Learn Data Science Through Practical Analysis
Develop practical skills for working with data — from cleaning and exploring datasets to visualising information, analysing patterns and communicating findings.
- Python
- pandas
- Visualisation
- Statistics
- Intro to ML
Who It's For
Data Science learning is suited to learners who want practical skills for working with data.
- Beginners
- University students
- Adult learners
- Professionals
- Python learners moving into data science
- Learners interested in analytics and machine learning
What You'll Learn
Introductory Data Science focuses on working with and understanding data. Machine learning is introduced where appropriate.
- Understand how data is collected, cleaned and prepared
- Explore datasets and summarise them with descriptive statistics
- Create clear visualisations to show patterns
- Use Python and pandas for data analysis
- Interpret results carefully and communicate findings
- Explore an introduction to machine learning where appropriate
Topics Covered
Topics are grouped from introductory foundations to more advanced modelling.
Working with Data
- Data collection concepts
- Data cleaning
- Data preparation
Analysis
- Exploratory data analysis
- Descriptive statistics
- Interpreting results
Tools
- Python for data analysis
- pandas
- NumPy where appropriate
- Matplotlib and visualisation
Communication & Next Steps
- Communicating findings
- Introduction to machine learning (more advanced)
Machine learning is a more advanced stage and is introduced only when the foundations are in place.
How Learning Works
- 1
Understand
Clear explanations of the statistical and analytical ideas behind each step.
- 2
Practise
Guided exercises cleaning, exploring and visualising data in Python.
- 3
Apply
Practical analysis tasks, which may use realistic datasets.
- 4
Review and reinforce
Feedback on your analysis and how clearly you communicate results.
- Clear explanations
- Worked examples
- Guided practice
- Practical projects
- Realistic datasets
- Feedback
Practical projects
Learners may work with realistic datasets and practical analysis tasks, chosen to suit their level and interests. Projects are agreed as part of your learning plan.
Your learning journey
A typical progression, adapted to your starting point:
- Python foundations
- Data preparation
- Exploration
- Visualisation
- Analysis
- Communication
- Introduction to modelling
Learning Format
One-to-one
Personal sessions built around your goals, experience and pace.
Small group
Shared sessions where available. Ask about current options.
Live online
Work on code and data together in real time, from wherever you are.
In person
Possible in Bournemouth, Christchurch and Poole where arrangements allow.
- Pricing:
- On enquiry
- Schedule:
- Agreed during your free consultation
- Delivered by:
- ADEL Learning
What Progress Can Look Like
- More confident handling of real-world data
- Better ability to spot patterns and summarise findings
- Clearer visualisations and written explanations
- Practical experience using Python for analysis
- A foundation for further study in machine learning
Progress depends on each learner. We don't guarantee specific grades, results or outcomes.
Frequently Asked Questions
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Ready to start learning?
Not sure which learning option is right for you? Book a free consultation and let's discuss your goals.