BCS Level 4 Certificate in Data Analysis Concepts

On this 5-day accelerated BCS Level 4 Diploma designed for Apprentices, you’ll learn key skills needed to master Data Analysis Concepts.

You’ll be immersed in the curriculum, through our unique Lecture | Lab | Review technique, which allows you learn and retain information faster than traditional courses.

On this course, you’ll explore different types of data; understanding the data life-cycle and learning the fundamentals of data structures. Our expert instructors will take you through the steps of carrying out routine data analysis task and database system design. You’ll also grasp:

• The importance of clearly defining customer requirements for data analysis
• Quality issues that arise with data and how to avoid and/or resolve these
• The organisation‚Äôs data architecture
• The range of data protection and legal issues
• The importance of the domain context for data analytics

During the course you’ll sit BCS Level 4 Certificate in Data Analysis Concepts Exam. Don’t pass the first time? Don’t worry – you’ll be covered by our Certification Guarantee.

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

There are currently no prerequisites for this course.

The format for the exam is a one-hour, closed book multiple-choice exam consisting of 40 questions. The pass mark is 26/40 (65%).

If you're taking the exam in a language that is not your native/official language, you are entitled to 25% extra time and are allowed to use your own paper language dictionary to translate during the exam.