This is Where the Fun Begins

Welcome to the first Parscode article! The intention for this site is to create a short (but complete) path to becoming a data analyst. So let’s get straight into it.

What does a Data Analyst actually do? #

In short, they analyse data to drive actionable change.

The role mostly consists of:

1. Finding data
2. Transforming data
3. Presenting data

Data Analysts often work for, or with a stakeholder in a company - someone who has a ‘stake’ or interest in an area of the business. The work can be ad-hoc (one off question) or project-based where you may build a dashboard, or deep dive into a specific problem. Usually, a stakeholder will come to you with a question they want to know the answer to. Your job is to try and answer the question with data.

Being a Data Analyst could be good for you if you like problem solving, spotting patterns and building new things. Data Analysts are in high demand, generally have a good work-life balance and command comfortable salaries. Have a look online (search on Google Careers or LinkedIn) to see what roles are available in your area.

Types of Data Analyst #

The role of a Data Analyst can vary across sectors and companies. For example, a Financial Analyst will likely analyse sales, product perfomance and develop forecasting models. In contrast, a Data Analyst working for the Environment Agency might analyse the geographic spread of a disease, river flood levels or the cost of natural disaster defences. Even within the same company the same ‘Data Analyst’ role can be different, but they will all generally share the same process.

Data Analysts can also overlap with Data Engineer and Data Scientist roles - You may find that you need to build a relational database if the data you are analysing is going to be used for regular processes (Data Engineer), or that you want to build out machine learning forecasting models to predict how variables might change in the future (Data Scientist).

The majority of of a Data Analyst’s time will, and should, be spent cleaning, transforming and validating data. Why? Because you need to be able to trust the data you present to avoid misleading stakeholders into the wrong big decisions.

Data inputted by humans is rarely 100% accurate due to input error, but there are several steps we can take to get as close to this as possible - we will explore these steps in more detail later on in the path.