Data Analyst

Overview

Data Analyst Overview

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Data Analyst apprentices are taught how to collect, organise and study data to provide business insight. They are typically involved with managing, cleansing, abstracting and aggregating data, and conducting a range of analytical studies on that data. They'll understand data structures, database systems and procedures and the range of analytical tools used to undertake a range of different types of analyses.

To achieve their Data Analyst apprenticeship, apprentices must:

  • Demonstrate competence against two knowledge modules: Data Analysis Tools and Data Analysis. These are assessed by examinations set by the British Computer Society and regulated by Ofqual. Apprentices must pass both modules.
  • Submit a portfolio of evidence showing how they have applied the knowledge from these modules to projects and activities in their workplace
  • Complete their formal End Point Assessment, which comprises: a synoptic project to showcase knowledge and skills from across the apprenticeship; a review of their portfolio of evidence; and a final interview with an independent EPA assessor

Successful Data Analyst apprentices go on into roles such as a Data Analyst, Data Manager, Data Scientist, Data Modeller, Data Architect and a Data Engineer.

Technical Competencies

Upon completion of their Data Analyst apprenticeship, individuals will be able to:

  • identify, collect and migrate data to/from a range of internal and external systems
  • manipulate and link different data sets as required
  • interpret and apply the organisation's data and information security standards, policies and procedures to data management activities
  • collect and compile data from different sources
  • perform database queries across multiple tables to extract data for analysis
  • perform routine statistical analyses and ad-hoc queries
  • use a range of analytical techniques such as data mining, time series forecasting and modelling techniques to identify and predict trends and patterns in data
  • assist production of performance dashboards and reports
  • assist with data quality checking and cleansing
  • apply the tools and techniques for data analysis, data visualisation and presentation
  • assist with the production of a range of ad-hoc and standard data analysis reports
  • summarise and present the results of data analysis to a range of stakeholders making recommendations
  • work with the organisation's data architecture

Technical Knowledge and Understanding

Upon completion of their Data Analyst apprenticeship, individuals will understand the:

  • range of data protection and legal issues
  • data life cycle
  • different types of data, including open and public data, administrative data, and research data
  • differences between structured and unstructured data
  • fundamentals of data structures, database system design, implementation and maintenance
  • importance of the domain context for data analytics
  • quality issues that can arise with data and how to avoid and/or resolve these
  • importance of clearly defining customer requirements for data analysis
  • processes and tools used for data integration
  • steps involved in carrying out routine data analysis tasks
  • industry standard tools and methods for data analysis

Underpinning Skills, Attitudes and Behaviours

  • logical and creative thinking skills
  • analytical and problem solving skills
  • ability to work independently and to take responsibility
  • can use own initiative
  • a thorough and organised approach
  • ability to work with a range of internal and external people
  • ability to communicate effectively in a variety of situations
  • maintain productive, professional and secure working environment

Qualifications

Apprentices will achieve one BCS qualification and one vendor qualification.

Funding

£15,000

Level

This is a level 4 apprenticeship.

Professional Recognition

This apprenticeship is recognised for entry onto the register of IT technicians confirming SFIA level 3 professional competence and those completing the apprenticeship are eligible to apply for registration.

Duration

The duration of this Firebrand apprenticeship is 16 months. Because this period involves both training and the final End Point Assessment (some of which must be carried out in the workplace), employers need to ensure the apprentice’s contract covers the full programme duration.

Registration to the Register of IT Technicians (RITTech)

Once apprentices have completed their apprenticeship they are officially recognised by the British Computer Society (BCS) for entry onto the Register of IT Technicians, confirming SFIA level 3 professional competence.

Curriculum

Data Analyst curriculum

Firebrand’s apprenticeship programme covers all mandatory knowledge and skills outlined in the apprenticeship standard. Every Firebrand apprentice attends a suite of market-leading training programmes, to cover knowledge required from the apprenticeship standard. This training is then fleshed out through a package of selected online learning, which also allows apprentices to explore any topics of particular interest/importance to them in greater depth.

How are apprentices taught?

Apprentices receive a range of market-leading training as part of their qualification – typically between three and five courses per apprenticeship - giving them fundamental skills at speed.

We'll deliver all the knowledge apprentices need to learn for each knowledge module in the Standard through our Lecture | Lab | Review delivery. Apprentices then attend a Syllabus Review Session to cover the knowledge content covered in the apprenticeship standard.


Knowledge Module 1: Data Analysis Tools

Upon completion of this Knowledge Module, Data Analyst apprentices will:

  • Understand and be able to apply the processes and tools used for data integration
  • Understand and be able to apply industry standard tools and methods for data analysis

Read through the full curriculum for Firebrand's classroom-based training and supporting online learning modules below.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

  • Introduction and the Course Agenda
  • Introduction to Big Data Analytics
    • Big Data Overview
    • State of the Practice in Analytics
    • The Data Scientist
    • Big Data Analytics in industry verticals
  • The Data Analytics Lifecycle
    • Discovery
    • Data Preparation
    • Model Planning
    • Model Building
    • Communicating results
    • Operationalising
  • Review of Basic Data Analytic methods Using R
    • Introduction to R
    • Analysing and Exploring the Data
    • Statistics for Model Building and Evaluation
  • Advanced Analytics - Theory and Methods
    • K-Means Clustering
    • Association Rules
    • Linear Regression
    • Logistic Regression
    • Naïve Bayesian Classifier
    • Decision Trees
    • Time Series Analysis
    • Text Analysis
    • Advanced Analytics - Technologies and Tools
    • Analytics for Unstructured Data: MapReduce and Hadoop
    • The Hadoop Ecosystem
    • In-database Analytics - SQL Essentials
    • Advanced SQL and MADlib for In-database Analytics
  • The Endgame, or Putting it All Together
    • Operationalizing an Analytics Project
    • Creating the Final Deliverables
    • Data Visualization Techniques
    • Final Lab Exercise on Big Data Analytics
  • Introduction to R (4 Hours) *
  • R Programming Fundamentals (7 Hours)*
  • Data Science with R (2h 30minutes)*
  • Data Management and Preparation Using R (1h 59 minutes)*
  • Statistics Foundations: Understanding Probability and Distributions (4h 24 minutes)*

Total time: 19 hours 53 minutes


Knowledge Module 2: Data Analysis Concepts

Upon completion of this Knowledge Module, Data Analyst apprentices will:

  • Understand the different types of data, including open and public data, administrative data, and research data
  • Understand the data life cycle
  • Understands the differences between structured and unstructured data
  • Understand the importance of clearly defining customer requirements for data analysis
  • Understand the quality issues that can arise with data and how to avoid and/or resolve these
  • Understand the steps involved in carrying out routine data analysis tasks
  • Understand the range of data protection and legal issues
  • Understand the fundamentals of data structures, database system design, implementation and maintenance
  • Understand the organisation's data architecture
  • Understand the importance of the domain context for data analytics

Read through the full curriculum for Firebrand's classroom-based training and supporting online learning modules below.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

This online session, led by a Subject Matter Expert, will focus on core concepts that apprentices will need to learn to get the most from their next residential training course. It provides practical information and/or study activities to help apprentices gain the prerequisite knowledge needed for the course.

1. Types of Data

In this topic, you'll explore the different types of data, including open and public data, administrative data, and research data. You'll learn to:

  • Describe the difference between data, information and knowledge.
  • Explain the range of different types of data.
  • Apply classification schemes for data.

2. The Data Lifecycle

In this topic area, you'll explore the data lifecycle and learn to:

  • Illustrate and describe that the data lifecycle management is a comprehensive approach to managing the flow of an information system's data and its associated metadata.
  • Explain each of the stages of a data lifecycle

3. Structured and Unstructured Data

In this topic area, you'll illustrate the differences between structured and unstructured data. You'll learn to:

  • Describe that structured data is information which can be ordered and processed by data analysis tools.
  • Recognise common sources of structured data
  • Explain that unstructured data can take various formats
  • Illustrate that, in order to maximise insight and derive useful business intelligence, organisations need to analyse both structured and unstructured data.
  • Recognise how structured and unstructured data complement each other.

4. Requirements for Data Analysis

In this topic area, you'll show the importance of clearly defining customer requirements for data. You'll learn to:

  • Recognise and demonstrate that data itself does not provide the answers to business problems.
  • Recognise and apply the key to effective data analysis is by asking the right questions which are defined as stakeholder requirements.

5. Quality Issues for Data Analysis

In this topic area, you'll develop an understanding of the quality issues that can arise with data and how to avoid and/or resolve issues experienced. You'll learn to:

  • Recognise how data quality relates different attributes.
  • Illustrate the importance of good quality data.
  • Demonstrate that minor data errors can cause major issues for data analysis.
  • Identify the common sources of errors.
  • Demonstrate that improving data quality and defining an organisational strategy for improved source data creation and storage will directly benefit the value of data analytics to improve business decision making.

6. Data Analysis Tasks

In this topic area, you'll explore the steps involved in carrying out routine data analysis tasks. You'll learn to:

  • Discover that data analysis is typically cyclic and iterative and illustrate the typical activities.

7. Compliance and Audit Considerations

In this topic area, you'll explore and gain knowledge on the range of data protection and legal issues. You'll learn to:

  • Describe the data protection and privacy issues that can occur during data analysis activities.
  • Explain the need to comply with the Data Protection Act 1998 UK.
  • Recall and define the 8 principles of the Data Protection Act.
  • Recognise the need for an organisational data policy in relation to data governance.

8. Data Structures

In this topic area, you'll explore the fundamentals of data structures and database system design, implementation and maintenance. You'll learn to:

  • Identify that data structure refers to formalised ways of identifying, accessing and manipulating data attributes by forming logical groupings.
  • Explain the concepts behind relational database structures.
  • Discuss how data warehousing and Big Data (aka. NoSQL) structures address performance issues.
  • Demonstrate why the variety of data structures requires a range of different data access and techniques.

9. Database Design, Implementation and Maintenance

In this topic area, you will explore database system design, implementation and maintenance. You'll learn to:

  • Apply data modelling techniques within database design, producing data models from different perspectives.
  • Recognise the most common forms of database
  • Demonstrate how a logical data model can be transformed into a physical database design, including de-normalisation.
  • Recognise that database maintenance is an activity designed to keep a database running smoothly and that a database can become sluggish and lose functionality otherwise.
  • Illustrate the importance of maintaining a database by backing up the data securely.

10. Data Architecture

In this topic area, you will work to understand the organisation's data architecture. You'll learn to:

Explain how an organisation's data architecture defines how data is stored, managed, used and integrated within an organisation and its database systems.

  • Explain that metadata is data that defines the data an enterprise needs, stores and uses.
  • Identify the need for a single enterprise view of data and how the canonical data model achieves this.
  • Define the nature of the Data Architecture functions.

11. The Domain Context for Data Analytics

In this topic area, you'll explore the importance of the domain context for data analytics. You'll learn to:

  • Illustrate the importance of domain knowledge to effective data analysis.
  • Demonstrate the role of Decision, Descriptive, Predictive and Prescriptive analytics
  • Big Picture: Enterprise Data Management (1h 12minutes)*
  • Data Analytics: Hands On (5h 2mins)*
  • Database Fundamentals (4h 35minutes)*
  • Data Science: The Big Picture (1h 9minutes)*
  • Big Data: The Big Picture (1h 28 minutes)*

Total time: 13 hours 26 minutes

*Preparation for Accelerated Learning Classroom attendance/experience


Additional Courses

Choose from any of Firebrand's accelerated courses listed below to add to this apprenticeship programme. These courses are delivered when apprentices have submitted evidence to their End Point Assessment gateway.

These additional courses support apprentices in achieving a Distinction grade at End Point Assessment.

Prerequisites

Who can enroll on a Data Analyst apprenticeship?

End Point Assessment

How are Data Analyst apprentices assessed?

Progression Plan

How do Data Analyst apprentices progress?

Exams

Exams

While apprentices benefit from new digital skills they can use in their job, almost all digital apprenticeships that Firebrand offer provide the chance to gain industry recognised qualifications.

Apprentices gain qualifications through either BCS or Vendor specific exams where applicable. These qualifications add to a professional career and can be used to help move seamlessly between roles in the IT industry.

All relevant exams that will be achieved during this apprenticeship are listed below:

  1. BCS Level 4 Diploma in Data Analysis Concepts
  2. EMC Data Science Associate

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