analysis and apply statistical thinking. They learn to work with larger datasets, use more advanced spreadsheet and visualisation functions, and apply basic statistical methods. Peer programming becomes more structured, with students taking on specific roles in collaborative analysis tasks. By the end, learners can confidently explore datasets, identify patterns, and present statistically sound conclusions.
PHASE 2
LEVEL 1
Level 1: Working with Larger Datasets
COURSE OVERVIEW
Students learn techniques for efficiently handling larger datasets in spreadsheet tools and begin using basic database concepts. The course covers sorting and filtering at scale, using pivot tables, and understanding data types in more detail. Collaborative activities involve exploring large, real-world datasets in peer programming sessions, reinforcing both technical and teamwork skills.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 2
LEVEL 2
Level 2: Introduction to Descriptive Statistics
COURSE OVERVIEW
This course introduces statistical concepts that help interpret data, such as measures of central tendency and variability. Students learn how to calculate and interpret mean, median, mode, range, variance, and standard deviation. Real-world examples illustrate how these measures inform decision-making. Peer programming exercises involve calculating and comparing statistics for shared datasets.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 2
LEVEL 3
Level 3: Advanced Visualisation Techniques
COURSE OVERVIEW
Students explore more advanced visualisation methods, including stacked charts, scatter plots, and heat maps. They learn how to tailor visualisations to the audience and purpose. Practical exercises use tools like Google Data Studio or Tableau Public. Peer programming sessions focus on designing visualisations that support specific business or research questions.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 2
LEVEL 4
Level 4: Phase 2 Capstone Group Project
COURSE OVERVIEW
This capstone integrates intermediate analytical and visualisation skills with descriptive statistics. Teams must explore a large dataset, summarise findings using pivot tables and statistical measures, and present insights through advanced visualisations. Peer programming ensures all team members contribute to both the analysis and design process.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
COURSE CONTENT
Phase 2 Level 1
Level 1: Working with Larger Datasets
Technical Skills Covered:
- Advanced sorting and filtering
- Pivot tables for summarising data
- Data type recognition and management
- Introduction to database concepts (tables, queries)
Soft Skills Covered:
- Organisational skills with complex data
- Analytical thinking
- Teamwork in data exploration
Individual Project:
Students create a pivot-table-based summary report from a large dataset.
Phase 2 Level 2
Level 2: Introduction to Descriptive Statistics
Technical Skills Covered:
- Mean, median, mode
- Range, variance, standard deviation
- Interpreting statistical results
- Using spreadsheet functions for statistics
Soft Skills Covered:
- Logical reasoning
- Data-driven decision-making
- Collaborative analysis
Individual Project:
Students calculate and interpret descriptive statistics for a dataset and present their conclusions.
Phase 2 Level 3
Level 3: Advanced Visualisation Techniques
Technical Skills Covered:
- Stacked and grouped charts
- Scatter plots and trend lines
- Heat maps
- Designing visuals for specific audiences
Soft Skills Covered:
- Visual storytelling
- Audience awareness
- Collaboration in design and review
Individual Project:
Students create an advanced visualisation addressing a defined analytical question.
Phase 2 Level 4
Level 4: Phase 2 Capstone Project
Capstone Group Project:
Teams deliver a comprehensive analytical report with advanced visual elements and statistical summaries.
Soft Skills Integration:
- Collaboration: Dividing and integrating analytical tasks.
- Problem-Solving: Overcoming dataset challenges.
- Time Management: Coordinating work across team members.
- Presentation Skills: Telling a clear story with data.
- Critical Evaluation: Ensuring accuracy and clarity in outputs.
PHASE 3: Data Tools, Automation & Predictive Thinking
This phase introduces students to professional data tools and the concept of predictive analysis. Learners begin using programming tools for automation, such as Python for basic data handling, and explore AI-assisted analysis. Predictive concepts are introduced at a simple level to prepare for more advanced methods in the final phase. Peer programming continues to develop collaboration and technical problem-solving.
PHASE 3
LEVEL 1
Level 1: Introduction to Python for Data Analysis
COURSE OVERVIEW
Students learn the basics of Python programming for data analysis. Topics include reading and writing data, using libraries such as Pandas, and performing basic transformations. Peer programming sessions involve coding small scripts together, encouraging shared learning and problem-solving.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 3
LEVEL 2
Level 2: AI-Assisted Analysis
COURSE OVERVIEW
This course introduces students to AI tools that assist with data analysis, such as generating summaries, detecting anomalies, or suggesting visualisations. Ethical considerations of AI use in analytics are also discussed. Peer programming allows students to test AI outputs and refine them collaboratively.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 3
LEVEL 3
Level 3: Introduction to Predictive Concepts
COURSE OVERVIEW
Students explore the basics of predictive analysis, learning about trends, forecasting, and correlation. They apply these concepts to simple datasets using spreadsheet and Python tools. Peer programming activities include developing basic forecasts as a team.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 3
LEVEL 4
Level 4: Phase 3 Capstone Group Project
COURSE OVERVIEW
This capstone requires teams to combine Python data handling, AI-assisted analysis, and basic predictive thinking to analyse and forecast trends from a dataset. Peer programming ensures active participation from all members, with final outputs including visualisations and written summaries.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
COURSE CONTENT
Phase 3 Level 1
Level 1: Introduction to Python for Data Analysis
Technical Skills Covered:
- Python basics (variables, loops, functions)
- Reading and writing CSV files
- Data cleaning with Pandas
- Basic data transformations
Soft Skills Covered:
- Problem-solving in programming
- Collaborative coding
- Logical thinking
Individual Project:
Students write a Python script to clean and summarise a dataset.
Phase 3 Level 2
Level 2: AI-Assisted Analysis
Technical Skills Covered:
- Using AI platforms for data summaries
- AI-assisted anomaly detection
- AI-generated visual suggestions
- Evaluating AI outputs for accuracy
Soft Skills Covered:
- Critical thinking in AI evaluation
- Adaptability to new tools
- Collaboration in AI-human workflows
Individual Project:
Students use an AI tool to generate an analysis, then validate and improve it manually.
Phase 3 Level 3
Level 3: Introduction to Predictive Concepts
Technical Skills Covered:
- Identifying trends in data
- Simple forecasting techniques
- Correlation analysis
- Basic predictive modelling concepts
Soft Skills Covered:
- Analytical reasoning
- Communication of predictive results
- Collaboration in model building
Individual Project:
Students produce a simple forecast and present how it could be applied to decision-making.
Phase 3 Level 4
Level 4: Phase 3 Capstone Project
Capstone Group Project:
Teams deliver a predictive analysis project using both AI and manual methods.
Soft Skills Integration:
- Innovation: Combining AI and manual methods effectively.
- Collaboration: Sharing technical tasks across the team.
- Problem-Solving: Handling data inconsistencies.
- Presentation Skills: Explaining predictive findings clearly.
- Critical Evaluation: Assessing model accuracy.
PHASE 4: Professional Data Analytics & Career Preparation
In the final phase, students work with production-ready datasets and tools, applying their skills to real-world scenarios. They create professional dashboards, automate reporting workflows, and prepare for employment through portfolio building and mock interviews. AI integration is advanced, focusing on workflow automation and scaling insights. By the end, learners will be fully equipped to start a career in data analytics or pursue further study.
PHASE 4
LEVEL 1
Level 1: Advanced Dashboard Design
COURSE OVERVIEW
Students master dashboard creation using tools like Tableau, Power BI, or Google Data Studio. The focus is on designing for usability, interactivity, and performance. Peer programming encourages collaborative dashboard development.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 4
LEVEL 2
Level 2: Workflow Automation for Reporting
COURSE OVERVIEW
Students learn to automate repetitive analytics tasks using Python scripts, AI tools, and dashboard scheduling features. They design workflows that reduce manual work and ensure timely reporting. Ethical implications of automation are discussed.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 4
LEVEL 3
Level 3: Professional Reporting & Career Skills
COURSE OVERVIEW
This course covers writing professional analytical reports, presenting to different audiences, and preparing a data analytics portfolio. Career preparation includes CV writing, LinkedIn profile optimisation, and mock interviews. Peer programming is applied in collaborative presentation preparation.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
PHASE 4
LEVEL 4
Level 4: Phase 4 Capstone Group Project & Mock Interviews
COURSE OVERVIEW
This final capstone challenges students to design and deliver a professional-grade analytics project using real-world data. Teams must create a dashboard, automate reporting, and produce a professional report and presentation. AI-powered tools should be integrated where appropriate. The project simulates professional client delivery, ensuring students can meet employer expectations.
DURATION 12 weeks COURSE FEE £480
NOTE: Entry to a level requires the completion of all preceding levels. Upon successful completion of a level, students will be automatically enrolled in the next one. Parents and guardians will receive a message with all the details.
COURSE CONTENT
Phase 4 Level 1
Level 1: Advanced Dashboard Design
Technical Skills Covered:
- Multi-page dashboards
- Interactive filters and slicers
- Performance optimisation in dashboards
- Designing for different audiences
Soft Skills Covered:
- Creativity in design
- User-focused thinking
- Collaboration in shared design projects
Individual Project:
Students build an interactive dashboard tailored for a specific audience.
Phase 4 Level 2
Level 2: Workflow Automation for Reporting
Students learn to automate repetitive analytics tasks using Python scripts, AI tools, and dashboard scheduling features. They design workflows that reduce manual work and ensure timely reporting. Ethical implications of automation are discussed.
Phase 4 Level 3
Level 4.3: Professional Reporting & Career Skills
Technical Skills Covered:
- Report structure and formatting
- Presenting data to non-technical audiences
- Portfolio building
- Job application skills for analytics roles
Soft Skills Covered:
- Professional communication
- Critical thinking in presentation planning
- Self-presentation skills
Individual Project:
Students prepare a professional report and present it as part of a mock interview.
Phase 4 Level 4
Level 4.4: Phase 4 Capstone Project
Course Overview:
This final capstone challenges students to design and deliver a professional-grade analytics project using real-world data. Teams must create a dashboard, automate reporting, and produce a professional report and presentation. AI-powered tools should be integrated where appropriate. The project simulates professional client delivery, ensuring students can meet employer expectations.
Capstone Group Project:
Teams deliver an end-to-end analytics solution, from raw data to final report and dashboard.
Soft Skills Integration:
- Leadership: Coordinating multi-role team efforts.
- Problem-Solving: Overcoming data and workflow challenges.
- Time Management: Meeting tight project deadlines.
- Communication: Presenting technical findings effectively.
- Critical Evaluation: Reviewing outputs for quality and impact.
SCHEDULE: STANDARD, FAST PACE, & 1-TO-1
Enrolment is open to students who have completed the foundation level. Use the button below to discuss an exception. We offer a flexible schedule with multiple start times available every month, allowing you to choose the time that best suits you. While our standard pace is designed to accommodate everyone’s needs, we also provide a fast-pace option for students who are determined to complete the programme before they turn 18. This is particularly beneficial for students in year 10 and 11 who need to complete the curriculum before they are 18. Please note that seats for all classes are limited and allocated on a first-come, first-served basis.
STANDARD SCHEDULE
Classes run for 12 weeks with one 2-hour session per week. New sessions start every month, with multiple start times available to choose from. A £50 registration fee is required to secure your place. The maximum class size is 5 to 7, and the total fee is £480. Available dates and times are listed in the application form.
FAST PACE SCHEDULE
Classes run for 6 weeks with two 2-hour sessions per week. New sessions start every month, with multiple start times available to choose from. A £50 registration fee is required to secure your place. The maximum class size is 5 to 7, and the total fee is £480. Available dates and times are listed in the application form.
PRIVATE (ONE-TO-ONE)
The private one-to-one is available on demand. It costs £1800 and covers a total of 12 sessions (2 hours per session). You can tailor the frequency and start times to your individual needs, allowing you to complete the sessions at a pace that suits you.
Also Included
Our comprehensive curriculum goes beyond technical skills to ensure your success. We prioritise essential professional skills like communication and problem-solving, integrate the theory and practice of AI, and provide support for professional certifications and job preparation.
Professional Skills
Our programmes go beyond technical training to include essential professional skills. This focus on workplace readiness is crucial for building confidence and giving students a competitive edge in their future careers.
Artificial Intelligence
The practical application of Artificial Intelligence is integrated into all our courses. This is crucial for equipping students with future-proof skills, giving them a competitive edge in any modern career path.
Professional Certifications
Our programmes provide a valuable opportunity to prepare for industry-recognised certifications in mobile development. While pursuing certification is optional, it offers several key advantages for your job search.
Job Preparation Support
We offer comprehensive job preparation support, helping students with everything from CV writing to interview skills. This is essential for building confidence and securing their first job in the industry.