01 Performance
Professionals finish able to take a raw dataset from question to decision-ready presentation: cleaning in spreadsheets, querying with SQL, analysing with pandas, and communicating through honest, well-designed dashboards.
02 Objectives
- Frame business questions as measurable analyses
- Clean and reshape messy data efficiently
- Query relational databases with SQL joins and aggregations
- Perform exploratory analysis with Python/pandas
- Design visualisations that inform without misleading
- Deliver a stakeholder-ready analytical presentation
03 Content
Thinking with Data
- Types of data
- Questions before charts
- Analysis workflow
Excel Analytics
- Formulas & lookups
- Pivot tables
- Charts that communicate
SQL for Analysts
- SELECT to JOIN
- Aggregations
- Querying real databases
Python & Pandas
- DataFrames basics
- Cleaning & transformation
- Grouping & summary statistics
Dashboards & Storytelling
- Visual design principles
- Building interactive dashboards
- Executive presentation project
04 Knowledge
- Data types, structures and quality dimensions
- Spreadsheet modelling: references, lookups, pivots
- Relational database logic and SQL syntax
- Descriptive statistics that matter for decisions
- Visual perception principles behind chart choice
05 Skills
- Pivot-table fluency on realistic datasets
- Writing multi-table SQL queries from scratch
- pandas workflows: load, clean, group, summarise
- Chart selection and dashboard layout craft
- Narrating insights: context, finding, recommendation
06 Key Competencies
- Scepticism about data quality before conclusions
- Executive-level clarity in communication
- Tool agility across Excel, SQL and Python
- Reproducible analysis documentation
07 Values
- Excellence: doing careful, complete work rather than rushing
- Integrity: honest effort, original work and truthful reporting
- Curiosity: asking questions and exploring beyond the lesson
- Responsibility: safe, ethical use of technology and information
- Growth mindset: treating mistakes as steps toward mastery
08 Learning Activities
Dirty-data clinics
Fixing deliberately messy real-world-style datasets under time pressure.
SQL gyms
Progressive query challenges answered against a live practice database.
Chart-makeover sessions
Redesigning misleading or cluttered charts for clarity and honesty.
Insight presentations
Weekly two-minute insight briefings to build communication muscle.
09 Teaching & Learning Resources
- Dynamic Academy course materials and practice sets
- A laptop or desktop with required free software installed
- Internet access for research and tool accounts
- Access to the student portal for announcements, assignments and progress tracking
10 Evaluation Guide
11 Practical Projects
Project 1:Sales Data Exploration
Excel-to-dashboard analysis of a retail dataset with written findings.
Project 2:SQL Business Questions
Answering ten realistic business questions purely through queries.
Project 3:Capstone: Decision Dashboard
Full pipeline analysis with interactive dashboard and executive summary.
12 Learning Outcomes
- Clean and prepare messy real-world datasets
- Analyse data with Excel, SQL and Python/pandas
- Design clear, honest visualisations and dashboards
- Present findings that drive decisions
13 Course Pathway
Professional upskilling pathway: start with Digital Skills if needed, choose a career track (Data Analysis, Cybersecurity, Digital Marketing, Virtual Assistant, UI/UX Design), deepen with advanced courses, and build a portfolio that supports employment or freelance work.