Back in February 2021 I wrote a short blog about a Data Fellowship apprenticeship that I was beginning. Today that journey came to an end when I received notification that I had passed the final parts of the course, exam, projects and interview. This means that I now hold a qualification and am awaiting my certificate as BCS Data Analyst (level 4).
It has been a long journey to completion, but each stage has been an adventure and one that I have enjoyed working through.
I know that I haven’t posted into my blog in a while. Mainly because I have been busy with my Data Fellowship and a few other things. Recently I have been studying for todays exam “BCS Level 4 Certificate in Data Analysis Tools” – QAN 603/0824/2.
The ability to still take exams at home (under exam conditions), is a bit more relaxing than having to take a journey to get to an exam centre, but still just as unnerving as you complete and press the end exam button awaiting the mark. The ability to take exams at home, still under the same conditions with cameras on and screen shared does open the ability to obtain qualifications up to more people and fit them in better around a normal working day.
The objectives of this part of the course/exam are:
Explain the purpose and outputs of data integration activities
Explain how data from multiple sources can be integrated to provide a unified view of the data
Describe how programming languages for statistical computing (SQL) can be applied to data integration activities, improving speed and data quality for analysis
Explain how to take account of data quality when preparing data for analysis, improving quality, accuracy and usefulness
Explain the nature and challenges of data volumes being processed through integration activities and how a programming approach can improve this
Understand testing requirements to ensure that unified data sets are correct, complete and up to date
Explain the capabilities (speed, cost, function) of statistical programming languages and software tools, when manipulating, processing and cleaning data and the tools required to solve analysis issues
Explain how statistical programming languages are used in preparing data for analysis and within analysis projects
The last exam that I took was in an examination centre where you turn up and sit at an already configured computer. This time I sat the exam at home in my dining room with camera and microphones on. Special software ensuring that my only windows open are the exam and meeting room with the invigilator watching me.
Sitting down getting ready for the exam, I hit that unfortunate moment of your laptop is about to reboot and install an operating system upgrade. Great timing! Just enough time to get another device loaded with the right software and logins to the required pages. Not a good start to entering an exam for the mindset, but all went well in the end.
Study for this stage of the Data Fellowship has been part of the apprenticeship course and objectives. For me it was a cementing of the concepts and bringing some areas up to date.
Objectives are: Demonstrate knowledge and understanding of Data Analysis and its underlying architecture, principles, and techniques. Key areas are:
Explore the different types of data, including open and public data, administrative data, and research data
Understand the data lifecycle
Illustrate 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
Explore the steps involved in carrying out routine data analysis tasks
Understand the range of data protection and legal issues
Explore the fundamentals of data structures
Explore the database system design, implementation, and maintenance
Understands the organisation’s data architecture
Understands the importance of the domain context for data analytics
Max Hemingway 