Data science and analysis

A data analyst is responsible for collecting, organising, analysing, and interpreting data to obtain insights and support decision-making within an organisation. Here are some key tasks and responsibilities of a data analyst:

Data collection
Data cleansing
Data analysis
Data visualisation

The specific data analysed by a data analyst can vary depending on the nature of the business. It may include financial data, sales data, market research data, or even data related to service users in fields like medicine or human resources. Prior to analysis, data analysts are also responsible for preparing and cleansing the data to ensure its accuracy and integrity.

Once the analysis is complete, data analysts present their findings to colleagues throughout the organisation. This can be done through presentations, reports, or dashboards. It is crucial for the data analyst to transform the complex data into a format that is easily understandable for colleagues who may not have an analytical background.

While data analysts and data scientists both work with data, there are some key differences in their roles and skill sets. Here are the main distinctions 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.

Here are some more differences to take into consideration:

Scope of work: Data analysts primarily focus on extracting insights from data to support decision-making within an organisation. They work with structured and often well-defined data sets, perform descriptive analytics, and answer specific business questions. Data scientists, on the other hand, have a broader scope of work. They not only analyse data but also develop and apply complex algorithms, predictive models, and machine learning techniques to uncover patterns, make predictions, and solve complex problems.
Skills and expertise: Data analysts typically have strong skills in data manipulation, SQL, data cleaning, statistical analysis, and data visualisation. They are proficient in tools like Excel, SQL, and data visualisation software such as Tableau or Power BI. Data scientists, on the other hand, require a deeper understanding of advanced mathematics, statistics, and programming. They often have expertise in programming languages such as Python or R, machine learning techniques, data modeling, and data engineering.
Complexity of analysis: Data analysts focus on exploring and summarising data to provide actionable insights. They may perform regression analysis, segmentation, or trend analysis. Data scientists, on the other hand, tackle more complex problems and work with larger and messier data sets. They develop and implement algorithms, build predictive models, perform deep learning, and engage in advanced analytics to solve intricate business challenges.

It's important to note that the roles of data analysts and data scientists can vary across organisations, and there may be some overlap depending on the specific job requirements. In some cases, the terms data analyst and data scientist may be used interchangeably.

Data analysts and data scientists can be found in a wide range of industries and businesses, but below are some examples:

Financial services
Consulting
Technology companies
Healthcare
E-commerce
Retail
Public Sector
Manufacturing and supply chain
Academia and Research

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:

Financial analyst
Business analyst
Data analyst
Data scientist
Health informatics
Machine Learning Engineer
Technical Architect
Bright Network - provides a helpful overview of different analyst roles

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:

A great to start your research is MyCareer, our graduate and internships job board with vacancies from employers targeting Nottingham students and graduate

Also use:

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.

You may have some experience through your degree – particularly if you are using data analysis as part of your dissertation or projects - but if not, are there additional modules involving data analysis that you could choose?

Gaining work experience will boost your CV – look out for part-time jobs or internships involving data analysis. Consider:

The Nottingham Internship Scheme

Nottingham Consultancy Challenge

Nottingham Advantage Award - Capital One Business Decision Making Module

On-campus part-time, casual and temporary jobs

Unitemps - sign up for part-time and temporary work opportunities off campus

Taking some free high quality introductory courses - find out more on Moodle

There's also other free online courses such as edX or Futurelearn to learn new programming skills and practice data analysis techniques.