On this 2-day accelerated BCS Level 4 Diploma designed for Apprentices, you’ll learn key skills needed to master Data Analysis Tools.
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 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 areas covered include learning to:
During the course you’ll sit BCS Level 4 Certificate in Data Analysis Tools Exam. Don’t pass the first time? Don’t worry – you’ll be covered by our Certification Guarantee .
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.
1.2. Explain how data from multiple sources can be integrated to provide a unified view of
1.3. Discover how programming languages for statistical computing can be applied to data
integration activities to filter and prepare data for analysis.
1.4. Explain the nature and challenges of data volumes and types being processed through
data integration activities.
1.5. Develop appropriate testing strategies to ensure that unified data sets are correct,
complete and up to date.
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.
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.
Exam and Firebrand developed course materials
There are currently no prerequiustes for this course
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Well done Firebrand in such challenging times!"
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