BCS Level 4 Certificate in Data Analysis Tools

On this BCS Level 4 Diploma designed for apprentices, you’ll learn key skills needed to master Data Analysis Tools.

You'll cover the range of concepts, approaches, tools and techniques that are applicable to Data Analysts. You will learn skills and knowledge applicable to Data Analysis Tools and the underlying principles and processes of data integration.

Key learning areas include:

• Describing the purpose and outputs of data integration activities
• Explaining how data from multiple sources can be integrated to provide unified data
• Discovering programming languages and how they are used to integrate data and prepare it for analysis
• Explaining the nature and challenges of data volumes and types being processed through integration activities
• Developing testing strategies to test unified data for a range of states
• Demonstrating an understanding of the capabilities of statistical programming languages and proprietary tools
• Preparing data for analysis using a series of techniques
• Carrying out data analysis

At the end of this course, you’ll sit the official BCS Level 4 Certificate in Data Analysis Tools exam, covered by our Certification Guarantee.

At Firebrand, we're proud to be a BCS Accredited Training Partner. Through our Lecture | Lab | Review method, you’ll access official courseware, learn from certified instructors, and train in a distraction-free environment.

1. Processes and Tools Used for Data Integration

In this topic, the apprentice will describe how data integration is achieved through the manipulation of data from different sources. They will also learn about how this data is manipulated using programming languages and how it is prepared for analysis. You'll be able to :

1.1. Describe the purpose and outputs of data integration activities.

• Business need for analysis
• Non-functional requirements (such as speed and time available)
• Information structure and rules relevant to the business
• Rationale for using and integrating data from multiple sources
• Importance of data in a business context

1.2. Explain how data from multiple sources can be integrated to provide a unified view of

the data.

• Reasons for using data from multiple sources
• Importance of data source quality to improve the quality of results
• Filtering data to ensure only relevant data is combined to underpin business
• objectives
• Ensure data is selected in line with current legislation
• Data integration techniques
• Common user interface
• Virtual integration
• Physical data integration (for example ETL (Extract - Transform - Load))

1.3. Discover how programming languages for statistical computing can be applied to data

integration activities to filter and prepare data for analysis.

• Programming constructs
• Sequence, selection and iteration
• Modularisation, coupling and cohesion
• Commands for manipulating data (for example, but not limited to)
• Select and Select* statements
• From
• Where (such as but not limited to; AND, OR, use of Wildcards and ordering)
• Joins (inner and outer, right, left, Full, Union and Select into)
• Joins with duplicate values
• Joining on multiple fields
• Single queries
• Multiple queries
• Expressions
• Functions (such as but not limited to; Avg(), Count(), Max(), Min(), Group by,
• Round(), Cast(), Convert(), ISNULL ())
• Querying multiple tables in different information
• Selecting the first/last of occurrences
• Implicit data conversion

1.4. Explain the nature and challenges of data volumes and types being processed through

data integration activities.

• Big data sets
• Qualitative data versus quantitative data
• Technical requirements for managing large data set (such as, but not limited to; the
• location of data and challenge of restrictions due to the computer architecture)
• Data warehousing
• Data migration
• Master data management
• Integration design
• Business requirements for integration
• Objectives and deliverable
• Business rules
• Support models and SLAs
• Non-functional requirements
• Data integration tools (such as future scalability, implementation and support costs)
• Data synchronisation (such as data ownership, frequency of updates, format,
• security, data quality, performance and maintenance)

1.5. Develop appropriate testing strategies to ensure that unified data sets are correct,

complete and up to date.

• Check against business requirements
• Test for a variety of states (such as, but not limited to; presence, completeness,
• configuration and format, that data is valued and that data is not fragmented)
• Business testing & technical testing
• Technical acceptance testing (TAT)
• User acceptance testing (UAT)
• Performance stress tests (PST)

2. Industry Standard Tools and Methods for Data Analysis

In this topic, the apprentice will describe and use a range of tools, techniques and methods to prepare and analyse data. The successful apprentice should be able to:

2.1. Demonstrate the data manipulating, processing, cleaning and analysis capabilities of statistical programming languages and proprietary software tools capabilities and functions of statistical programming languages (such as, but not limited to; R, Python, SPSS, SAS, SQL, Microsoft Excel and VBA, Julia, Hadoop and Hive, Scala)

2.2. Demonstrate how to apply statistical programming languages in preparing data for analysis and conducting analysis projects.

• Preparation techniques (such as, but not limited to; searching and sorting, grouping,
• filtering, macros and modelling)
• Data cleaning to remove a range of data issues (such as, but not limited to; errors,
• invalid values, data that is out of range, outliers)
• Processing and analysing:
• Mean, Median, Mode and Range
• Probability
• Bias
• Statistical significance
• Linear Regression (simple & multiple)
• Logistics Regression (simple & multiple)
• Scatter plots and correlation
• And/Or probability
• Stem and leaf plots
• Factorials
• Box and whisker plots
• Methods for presenting results (such as, but not limited to; tables, charts and
• graphs, correctly arranged and presented using suitable language)
• Presenting for data analysts
• Working with people

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

If you take the exam in a language that is not your native or official language, you are entitled to 25% extra time and are allowed to use your own paper-based dictionary to translate during the exam.

There are no prerequisites to attend this course.

Exam and Firebrand-developed course materials.

Individuals who successfully complete their first examination of any BCS qualification will qualify for a free one-year Professional BCS Membership.

Six reasons to sit your course with Firebrand Training

• Three training options. Choose between residential classroom-based, live online, or city-centre courses
• All-inclusive course. A one-off fee covers all course materials, exams**, accommodation* and meals*. No hidden extras.
• Pass the first time or train again for free. This is our guarantee. We’re confident you’ll pass your course the first time. But if not, come back within a year and only pay for accommodation, exams and incidental costs
• You’ll learn more. Our expert instructors cover exclusive additional materials not taught through the official curriculum
• You’ll learn faster. Chances are, you’ll have a different learning style compared to those around you. We combine visual, auditory and tactile styles to deliver the material in a way that ensures you will learn faster and more easily
• You’ll be studying with the best. We’ve been named in the Training Industry’s “Top 20 IT Training Companies of the Year” every year since 2010. As well as winning many more awards, we’ve trained and certified over 145,000 professionals

*For residential training only. Doesn't apply to online courses
**Some exceptions apply. Please refer to the Exam Track or speak with our experts

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