Data Analyst

24 months
Level 4
NCFE
Blended
Course Overview:

 

Data analysts are essential in any sector utilising data for business decisions. They operate across departments like finance, sales, HR, manufacturing, and marketing, in diverse fields such as retail, banking, media, and government. Their primary goal is to extract insights from data to solve problems and inform decisions. This involves various processes like requirement-gathering, data cleansing, transformation, and modelling. They Investigate various aspects such as social media trends, sales figures, staff retention rates, and service wait times to enhance organisational effectiveness. Interacting internally and externally, they provide data analysis services and collaborate with stakeholders. Responsibilities include adhering to data policies, ensuring data security, and staying updated with industry trends. This apprenticeship equips individuals with skills to interpret data effectively for organisational success.

 

Why choose an apprenticeship with ART Skills Centre?

ART Skills Centre is revolutionising the delivery of apprenticeships. Leveraging our extensive experience in providing quality blended learning to all our learners annually, using our unique blended delivery model that prioritises flexibility and efficiency for both apprentices and employers.

Our focus on high-quality learning resources and approach now extends to the apprenticeship sector, ensuring that more individuals receive personalised training and robust career guidance.

At the ART Skills Centre, we are committed to the personal and professional growth of our learners. Our individualised programme of learning is designed to help learners develop as individuals and employees, and to facilitate their progress at every stage of the learning journey.

 

Entry Requirements:

  • Apprentices without a level 2 qualification in English and maths must achieve this level before taking the End-Point Assessment.
  • For apprentices with an education, health, and care plan or a legacy statement, the minimum English and maths requirement is Entry Level 3.
  • For apprentices whose primary language is British Sign Language (BSL), a BSL qualification can be used as an alternative to the English qualification.

BENEFITS

Benefits for Learner(s):

  • Gain a nationally recognized and accredited qualification
  • Earn while learning
  • Access high-quality learning and assessment materials for your employees
  • Receive bespoke support for high-quality “off the job” learning
  • Access support from a qualified assessor / tutor and a Learner Support Advisor (LSA) during scheduled check-ins throughout the apprenticeship

 

Benefits for Organization(s):

  • Improve staff retention
  • Work in partnership with a reputable training provider
  • See rapid improvements in employee performance, productivity, and behaviours that will benefit your organisation

KNOWLEDGE

The following is a list of summarised contents (but not limited to) that a DATA ANALYST must know and understand upon completion of this programme:

  • Understanding current relevant legislation and its application to the safe use of data
  • Applying organisational data and information security standards, policies, and procedures relevant to data management activities
  • Implementing principles of the data life cycle and executing routine data analysis tasks
  • Recognising principles of data, including open and public data, administrative data, and research data
  • Differentiating between structured and unstructured data
  • Grasping the fundamentals of data structures, database system design, implementation, and maintenance
  • Incorporating principles of user experience and domain context for data analytics
  • Identifying quality risks inherent in data and implementing mitigation strategies
  • Defining customer requirements for data analysis
  • Integrating data from different sources for analysis
  • Utilising organisational tools and methods for data analysis
  • Understanding organisational data architecture
  • Applying principles of statistics for analysing datasets
  • Employing principles of descriptive, predictive, and prescriptive analytics
  • Addressing ethical aspects associated with the use and collation of data

 

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SKILLS

The following is a list of summarised contents (but not limited to) that a DATA ANALYST must be able to do upon completion of this programme:

  • Using data systems securely in compliance with organisational procedures and legislation, including Privacy by Design principles
  • Implementing stages of the data analysis lifecycle
  • Applying principles of data classification within data analysis activities
  • Analysing datasets considering various data structures and database designs
  • Assessing the impact of user experience and domain context on data analysis activities
  • Identifying and escalating quality risks in data analysis, proposing mitigation strategies
  • Undertaking customer requirements analysis and integrating findings into data analytics planning and outputs
  • Identifying data sources and associated risks and challenges in data combination within analysis activities
  • Applying organisational architecture requirements to data analysis tasks
  • Applying statistical methodologies to data analysis activities
  • Applying predictive analytics in data collation and utilisation
  • Collaborating and communicating effectively with internal and external stakeholders, adapting communication styles as needed
  • Using a range of analytical techniques such as data mining, time series forecasting, and modelling to identify trends and patterns
  • Collating and interpreting qualitative and quantitative data, converting them into infographics, reports, tables, dashboards, and graphs
  • Selecting and applying the most suitable data tools to achieve optimal outcomes

 

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BEHAVIOURS

The following is a list of summarised contents (but not limited to) on how a DATA ANALYST must behave or demonstrate upon completion of this programme:

  • Maintaining a productive, professional, and secure work environment
  • Showing initiative and resourcefulness in problem-solving
  • Working independently and collaboratively
  • Being logical and analytical
  • Identifying and solving complex problems efficiently
  • Viewing obstacles as challenges and learning from failure
  • Adapting to changing contexts within projects or organisational directions

 

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