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Data Analytics

What is Data Analytics?

Data analytics is the science of analysing raw data to make conclusions which then inform business decisions.

More companies are collecting a huge amount of data each and every day. They use this data to predict trends, see what customers are buying, and how their budgets should be spent. This involves manipulating vast tables of data, and being able to interpret that data in ways that can benefit the organisation.

In this article, we’ll go over the following questions:

  • What is data analytics?

  • How has data analytics transformed business?

  • What are the different roles in data analytics?

  • What skills do you need for a job in data analytics?

Learning more about this exciting field of business is becoming more important as organisations further digitise how they function. Read on to find out more.

What is data analytics?

Plainly speaking, data analytics is the practice of looking at data to answer questions and drive decision-making. It comprises the collection, organising and examination of real-world information in order to use it for a company’s benefit.

Data analytics has been around, in some form of another, ever since we invented the storage of information. People have been collating facts and figures and drawing conclusions from that data for centuries. However, in the 21st century, data analytics is taking on a life of its own in the corporate landscape. A successful data analytics initiative will provide a clear picture of where the company is, where it has been and where it should go.

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Python ProgrammingStarts on Jan 18thPart-timeView Details
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There are four primary types of data analytics: descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different goal and a different place in the data analysis process.

  1. Descriptive analytics - this is the process of describing historical trends in data, i.e. the question “what happened?” This can involve measuring traditional indicators such as return on investment (ROI) or other key performance indicators (KPI) relative to budgets and time spent on the project. Descriptive analytics are pure information: they summarise data in an understandable way, but interpretation and predictions are left for later.

  2. Diagnostic analytics - this process answers “why did that happen?” questions. These techniques take the findings from descriptive analytics and dig deeper to find the causes of those outcomes. Anomalies in the data are identified first, such as unexpected changes in a metric or within a market, and then investigated to find out why it happened.

  3. Predictive analytics - this stage focuses on answering questions about what will happen in the future. These techniques use the past steps to identify how long trends will last, and if anomalies are likely to recur. Tools to predict these outcomes include a variety of statistical and machine learning techniques, such as: neural networks, decision trees, and regression.

  4. Prescriptive analytics - finally, this stage of data analytics informs companies about what should be done to secure a good outcome. By using insights from predictive analytics, backed up by diagnostic and descriptive information, solid decisions can be made. Machine learning strategies are needed to find patterns in large datasets. By analysing past decisions and events, the likelihood of different outcomes can be estimated.

Each type is important, and companies have an ever increasing set of tools for gathering and analysing data. But each type may pose ethical questions about data analytics strategies that companies are employing. Every company has a duty to capture and store data safely, and a responsibility to use that data in a fair and appropriate manner.

How has data analytics transformed business?

Data has changed the business landscape by making a vast amount of information accessible to key decision makers. Gathering large amounts of data has become very cheap, and means companies can now make decisions based more on real life information, and less on guesswork. Learn more about how companies are Innovating Using Data Analytics. Huge swathes of data such as those gathered by companies are generally referred to as “big data”.

But while it’s easier than ever for companies to gather large amounts of data, it’s of no use to anyone just sitting there doing nothing. That’s where data analytics comes in.

Business decisions are not driven by the amount of data, but rather by questions that need answering. This is the fundamental difference between big data and data analytics:

  • Big data: the high-volume, high-variety information assets (data) that require technologically advanced forms of information processing to collect, clean, store and translate into a usable format.

  • Data analytics - the process of examining data in its raw or translated form with a specific objective in mind. This could be to find answers to questions, find evidence to back up arguments, or predict trends.

Data analytics is a new and exciting field, and almost everyone could benefit from learning more about it. Taking a data analytics course not only informs you about how companies collect and use consumer data, but also how your organisation may benefit from data-led approaches.

No matter your role, it’s likely that in the future your responsibilities will include some sort of data analytics, if they don’t already. It’s worth knowing exactly what that entails to be ready when the time comes. Ask yourself, How Data Literate Are You?

What are the different roles in data analytics?

Data Engineer, Data Architect, Data Analyst and Data Scientist are all roles in the data science field whose functions won’t be obvious to anyone who isn’t familiar with data science.

All of these functions relate back to the organising, cleaning and collection of data, as well as its interpretation. So, with that in mind, let’s take a bit more of a closer look at what they do:

  • Data Engineer - Data engineers work to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. They build, maintain and optimise the algorithms and systems by which the organisation collects its data.

  • Data Architect- Working alongside the data engineer, a data architect plans how data will be stored. It is their responsibility to plan out data storage and processing frameworks, which are then built by the engineer.

  • Data Analyst- A data analyst interprets the raw data into actionable insights. Like a translator, they have the industry knowledge and core skills needed to turn bare numbers and figures into trends, predictions and advice. They need to think creatively, investigate data from multiple angles, and present what they find in a way that’s understandable to others.

  • Data Scientist- This role is often interchangeable with a data analyst, but there are some key differences. For example, a data scientist should be able to apply statistical techniques in order to differentiate between signal and noise in the findings. They should also be able to make decisions about which observations from a data analyst are worth following up on.

What skills do you need for a job in data analytics?

Key skills for a data analyst include mathematical ability, programming languages such as SQL and Python, problem-solving, and attention to detail.

  • Many hard skills are non-negotiable in the world of data analytics. You will need to know at least two programming languages, such as SQL, Oracle or Python, as well as keeping up to date with their developments.

  • You will also need soft skills such as problem solving, and have a methodical and logical approach to work in general. Creativity does play a part in data analytics, but only in the later stages after the data has been gathered, cleaned and stored.

  • It’s essential that all of the data you gather and clean is as accurate as possible. This is why you need an excellent eye for detail, and be able to perform repetitive tasks without mistakes. And of course, project management and meeting deadlines are valuable skills to have as well.

If this doesn’t sound like you at first glance - don’t panic. Many of these skills are easy and enjoyable to learn, even while you’re working a full time job.

We believe it’s important for any modern professional to familiarise themselves with data analytics and inculcate it into their daily processes. That’s why our Data Analytics and Visualisation courses can be completed part-time, alongside your job. For more information, check out the course on our website.