Data science and analysis
With the amount of data becoming available through the use of smart technology, internet of things and web-based activities that businesses use, the need for data scientists and analysts is growing.
There is simply more data to analyse which requires more people. The need for deeper analysis is growing in order to work out what is going to help with the goals of the business so companies are investing more into their data analytics.
The World Economic Forum report 2018 states that by 2022, data analysts will be amongst the most in demand roles to support future technologies.
Spotlight On: Data science
In this video Vice President of Data and Insight at UNiDAYS, Tamara Castelli, talks about what it means to be a data scientist and what the future holds for this exciting and growing sector.
What does a data analyst do?
Simply put, the role of a data analyst is to interpret statistics and figures and translate them into a format that is easily understood by others. A data analyst generates data from systems within the business to scrutinise and prepare reports that enables others within the organisation to make decisions based on the analysis produced.
The data analysed can vary depending on the nature of the business. For example, the data could be financial, sales driven or market research. The data could also be used in organisations where details about a service user may need to be analysed, for example in medicine or human resources. Another part of a data analyst's role may involve preparing or cleansing the data to ensure it is accurate and not corrupt before analysis.
Data analysts present their findings either through a presentation, a report or dashboard to colleagues across the business. It is important that the data analyst transforms the data into an easily understandable format for colleagues without an analytical background.
What is the difference between a data analyst and a data scientist?
Data scientists are often involved in the entire data process of a business, for instance, setting the questions within the business that will inform the analysis used by data analysts.
Data scientists might use more advanced methods to produce data, for example, machine learning or artificial intelligence to predict future trends for the business. They may even be involved in creating different ways for the data to be gathered (or mined) within the business – for example, through setting up algorithms in their software or creating systems which will do some of the work for them.
Quite often, a data scientist may be required to have a higher level of qualification (often a PhD or masters but not always) but nevertheless, someone with strong technical and mathematics skills is required for this kind of role.
There are many differences but also many similarities between the roles. Read this article for an excellent summary.
inside BIGDATA - The difference between data science and data analytics
What type of employers do data analysts work for?
The leading employers for data scientists tend to be in the finance, retail and ecommerce sectors. Businesses in these sectors are keen to better understand their audience groups in order to target their focus on relevant products and offerings.
Sectors such as telecoms, oil and gas, and transport are increasingly using big data to make decisions that could positively impact their workforce, operations or sales.
Jobs are also available with:
- government departments
- universities and research institutes.
With experience, you can work for a consultancy firm in a client-facing role, working on projects for a range of companies.
... data analysts will become increasingly more important in all industries.
What job titles that I should look out for?
There are a range of job titles that companies use for people who work with data. The duties of analyst can vary massively between companies, so it is always a good idea to read the job description to check that the role covers the areas that interest you. The more common iterations of a data analysis role include (but are not limited to) the following:
What are the routes into data analysis?
You can get into data analysis without a degree especially with larger companies where there are trainee roles available. However, there are internship, placement and graduate schemes available in many companies.
Getting a graduate role in data analysis is possible if you do not have any prior experience in this area. However, developing your data analysis skills through an internship or work experience placement would give you an advantage over those without direct experience. According to Prospects, employers look for applicants with:
- numerical skills
- analytical skills
- advanced skills in Excel or MS Access
- the capacity to develop and document procedures and workflow
You will also need communication skills, teamwork skills and excellent attention to detail.
An understanding of the sector and business that you wish to work in is important if you want to impress a recruiter. This is often called commercial awareness. For example, if you understand how the customers or users of a business behave, you can easily check data anomalies to identify any issues with the data or if the dip or spike in behaviour can be explained by your analysis.
Find out more about commercial awareness
For financial analyst roles, it can sometimes be desirable to have experience or knowledge of the finance industry.
The requirement for higher level qualifications may vary between employers, so it is best to check the requirements of those companies that interest you. To explore further study options, use:
Find a PhD
Find a masters
Where do I look for job vacancies?
A great to start your research is MyCareer, our graduate and internships job board with vacancies from employers targeting Nottingham students and graduate
What do I need to emphasise during the recruitment process?
To progress through the recruitment process, you will need to:
- provide examples of the types of techniques you have used to analyse data
- have a look at the methods the company uses as stated in the job description or person specification and provide examples of using these or similar ones if you have no direct experience in those specified
- highlight your technical skills, for example advance knowledge of Excel or Access or any related programming languages such as R or SQL, and how you have used them
- emphasise your passion for data and attention to detail
- demonstrate your ability to talk about technical or complicated information in a way that is easily understood by a non-technical audience.
What can I do next at Nottingham to enhance my job prospects?