Learning Journey

Building confidence using Excel as an analysis tool: structuring data correctly, producing summaries, and creating stakeholder-friendly outputs.

Key tasks and objectives achieved

• Researched and documented common laws/regulations relevant to working with customer data (why they matter, impacts, and consequences).

• Worked hands-on with a retail dataset:

  • Converted raw ranges into an Excel Table

  • Performed sorting and basic analysis

  • Calculated totals and averages using SUM and AVERAGE

• Built a Pivot Table summarising sales by county and product.

• Added a derived column to categorise products by sales volume using SWITCH.

• Practised charting/visual communication using a Bike Sales visualisation task.

• Completed a scenario task framing analysis for a board audience (how findings would be presented, including considerations like renewal behaviour).

Moving from spreadsheet outputs to interactive dashboards, and learning how different BI tools shape analysis and storytelling.

Key tasks and objectives achieved

• Tableau research: compared Tableau versions and explained limitations of Tableau Public.

• Built a Tableau dashboard using the EMSI_JobChange_UK dataset including:

  • A line chart showing job change trends

  • A UK map showing affected city locations

• Conducted open-scope exploratory analysis tasks:

  • Spotify dataset: found trends and documented insights that could support future organisational work

  • Health dataset: identified trends and reflected on how data supports NHS decision-making

• Completed multiple guided Power BI labs (evidence captured via screenshots):

  • Getting data into Power BI Desktop

  • Loading transformed data

  • Designing a report

  • Creating a dashboard

Understanding how data is stored and queried in relational databases, and translating business requirements into database structure.

Key tasks and objectives achieved

• Researched and answered questions on database fundamentals and key concepts.

• Studied and explained common JOIN types, including when each join is appropriate and what kind of data relationships they support.

• Completed a written, portfolio-relevant task:

  • Designed a database approach for a small retail business

  • Wrote a structured essay covering:

    • business requirements gathering

    • schema design (tables/entities/relationships/keys)

    • implementation considerations and steps

Cloud fundamentals and how organisations use Azure services to store, secure, integrate, and analyse data.

Key tasks and objectives achieved

• Researched core cloud concepts and cloud service models.

• Explained IaaS / PaaS / SaaS with examples and use cases.

• Researched key cloud terms and where they’re implemented in real organisations.

• Reviewed security/legal topics (including the Computer Misuse Act themes and related considerations).

• Produced a structured Azure-focused proposal section (the groundwork for your Paws & Whiskers project), covering:

  • Data analysis tooling options (e.g., analytics services)

  • Data integration and automation concepts

  • Data types and modelling approach (entities/relationships)

  • Storage formats and structures

  • Security and encryption considerations

  • Backup/disaster recovery planning

  • Visualisation approach (Power BI)

  • Scalability considerations

Using Python to automate analysis, explore datasets quickly, and work with tabular data using pandas.

Key tasks and objectives achieved

• Implemented FizzBuzz (classic interview problem) and captured both code and output.

• Worked with a real CSV (student.csv) to practise pandas fundamentals:

  • Loading data into a DataFrame

  • Viewing head() / info()

  • Summary statistics and basic exploration

• Worked with GDP (nominal) per Capita.csv:

  • Load and save to DataFrame

  • Print first/last rows

  • Select specific columns (e.g., Country/Territory, UN_Region)

• Extended exploration tasks as a group: experimenting with additional datasets and outputs for practice.