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⚡ Source: ReedRef: 56811162

Trainee Data Analyst

ITOL Recruit·Bexley, London·Posted 1 week ago
💰 30-50k GBP/year
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Job description

Original text imported from Reed

Are you looking to benefit from a new career in Data Analysis?

If you are detail orientated, perceptive, organised, competent, analytical and can communicate well with those around you; you could have a truly rewarding future as a Data Analyst

We do this using our specialised Data Analyst career programme which looks to assist and place qualified candidates into a career pathway in Data Analysis.

Please note this career program is designed for entry level individuals with limited or no experience, so please do not apply if you are already an experience Data Analyst as we will be looking primarily at entry level roles.

Demand for Data Analysts has grown 20% year on year with experienced analysts easily commanding salaries of £50k+. All business decisions rely on data to ensure correct business decisions are made and therefore the role of the data analyst in the new digital world has become essential for business owners.

Below are current average salaries in the sector for lower-level positions and fully trained Data Analysts:

  • Junior Data Analyst - £30,000
  • Data Analyst - £50,000
  • Business Data Analyst - £67,500
  • Data Analytics Analyst - £80,000
  • Business Analysts - £60,000

Using our experience in providing data analysis and business analysis training online and through our expert recruitment consultants, we can provide a seamless journey and often fast-tracked route into a new career in Data Analysis.

The courses in the package have been identified by our recruitment partners as industry standards for the UK/European and International Data Analysis sector.

Skills shortages across all sectors are increasing the demand for qualified, entry-level career seekers and career changers.

Whether you are working full-time, part-time or are unemployed, this job and recruitment package has the flexibility to be completed at a pace that suits you and can be completed in a few weeks. Your job and career goals are completed in 4 easy steps listed below.

Step 1 - CompTIA Data+ Qualification

The first step is completing the CompTIA Data+ Qualification accredited by CompTIA.

This professional international industry-recognised qualification teaches you the essential skills of a Data Analyst. You will cover skills such as data mining, manipulating data, visualising data, and reporting on data.

Study time for the qualification is approximately 30 hours and qualification is achieved by completing a 1-hour multiple choice exam.

The course is provided online and comes complete with exam simulators and revision tools.

You will be provided with access to a tutor and a 1-hour online revision workshop prior to sitting your exam.

This qualification will set you on a route to becoming a data analyst.

Step 2 - Data Administration Training

Data is extracted using various method which are normally IT driven.

Therefore, we will provide you with access to the following additional courses to ensure you are suitably employable as a data analyst.

  • Microsoft excel - to expert level.
  • Learn SQL - the programming language used for extracting data from more complex databases.
  • Learn Python 3 - Python 3 is a simple to use programming language used by many analysts.
  • Learn Tableau - Tableu is a tool which analysts use to build visually appealing dashboards for complex data making it easier for stakeholders to comprehend.

Study time for these courses is approximately 30-60 hours and qualification is achieved through completion of the course with no exam.

Step 3 - Business Analysis Foundation

Business Analysts and Data Analysts often work very closely together and indeed many Data Analysts become Business Analysts and vice versa.

Adding a Business Analyst Foundation certificate will make you much more employable and enable you to cross over into business analyst roles and hybrid roles. Business Analysis Foundation accredited by the BCS (The British Computer Society also known as the Chartered Institute of IT).

Study time for this course is approximately 15 hours and qualification is achieved through an online BCS exam.

Step 4 - Recruitment Support

Now you are qualified for an entry level data analyst position our recruitment support team will now begin collaborating with you to help you secure your first a suitable role based on your new qualifications and any other experience you may have.

We have been helping candidates start and build careers in the project management industry since 2007 and have a 4.8 Trustscore on Trustpilot.

Our Recruitment Support team will help you work through job applications, interviews and provide you with a full cv review based around your new qualification and written to maximise your chances of obtaining a role in the project management sector.

They will provide you with guidance as to which roles our most suitable for you as an entry level project sector worker aiming to become a project manager. This will include mock job interviews as well as any help you feel you need.

Our money back guarantee

If after 1 year of passing your formal qualifications, we have been unable to help you secure a role, we will refund your study fees minus the cost of the exams.

However, we are normally able to help candidates find their first role within 6 months of qualifying and for locations based close to major cities this is often reduced to less than 3 months.

Check our testimonials from the hundreds of candidates we have already helped.

What Now?

To accommodate candidates, the training element of the package is available on finance terms of up to 1 year. This enables you to get qualified and start in your new role without having to fund all the training costs up front.

Enquire now and one of our experienced Career Consultants will contact you within 4 working hours to answer any questions that you may have and to assist you in taking the first step towards your Data Analyst Career aspirations.

SpeedCV AI

Key skills

AI-extracted from the job advert

Must-have skills
CompTIA Data+Data analysis fundamentalsAttention to detail
Nice-to-have
Excel proficiencySQL knowledgePython basicsData visualisation toolsBusiness analysis experience
Soft skills
Attention to detailAnalytical thinkingCommunicationOrganisationProblem solvingAdaptability
SpeedCV AI

Application advice

5 AI-generated recommendations to maximise your chances.

1

⭐ Highlight your analytical mindset and attention to detail at the top of your CV as these are specifically mentioned as key traits

2

📊 Emphasise any experience with data, even basic Excel work: 'Analysed 500+ customer records using Excel pivot tables'

3

🎓 Mention the CompTIA Data+ qualification prominently as it's the core certification for this programme

4

💼 Include any business analysis or process improvement experience as the role bridges into business analysis

5

🔧 List any technical skills like SQL, Python, or visualisation tools even at beginner level

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Suggested CV bullets

3 bullets our AI drafted for this specific advert, mirroring its ATS keywords.

How to tailor your CV

Add these 3 bullets under your most recent experience:

  • Completed CompTIA Data+ certification covering data mining, manipulation and visualisation techniques across 30 hours of structured learning
  • Analysed customer feedback data from 250+ responses using Excel pivot tables, identifying 3 key improvement areas for management review
  • Processed and validated financial records for 15 monthly reports, maintaining 99.8% accuracy through systematic quality checks

Free to copy — tailoring requires a 30-sec CV upload.

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Letter preview — tailored to ITOL Recruit

Dear Hiring Manager,

ITOL Recruit's Trainee Data Analyst programme represents exactly the career transition I've been seeking, particularly the structured approach combining CompTIA Data+ certification with practical data mining and visualisation skills. My analytical mindset and attention to detail align perfectly with the programme's requirements for entry-level candidates.

My background in problem-solving and working with detailed information has prepared me for the analytical demands of data analysis, and I'm eager to develop technical skills in data extraction, manipulation, and reporting through your comprehensive training pathway.

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SpeedCV AI

Interview questions

10 questions generated from this advert.

Technical

  • What is the difference between data mining and data visualisation?
  • How would you approach cleaning a dataset with missing values?
  • Explain the importance of data validation in analysis
  • What tools would you use for creating data dashboards?
  • How do you ensure data accuracy when extracting from multiple sources?

Behavioural

  • Describe a time when you had to analyse complex information to make a decision
  • How do you handle working with incomplete or unclear data?
  • Tell me about a situation where you had to explain technical findings to non-technical stakeholders
  • Give an example of when attention to detail was crucial in your work
  • How do you prioritise multiple data analysis tasks with competing deadlines?
SpeedCV AINEW

STAR answer examples

Model answers using the Situation-Task-Action-Result framework. Adapt to your own experience.

1Question

Describe a time when you had to analyse complex information to make a decision

When managing inventory for a retail store, I noticed discrepancies between our system records and physical stock counts. I systematically analysed 3 months of sales data, delivery records, and stock movements across 200 product lines. I discovered that returns weren't being properly logged in the system, creating a 12% variance. I presented my findings to management with a clear action plan, which resulted in implementing a new returns logging process that reduced inventory discrepancies to under 2% within 6 weeks.
2Question

How do you handle working with incomplete or unclear data?

During a project reviewing customer satisfaction surveys, I received data with 30% missing responses and unclear rating scales. Rather than making assumptions, I first contacted the survey team to clarify the rating methodology and identified patterns in the missing data. I then created a systematic approach to categorise responses, flagged uncertain entries for review, and presented findings with clear confidence levels for each conclusion. This approach ensured our recommendations were based on reliable data and management could make informed decisions with full transparency about data limitations.

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