data analyst salary australia

Academy Xi Blog

Market Update: How much do Data Analysts earn in Australia 2022

By Academy Xi

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data analyst salary australia

Are you drawn to a career in Data Analytics and keen to find out what’s going on in today’s industry? We’ve put together this market update to bring you all the latest Data Analytics insights, statistics and trends.

Most companies have a wealth of data at their fingertips. However, in its raw form data isn’t worth that much. Luckily, this is when Data Analytics steps in. Data Analytics is the process of analysing raw data and extracting meaningful, actionable insights. These insights are then shared with team members and used to make smart, well-informed business decisions. 

Data analytics is all about finding data patterns which tell us something useful about an area of a business. For example, it can be used to predict the behaviour of customers, or to assess where a website’s traffic is coming from. In short, Data Analytics is a form of business intelligence that companies use for problem solving and strategic decision-making. 

Is Data Analytics a good career in Australia?

Forbes has predicted the amount of new data produced in the next three years will be greater than that produced during the past three decades. This means the demand for Data Analysts with the skills needed to turn raw data into valuable insights is at an all-time high.  

According to James Milligan, global Head of Technology at international recruitment firm Hays, “data is the new corporate currency, as advancing digitisation sweeps over every horizontal and vertical market in the world. The impact on the Data Science sector is far-reaching and a range of new roles and skill-sets are in high demand.”

With a full range of industries now keen to harness the power of data, Seek predicts that employment opportunities for Data Analysts in Australia will grow by 27.7% in the next five years, ranking it as one of the nation’s most promising employment markets. 

Are Data Analysts in demand in Australia?

Data Analysts are currently in high demand in Australia and will remain so for the foreseeable future. Wherever there’s new data, there’s a need for Data Analysts.

Currently, there’s a sizable skills gap for Data Analysts in Australia, which is reflected in the 17,451 roles advertised on Seek.com.au (as of August 2022). Breaking these numbers down by state: 

  • New South Wales offers 6,581 roles 
  • Victoria offers 5,083 roles 
  • Queensland offers 2,404 roles 
  • Western Australia offers 1,063 roles
  • Australian Capital Territory offers 903 roles
  • South Australia offers 607 roles
  • Northern Territory offers 105 roles
  • Tasmania offers 119 roles

It’s also worth remembering that many Data Analyst roles can be fulfilled remotely. Advances with online work systems mean being a remote Data Analyst can be every bit as collaborative and engaging as working in-person.

The Australian Financial Review has predicted the shift toward remote work will be maintained throughout 2023, with Seek currently advertising 1,809 remote Data Analyst roles throughout Australia.  

Your earning potential as a Data Analyst in Australia

The earning potential for Data Analysts in Australia is representative of a lucrative industry that’s always on the lookout for skilled professionals. The latest stats from Talent.com record the average Data Analyst salary in Australia as $104,450. Even Junior Data Analysts earn an average annual salary of $90,988, while Senior Data Analysts make $129,500 a year on average.   

The average salary in each state is as follows:

Which industries most commonly hire Data Analysts?

Data Analysts have the capabilities to help any kind of business make shrewd, data-backed decisions. As a result, Data Analysts are highly sought after in just about all industries. Some of the industries that most frequently hire Data Analysts include:

Finance

As you might have guessed, the finance industry deals with vast amounts of data and relies heavily on the expertise of Data Analysts.

Data Analysts help finance companies complete credit scoring, perform financial modelling, make profitable investments, enhance customer satisfaction and improve business operations.  

Because finance is now almost completely data-driven, the industry is one of the highest employers of Data Analysts and an excellent field for a Data Analyst to build a career in.

Healthcare

Another industry that works with masses of data is healthcare. The healthcare industry looks to Data Analysts to gather and analyse data, helping healthcare professionals accurately diagnose patients and provide the most effective treatments. 

The outbreak of an international pandemic has seen big data take on a pivotal role in healthcare, with Data Analysts helping healthcare organisations predict the spread and impact of diseases.  

Healthcare has long been one of the industries that most frequently hires Data Analysts and remains a promising industry to launch a career in. 

Retail

The application of Data Analytics in retail enables businesses to make customer recommendations based on purchase history and behaviour, resulting in personalised shopping experiences and improved customer service. 

Working with big data also helps retail companies to forecast trends and make strategic decisions based on market analysis. As a result, it’s an industry in which Data Analysts are in high demand. 

Entertainment

Large streaming services are now dominating the entertainment market, and they’re using data to drive that growth. Netflix has recently used its data to recommend shows to users with an 80% success rate, cutting costs by a billion dollars each year. 

The entertainment industry also uses Data Analytics to decide which productions to greenlight, test marketing strategies, set prices and optimise the user experience offered by online platforms. 

With the entertainment sector increasingly focused on tapping into its data, it’s an industry that provides plenty of employment opportunities for Data Analysts.   

What other titles do Data Analysts go by?

When you’re searching for Data Analyst positions, it’s worth remembering that Data Analyst roles often go by different titles. In some cases, the following job titles can refer to roles that come with a remit of Data Analytics:

  • Business Intelligence Analyst
  • Data Engineer
  • Quantitative Analyst
  • Data Analytics Consultant
  • Data Administrator
  • Operations Analyst
  • Marketing Analyst

What are the top skills a Data Analyst needs?

Today’s Data Analyst needs a wide repertoire of capabilities, including a mix of both soft skills and hard skills.

What are the most important hard Data Analytics skills?

Coding

While it’s not imperative for Data Analysts to have advanced programming skills, it’s essential that they at least have a firm grasp of coding basics.  

One of the most popular programming languages among Data Analysts is Python. Python enables Data Analysts to work with massive amounts of data, automate reporting and connect to databases seamlessly.

Another programming language that Data Analysts frequently use is SQL, which allows data professionals to access and query databases, and then clean and manipulate any retrieved data.  

Regression modelling 

Regression modelling is a sophisticated way to measure the relationship between multiple variables in a dataset. This helps analysts confidently determine which factors within data matter most and which can be ignored, as well as how important factors influence each other. Using regression modelling, Data Analysts are able to make all kinds of accurate predictions.  

Regression modelling is a go-to method in analytics that many Data Analysts will use at some stage in their career. For anyone keen to advance their career, it’s a technical skill that’s well worth developing.  

Data visualisation 

Data insights are of minimal value unless they can be shared with colleagues and used to influence decision making. For this reason, Data Analysts will normally convert their findings into high-impact visualisations (charts, graphs, tables and other infographics) which are used to persuade stakeholders to pursue a new course of action.   

While spreadsheets like Excel can be used to build basic visualisations, using programming languages like Python allows Data Analysts to create custom infographics that are packed with data insights yet easy on the eye.

What soft skills does a Data Analyst need?

While many Data Analysts focus on learning technical skills, there are a range of soft skills that are needed to advance in the industry. Some of the vital soft skills any Data Analyst should have include:

  • Curiosity – Data Analytics is all about being inquisitive, digging beneath the surface of problems and uncovering data-driven solutions. 
  • Critical thinking – Data Analytics is all about critical thinking and framing the right questions. If the questions a Data Analyst asks are well grounded in their business, product and industry, they’re more likely to get the answers they need. 
  • Communication – Data Analysts are often expected to report their findings to teammates and stakeholders, which means public speaking and presentation skills are crucial. 
  • Collaboration – Data Analysts rarely work in isolation and need to be capable of working effectively with teammates. Remember – the opportunities for data insights multiply as more minds are brought to bear on a problem.  
  • Business acumen – Understanding their organisation’s business objectives and market position enables Data Analysts to initiate data research projects that solve critical business problems.

The 5 latest trends in Data Analytics

With tech and software advances always pushing the possibilities of Data Analytics, it’s an exciting time to be involved in the industry. Here are five Data Analytics trends to keep an eye out for 2022 and beyond. 

AI analytics

AI analytics is a subset of business intelligence that relies on machine learning techniques to uncover patterns and reveal insights in data. In practice, AI analytics involves automating much of the work that a data analyst would normally conduct manually.

It should be noted that the aim of AI analytics is not to replace analysts. Instead, it can enhance the scale and granularity of data that a Data Analyst can analyse, while also making their work more time-efficient.

Composable data analytics 

Composable data analytics is a process by which businesses combine analytics capabilities from various data sources. Composable data analytics means companies are able to easily pool all their information, leading to effective decision-making that takes more data into account.  

Additionally, with composable analytics, businesses can drastically reduce data centre costs. Gartner analysts have predicted that by 2023, 60% of businesses will have built applications composed of components from at least three analytics solutions.

Democratisation

If you still think of Data Analytics as a relm reserved only for professional Data Analysts, your conception is probably a little outdated. These days, people working in every kind of role in every kind of industry have access to data and are using it to enhance their professional performance.  

Gone are the days when professionals were entirely reliant on data teams to perform analytics on request. The democratisation of data will see more and more people analysing data on their own, aided by simple, user-friendly software and tools that are designed to make the processes of Data Analytics accessible to everyone. 

Data visualisation  

The ability to quickly and easily grasp crucial information has long been a core focus of the Data Analytics process. Going way beyond simple pie charts and graphs, modern Data Analysts build interactive data visualisations that not only keep viewers informed, but also encourage them to actively engage with data insights. 

Recent years have seen the emergence of immersive videographics, data illustrations and real-time infographics (which constantly update with the latest data).

Real-time analytics decisions 

Sticking with the real-time theme, Data Analytics is now all about the speed with which decisions are made in response to data insights. The ability to make quick, accurate decisions has always been crucial in data science, but the onset of the pandemic made speed an even more essential trait of the analytics process. 

Now, more and more businesses want their data to be as current as possible and their decision-making processes to be ultra nimble. As a result, expect to see an agile, real-time approach brought to business analytics in the coming years. 

Entry points and career pathways in Data Analytics

Entering the world of Data Analytics might seem like a daunting feat, but getting started in the industry is easier than you might think. 

To launch a career as a Data Analyst you’ll need to follow a few simple steps:

  • Get educated – you’ll need to master the essential tools and practical skills.
  • Develop foundational skills in coding languages (it’s often wise to pick a language that’s versatile and widely used).
  • Identify your area of interest – Data Analytics is a broad field and you’ll need to identify your niche.
  • Build a portfolio demonstrating to employers that you’re capable of working effectively with data.

When searching for your first role, it’s not uncommon to start as a Junior Data Analyst, which will entail:

  • Gathering, storing and organising data
  • Cleaning, manipulating and analysing data
  • Writing reports
  • Creating data visualisations

Once you’ve built up your industry experience and a more extensive portfolio you’ll have the chance to apply for mid-level roles. 

As you gain more exposure to the industry’s practices, you’ll be able to apply for senior Data Analyst roles. As a senior Data Analyst, there are normally chances to lead data projects and teams, which means you might even land a formal management role. This involves overseeing data research budgets, as well as managing timelines, workflows and team development. 

Beyond this, high-level work experience and further formal training will allow you to apply for the most senior roles, such as Data Scientist and Data Architect. 

For those with big ambitions, the top of the career ladder can lead to executive positions, such as Chief Technology Officer.

Become a freelance Data Analyst

It’s also worth keeping in mind that Data Analysts often go freelance. Many clients will hire freelance Data Analysts for their unique skill-set on a short-term contract or project-basis. 

When you’re working as a freelance Data Analyst, no two projects will be alike, but there are common skills that clients always look for. 

To kickstart a career as a Data Analyst, it’s vital you undergo practical training that helps you to get to grips with the industry’s latest tools and techniques.  

Ready to bring the power of Data Analytics to your career?

Academy Xi offers a range of Data Analytics courses that are built and taught by industry experts. 

Harness the power of data in your career, even as a non-data professional, with our Data Analytics: Elevate course: 

  • Built and taught by experts in consultation with VisualNoise Data, offering the latest skills used by real professionals.                  
  • Develop practical data analytics skills – retrieve raw datasets, run SQL queries, manipulate data and create stunning data visualisations.
  •  Get to grips with Excel, SQL, Tableau, Google BigQuery and Datawrapper, with the option to explore Python and Google Colab.          
  • Mobilise data in a real-world context – connect your analysis with industry scenarios and find data-backed solutions to real business problems.
  • Set your own brief and use Our World In Data to create a unique final project showcasing your skills.

Upskill and revamp your role with our Data Analytics Pro: Elevate course: 

  • Develop practical skills – use Python to manipulate, analyse and visualise data, communicate with databases using SQL, and make predictions with regression models.         
  • Master the key concepts of data analytics, learn to use data to solve business problems and gain introductory exposure to data science.
  • Spend 50% of your time coding, completing weekly lab exercises and two data analytics projects of rising complexity – one in Python and the other in Data Modelling.
  • Showcase your new skills to employers by interactively narrating your projects in Jupyter Notebooks.
  • Showcase a real-world scenario project to a panel of data professionals – get expert feedback and expand your industry network. 

Change careers with our Data Analytics: Transform course: 

  • Premium course content built by experts in collaboration with the award-winning Flatiron School (New York, US). 
  • Land your dream web development role with CV, job search and interview advice from our proven Career Support Program (97% job placement rate in FY 22).
  • Work with multiple languages, tools and frameworks, including JavaScript, React, HTML, CSS and Git.
  • Spend over 50% of your time coding, with weekly labs and 2 personal projects.

Get to grips with Python essentials and learn to programme custom visualisations with our Data Visualisation with Python: Elevate course:

  • Spend over 50% of your time coding.   
  • Master the fundamentals of Python programming by manipulating, cleaning and analysing datasets.
  • Boost your skill-set by learning to build custom infographics with Python. 
  • Learn to storytell about data insights and secure stakeholder buy-in by presenting a final project.
  • Showcase your new skills to employers by narrating your data projects in Jupyter Notebooks and building a personal Github profile.

Want to discuss your transferable skills and course options? Speak to a course advisor today and take the first step in your Data Analytics journey.

Academy Xi Blog

Fireside Chat: I’m a Data Analyst–Ask Me Anything

By Academy Xi

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Here’s the transcript of the latest Q&A in Academy Xi’s Fireside Chat series. Data expert and director of VisualNoise Data JP Hwang showed us how to harness data for intel, insights, and impact.

We went on a deep dive on what it takes to be a data-driven workplace and how you can make smarter, data-informed decisions in your everyday work! We also got the chance to answer some of our audience’s burning questions about skills you need to become a data analyst and cleared some questions about our Data Analytics courses.

We had a blast, enjoy!

Academy Xi (Olivia Bowden, event host): Hi JP! Welcome to our fireside chat series. We are so excited to have you with us today. I think I’m gonna hand it over to you to introduce yourself if you don’t mind, let us know a little bit about you, your background, and your journey towards the world of data.

JP Hwang: Fantastic, yeah, thanks Liv. So, my background at university is in engineering and in IP or intellectual property but then I started working on a tech start-up which was all about extracting competitor information and market trends using scientific papers and patent data which is quite information rich and dense.  In that process, I was immersed in data science, machine learning, and programming and all that good stuff.

More recently though, I’ve kind of identified a need in a lot of organisations, where, for many of them, if not most, the volume of data really outweighs the analysis tools and just general capability to do something meaningful with it.

So, I’ve kind of pivoted. What we’ve been doing now is to help organisations make better use of data to help not only analyse it but to better consume the outputs and make better use of it going forward.

Some of the stuff we do and what we have done is things like help with the internal knowledge management systems by building databases and apps to access them and manage them over time. And things like business intelligence apps for analysing customer feedback or customer feedback on various products and groups. Or even doing cool things like analysing video feeds to extract data information in real time, so you can provide real time feedback. 

I have been working with clients small and large from start-ups to fairly big companies and, more recently, obviously we’ve been working with you guys at Academy Xi to create the Data Analytics Course. It has been a really rewarding experience empowering people in that space as well.

Academy Xi: I remember when we first got to know one another, I had a look at your LinkedIn profile, you haven’t always been in data, this is something you’ve been moving towards in the past couple of years. Do you want to take us back a little bit and sort of tell us a little bit more about your journey? 

JP: Data science is an interesting field, because it’s a new field–it kind of combines statistics and computer science. It’s a new discipline so I’ve come from an engineering background, so I’ve got a technical background. I’m used to coding all my life basically. And then I worked as a patent attorney for a few years as well and that’s where I kind of the two worlds came together in terms of programming and the IP space and I was working on that start-up right, so it was kind of all about leveraging the gigabytes or terabytes of patent data to do something with it and make it easier to extract intelligence and information out of that.

And that’s the start of the formal, I suppose, move into the world of as a developer, and as a data scientist. 

That’s been unexpected, but it’s also been really fun and rewarding because it’s a new field; there’s a lot of exciting developments going on and I’m sure you’ve all heard the discussions going on about AI. You’ve probably seen bots, you know chatbots, or whether it’s language generating bots. Even these days you’ve probably seen pictures from things like DALL-E that generates images based on the prompt you’ve given them.

And there’s a couple of things really cool about that, which is learning how to use these tools which we’ll talk about a bit later. But the creative prompts that people give those AI bots are really interesting because there’s an art to using them in terms of giving the prompts the right way.

Academy Xi: What I think is great about your story as well, and I think a lot of people on the chat will resonate with is that you haven’t always been a data scientist. That it is possible to transition and take those transferable skills and carve a career out for yourself in data.

But we’re also going to have a lot of people on this call, presumably like myself, who aren’t too overly familiar with data at all. 

I guess the first question that comes to my mind is, why do all of us need to know or care about data? 

JP: I think that’s a good question and over the last couple of years, the word data has taken on this kind of a fairly mythical and intimidating quality. So let’s just kind of talk about what it is right? 

I guess the more boring definition will be that data is just information that’s been collected and quantified. That is any kind of information that’s been written down essentially or recorded in some way, even if it is not particularly meaningful or necessarily even important nor you might not see what the hype or the fuss is about.

But I think, where we can all agree, though, is that being able to understand the world around us better and making higher quality decisions is really, really important and desirable. 

Data allows us to do exactly that and the rise of things like Machine Learning and AI in terms of having bots, for example, that can look at our radiology pictures and come up with a really accurate diagnosis that outperforms physicians is all enabled by data. 

So it’s really those enablers and the way to leverage data to be better informed, to make better tools and to make better decisions. I think those are the reasons to really care about data. 

That’s a long way of saying that the Data Analytics process is all about answering questions about our world with data so that by the end of it we give ourselves a better chance of making better decisions than we would if we hadn't done that.

JP Hwang

Academy Xi: But in the enterprise side of our business, we have been hearing about how we can make our people or allow people or empower people to make more data driven decisions. Have you got any examples you can think of, in a corporate context where people could be using data just to do their everyday tasks a little bit better? 

JP: In professional capacities, if you’re doing things like capacity planning, you’d be looking at things like your seasonal and annual data from your historical perspectives and you’d have data about what your product release cycles might look like; and how that affects your capacities. 

If you’re an R&D manager in a tech firm or a research firm of course, you could look at scientific publications and patents to get trends and see what your competitors are doing.

And one of the biggest changes in the last 10 years has been doing things like AB Testing in a lot of domains right? That’s websites, that’s marketing–and that’s extremely data driven in that we can do that. To be able to systematically analyse it to make better decisions on what people want: what kind of interfaces work better and what kind of workflows work better for our customers. 

Academy Xi: Yeah fantastic. It would be amiss for us to have a data expert here and not actually talk just briefly about what the data analysis process looks like for somebody who’s a specialist–say a data scientist, a data analyst? Can you explain what that process is all about, and then we can drill down into other areas? 

JP: Yeah sure. So, Data Science and Data Analysis is such a big field, I won’t pretend to kind of encapsulate all of that in one go. But I can talk a little bit about what data analysts do which is, I guess, a little bit more defined in terms of using data to answer organisational questions. And to come up with analysis that can be leveraged directly in an organisational perspective. 

Typically, the process begins from defining the problem for the analyst and that might be internal or external, meaning translating the business goals and objectives to specific data driven analysis goals, and these are typically SMART goals right? In the same way that we set SMART goals for any other things that we do. 

Move forward then of course to the process of analysis which I won’t go through in detail because it’s quite dry, to be honest. It’s all the steps of gathering data, cleaning data, exploring and then finally performing the analysis to come up with insights from our data.

And the last step is really the key, one that non-technical people or non-data professionals would really engage with a lot, is really making use of the data and effecting change. That might be communicating those outputs to the stakeholders to help them make better decisions or it might be you consuming those insights yourself to act, or just be better informed about the world around you.

That’s a really long way of saying that the Data Analytics process is all about answering questions about our world with data so that by the end of it we give ourselves a better chance of making better decisions than if we hadn’t done that.

Academy Xi: Yeah, fantastic. In terms of actionable insights and giving people tips on how to do that and I understand from what we’ve talked about, there are three key pointers that you’re going to be taking us through today? 

JP: We’re going to start by talking about developing your data intuition. For a lot of non-data professionals, the key part of your data literacy skill that you should be focusing on is really the reading and comprehension part of data literacy. What that means is being able to understand, internalise, and make use of data driven outputs right.  So, as we’ve been talking about the volume of data collected and data driven outputs, many of you might already be dealing with these outputs. So you probably heard about reporting terms like ‘confidence intervals’, ‘margins of error’ and then all the talk about what a model can do. 

The good news is most of you will never need to calculate these statistics or develop any models yourself. What you will be doing though, going forward is to be asked to consume these outputs and to integrate them to your work. That’s why developing your data intuition, which is a gut feel for what those data points tell you, and how you can use them is important. 

Academy Xi: This is super interesting JP and I’m just going to pause here to say we had one of our attendees pose this question: “Hi, I’m a marketer wondering about instinct or gut-feel versus being data driven, do both have a role to play in better decision making? When do you need a balance of both?”

JP: What people think of gut feel or instinct is interesting because that’s basically your brain having internalised patterns that you’ve seen. So, it’s not really codified in terms of hard numbers but there’s quite often something to that. So how those insights and gut feels, or those intuitions get used might have been a process that you would form some hypothesis about your world, what’s happening with your customers, and what have yous using those gut feels.

And then, what you might do is to develop a Data Analytics process based on it to test the hypothesis right. To see what is happening. How likely is it that it’s happening if you make improvements, based on your findings.

How you leverage these insights and quite often, of course, it turns out to be not true because we all have internal biases. 

Academy Xi: To that person, say we thought about a customer service team manager, so how, in your opinion, would this kind of person develop “data intuitions” in their role? 

JP: Yeah, that’s an interesting point, and of course it depends on every role. I won’t pretend to be an expert on facility managers, forgive me if the example’s not perfect.

But, as I was thinking about it, I think it would really help them in designing the workflow in terms of how to use data and how to engage with people. So, let’s say a customer service team manager has some thoughts about how to improve their workflow for the customer. Knowing what types of experiments to conduct to find out more about this, how many people, they’d need to survey or get samples on whatever how much money it costs as a result. And then once you get the results back. What you do with it and do what you know to interpret it so they can make sense of it.

But then, if you just had a general sense of what’s possible, once they understood what AB testing is and how it worked and how those two things you conducted and who to talk to will really help them get started.

It’s the same thing with integrating things like chatbots, which is, I think, is used a fair bit in the world of customer service. We kind of see that a lot on websites and quite often you’ll see that pop up and it’s not a real human, you’re just talking to a chatbot.

Chatbots are cool but they can’t do everything right? So, if you understand the kind of things they can do well, which is things like, question and answer, and retrieving certain parts of information they’ll be able to then look at their data and say, “well how much work for the representative is that? How much would introducing chatbots help to our workflow? How much would it cost? They can do that cost benefit analysis much, much more easily rather than just if you didn’t understand what chatbots did or couldn’t do. It’ll be hard to make that comparison and think about whether it’s worth integrating tools like that to your work.

Academy Xi: Moving on to the second one, because this for me is always a fascinating area. I think we’ve all been privy to some dodgy confusing data visualisations in our time so dealing with data visually this is point number two.

JP Hwang: I do a lot of work in data visualisations just because it’s I think an essential tool for those who work with data, kind of day in, day out. But also, for consumers of data, the thing is that we simply consume a lot more information and faster when it’s visually encoded. If you had a table and tried to make sense of any of it and what’s the pattern and there’s thousands of rows and that, but if you see a graph, you can see patterns on that clearly, so that’s one thing one can benefit about data visualisation.

Another good thing about visualisations is that unusual patterns reveal themselves quickly amongst thousands of data points, and this is a data and looking at.

Academy Xi: I guess at the end of the day, this is about clear communication and setting yourself up, I guess, with some simple tools to help you do that, which links us to this question from our audience: “What software tools can one learn to be an expert in data visualisation?”

JP: There’s a few depending on your workflow and how you use data visualisation. Some of the more popular ones like Tableau, Power BI where you’re using desktop software. And then there’s online tools like DataWrapper or what’s called Flourish.

For someone like me, I obviously do a lot of my work in the programming space, I’ll use programming libraries like Plotly or Bokeh or even something called D3. So it’s really horses for courses in terms of what you’re doing and what you’re trying to present.

In a lot of cases, if you’re just creating charts, simple charts for presentation purposes, something like Excel is really good enough because it’s not really about creating the most fancy charts and the most complex charts. But it’s about really communicating your message through whatever means necessary.

Academy Xi: Lets loop back to that customer service team manager. How would you envisage they use visualisations to communicate data clearly?

JP: There’s two sides of that right. So as consumers of data visualisations it’s important that they be able to communicate with the stakeholders or service providers who might be creating these visualisations, so you understand what it is that they’re telling them.

Quite often, though, depending on where you’re at in an organisation or what your role is you, quite often will be in the role where you must communicate your findings from your studies to stakeholders higher up.

That means selecting some charts or even creating some charts yourself to communicate your message and key takeaways to your stakeholders. In that case you’d want to be able to make at least some charts and it’s not probably as intimidating as it feels for a lot of people to learn how to create these charts.

And, as I mentioned, those online tools like Data Wrapper or Flourish that I really like, are very easy to use. If you know how to use Canva, you can probably use a lot of that too.

It is about empowering yourself and as well as others in the organisations with tools, right? There was a stat that I read, which was mind blowing and it said that “when people are faced with data-related tasks about half the people would either avoid the tasks or not use the data provided at all.”

So that’s quite interesting. And on top of that, about three quarters of the same respondents said that those tasks made them unhappy or overwhelmed. That’s a really striking stat for me because that means for half of those people, they’re not using the data. So they’re not any better off than they would have been if they hadn’t been provided with the data.

But now they’ve been left kind of disenfranchised or unhappy, and disempowered–sort of feeling like they’re not capable of doing this. Data, for those people, made their lives and work worse than if they hadn’t had that in the first place.

One of the key recommendations here is to kind of really empower yourself with a tool that can add value to your work, and it doesn’t have to be anything super fancy like a programming language. And it can be something basic like just learning how to use a couple of formulas in Excel or load up data in Excel or Google Sheets, it might be something like Tableau. Or even those online tools like drawing charts in Canva or Data Wrapper, as I mentioned, and that’ll be really quite empowering.

It depends on what your work is and what your relationship with data is, but I think it can be a kind of transformative experience.

Below are questions from our audience ranging from career, salary, software, skills, and more. 

Audience question: What is the difference in job profile between a data scientist and data analyst? If there is a difference, what should a data scientist do in the data analytics field?

JP: One of the things that we wanted to do at the start of our data analytics course was to disambiguate between these two terms between data analyst and data scientist.

The truth of the matter is that they are fairly nebulous terms, but I’ll try and do what I can in terms of the best of my understanding.

Data science is generally a broader term than data analysis. We talked about the process of data analysis in terms of problem definition, all the way out between data cleaning, data managing and analysis, all the way to delivery of data communications.

Data science tends to be a little bit broad, broader (excuse me), and a lot of what data scientists do is things like develop models.  You know if you’re talking about people who develop state of the art models in image recognition. Sorry, I’m getting fairly technical. Like if you see those bots or a website where you’re putting (uploading) a picture and it tells you what it is.

Or, if you’re driving a Tesla and it’s able to drive around and figure out if they’re driving on a road or if they’re about to run into another car or follow lanes and stuff—these are all driven by AI models developed by data scientists

That’s probably how I understand data science and data analysis.

And in terms of what should data scientists do to get into a data analyst field, I think some of the skill sets are slightly different. Data analysts tend to work with things like SQL a little bit more. They’re dealing with kind of more predefined roles and analysis pipelines because they’re more often in a business role answering defined business questions with these tools.

Whereas data scientists’ roles, like building models so it tends to be a little bit different from that sense.

Audience question: We often hear from L&D managers, ‘look, our executive has said, our people need to be more data literate and make more data driven decisions across the board, where do we even start? People have varying levels of competency already, how do we even begin on this journey?

JP: There’s a couple of things that can do that can help, obviously, one thing is just kind of reducing that intimidation factor in dealing with data. I think a lot of that is going to be just training and continued development for those people and supporting them throughout.

In terms of how to figure out where they’re at in terms of their comfort with data and abilities, I think it’s a good idea to have conversations with people about how comfortable you are in dealing with these tools that might be useful on top of or in your day-to-day tasks.

And then you can ask them questions like, “how comfortable are you with Excel, formulas, pivot tables and VLOOKUP formulas and so on and so forth?”

It might be that you’re dealing with databases all the time. How much do you use SQL you know? It’s probably quite intimidating to be formally assessed in something, but I think a lot of training would be a really good way forward for many organisations and it can be a collaborative process to put them in groups and work on kind of relevant business problems together.

Audience question: Do data scientists need to have knowledge of statistics and what level of it do you need to become a data analyst?

JP: I guess by nature if you’re dealing with data science and data analytics types of fields, it is undoubtedly going to be computer heavy so levels of computer literacy is really helpful.

But I guess the question is, can someone without an IT background join the data world, I think the answer is yes, none of us are born with these skills, right? These are not innate skills that are encoded to our genes or anything so for sure it is something that we can learn.

And I’ve seen people do it in the Data Analytics: Transform because a lot of the students in our cohort that I’m mentoring come from a non-technical background, I would say, actually, most of them come from non-technical backgrounds. And a lot of them pick it up really well and they do fully admit that it is not easy, but it’s something that they pick up and they do really well so that’s great to see.

In terms of statistics, there’s two parts of this question I can see, so I’ll try to answer the first part about the knowledge of statistics. Depending on what you’re doing in data science. If you’re building models and using machine learning to do things like regression models which is when you predict things like numbers, so when you predict things like housing prices, based on where it is, how big the houses are, blah blah blah. Or if you’re using models at any level you want a fairly good intuition of statistics and maths.

But if you actually work on some of these fields, what you’ll see and what’s required isn’t a university graduate level of statistics or algebra or calculus. What you do need is basically a low level understanding of some statistics and some algebra and that’s kind of all you need.

If you of course, are doing some role that is fairly statistics heavy and that might be in the fields of experiments where you are required to do things like, “does this experiment tell us with a certain degree of confidence that this drug is going to work”–or something like that those fields are, of course, more statistics heavy and critical, so it really depends.

Audience question: Is it worth doing a master’s degree in this field or would the Elevate course or Transform course offered be enough to become a data analyst or data scientist?

JP:  That’s a really good question. I don’t feel particularly well placed enough to say whether masters is worth doing as that’s a fairly personal type question.

The Data Analytics: Elevate course is designed for primarily non-data professionals trying to upskill and sort of become more familiar in dealing with data and to empower them as we mentioned and to give them a flavour of what a professional data analyst might do.

That’s why we have optional modules and things like using Python, which we encourage people to look at, but it’s not part of the assessment or anything like that. And we do the same thing by giving them a flavour of things like machine learning and to show them what they can do.

The Data Analytics Transform course is meant for exactly that, for people who are trying to get into the field of data analytics. I guess that’s the intent of it. It’s an intensive course and I think it’s designed to be around 15-20 hours a week, you know for 14 weeks part time. That is what it’s what it’s intended for, and it covers the gamut from learning how to use Python. Using statistics and ending up with being able to build regression models, as the capstone project, where you can pick a subject of your choice or topic of your choice build regression model out of it and be able to really complete that loop, I suppose, from problem definition stage to driving some insights and communicating it out to external stakeholders.

Audience question: If you don’t have a data analyst in the business it’s hard to get the data you want extracted, any tips on how to implement a data analyst into an organisation?

JP: I think it goes twofold: I think it’s important for data analysts to understand what the business objectives are, what their priorities are and how they work.

And conversely, it’s important for the organisation to understand what data can tell you, and what the data analysts can do for you and what they can’t do. So, understand the limitations. That’s because it’s important for data analysts at the problem definition stage to clearly understand the objectives as far as what the business objective is. So that they can translate that into actionable goals.

If that doesn’t happen necessarily well, what happens is that the business stakeholders would provide a brief and that necessarily doesn’t get translated into a data analyst’s understanding. They go off and do the work, get some results back and it turns out that it doesn’t necessarily answer the question that they were looking to answer; or it’s not particularly actionable.

And from the data analyst perspective, quite often the complaint I hear is, “well they asked me to do this thing that’s not actually possible so now, I have to manage their expectations about what is actually possible with data, as well as to try and answer this question some way”. So if they have an understanding of the limitations of data and analysis they can obviously integrate the data analyst or team better into the organisation and make better use of that resource.

Audience question: Do you have a structure when communicating the insights you’ve garnered from data to stakeholders?

JP: Yeah, that’s a good question. I think of data communication as similar to a part of rhetoric, so when you’re trying to convince someone or change their minds on something, the data should really be designed on backing that up.

A good place to start would be to think about what your goal was when you started the data analysis and kind of anchoring everything back to that.

In terms of this structure or the methodology it doesn’t have to be something complex, so you might see some beautiful data visualisation online or on Twitter—I see and I follow a lot of these people—so I see a lot of that, but then you kind of look at it and even for me who is quite used to them, I kind of go oh that’s really pretty but I have no idea what that says, but it’s really pretty.

So sometimes just a table with like three numbers might be better than a very complex beautiful data visualisation because it tells a clear message.

So that’s my way of saying that, whatever does the job is good. It always is what you want. And, I know I said less is more in terms of visualisations but it fits for the right audience, you can actually do more complex things.

For certain clients of mine, I’ve built them some dashboard apps that look at data that connects to their data pipeline. But what that allows the audience to do is to dig further into the data themselves. Those types of outputs tend to be a little bit more complex but because they have the expertise in their domain and because they are a little bit more data savvy, they’re able to dig into the data themselves. They won’t be performing the analysis and cleaning the data and so on and so forth, and building those models; they can look into it and answer their own questions as they go.

Audience question: I am a junior marketer and hoping to be able to make use of marketing data to optimise marketing decisions and campaigns, however, I don’t come from a technical background, so I wonder which course I should take in terms of elevate versus transform?

JP: I think, for me, the Elevate course would be preferable in your use case. It is designed around non-data professionals looking to upskill and really understand more about the world of data and data analytics at answering questions using data. That’s my two cents on that.

Of course, if you’re looking to challenge yourself, I wouldn’t discourage you from taking that Transform course, but my recommendation is to do the Elevate course.

In the Transform course we do cover a little bit more of things like statistics and hypothesis testing and A/B testing and how to analyse results of that more clearly. If you’re specifically interested in those areas, you might look into the Transform course. It is significantly more challenging because it is programming heavy.

Audience question: I have a background in tech sales but am quite curious in terms of how I can use data. I’m thinking of reaching out to our marketing team in house or other departments to see where I can add value. Any tips on what tools I can leverage to work on during my own time?

JP: A lot of things that people do is try and build their online portfolio in data science and analysis and there’s a lot of data out there that’s good – really, really high quality and publicly available. What people do is to source some of that data in whatever domain you’re interested in and build a portfolio of analysis and build your publicly available profile.

Now you can do a similar thing with your organisational data probably too depending on what is available to you, given your role and how accessible that data is. It is always always useful to solve a problem that the organisation has and quite often what they’ll have is things like there’s a lot of data in this field and they’ve collected it, for whatever reason and they probably haven’t had time to do anything with it, and so they can probably give you a background on what they were thinking when they collected this data. And why they haven’t actually done anything with it and it might be something as simple as they have collected all this data but it’s quite messy.

So it needs someone to go through that and clean it and that’s a really valuable process. There’s a running joke that people think data science is extremely glamorous and you’re building these AI bots and whatever. What you’re actually doing is just cleaning data and getting rid of typos like 90% of the time and that’s more true than people actually think.

So, you know I would say talk to people who have data in your organisation and talk to them about the background, as to why. Talk about what their needs are, and you can probably help them to address some of that and it’s a good way to build your and to network as well with the parts of the business.

Audience question: How do people in an organisation best work with specific data teams? There are often teams of specialists who are excellent at what they do, but how can others in the business really tap into that resource in an effective way?

JP: I think it’s about engagement and about data literacy. I think quite often what you see in organisations is that perhaps they’re a little bit too siloed in terms of what they do.

So really engaging with them, talking about what they do, and then that also helps them understand what you do and what your needs are as well.

I’ve spoken to people who are doing things like rolling out tools like Tableau in the organisation. They said getting to engage throughout the organisation, because they were rolling out these tools, helped them to speak to different parts of the organisation that they otherwise wouldn’t have to.

Kick start your career and gain in-demand skills with our 100% online Data Analytics courses.  

Academy Xi Blog

FAQs: Graphic Design

By Academy Xi

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We’ve compiled this list of questions most frequently asked about Graphic Design to help you understand what it’s like to make a splash as a Graphic Designer.

Graphic Design and why it’s important

Graphic design is the practice of creating high-impact visual content that informs, inspires and entices a target audience. Fusing traditional art and tech capabilities, modern Graphic Design serves print and digital media, and can be accomplished by hand, with computer software, or a combination of the two.

Your day-to-day life is filled with graphic designs, from the branding on your coffee cup, to the eye-catching billboard on the train platform, and even the news app you read during your morning commute.

Though Graphic Design is widely used by businesses to promote goods and services, it’s also considered a form of artistic expression. The beauty of Graphic Design is that it combines commercial problem-solving with true creativity.

As you’ve probably already guessed, Graphic Design goes way beyond aesthetics. It acts as a vital point of connectivity between a business and its target audience.

Businesses rely on graphic design at every stage of the marketing funnel, using it to streamline communications about a product or service, distinguish itself from the competition and convince customers to make a purchase.

When it comes to building a brand, the right graphic design will elicit an emotional connection, giving people ‘reasons to care’ and increasing customer loyalty.

Everything you see of a business, from an online advertisement, to a social media post, or even a product’s packaging, is the end result of a strategic Graphic Design process that’s intended to enhance a company’s visibility and tell a compelling brand story.

Graphic design is a multifaceted field and no two graphic design roles will ever be the same. That said, there are certain responsibilities that most professional graphic designers perform. These include:

  • Meeting with clients or art directors to define the scope of a project
  • Using photo editing software, layout software and digital illustration to produce designs
  • Selecting colours, images and typefaces to communicate a company’s brand and message
  • Presenting design concepts to clients or art directors
  • Revising designs based on stakeholder feedback and opinion
  • Proofing final designs to ensure there are no errors before printing or publishing

Design theory, skills & software

Design theory is a system that helps designers understand why certain visual concepts ‘work’ when conveying visual messages. This involves breaking down the different elements in an image, explaining why each element is important and using design principles to determine how their effects can be maximised.

Rather than being guided by creative instincts alone, design theory allows a designer to predict how certain stylistic choices will influence a viewer’s reaction.

Applying design theory is all about asking and answering the question “why am I designing this way?”. If you’re designing and find you can’t answer that question convincingly, you probably need to do a bit more thinking!

Explained in the simplest terms, Gestalt theory is based on the idea that the human brain will attempt to simplify and organise complex images or designs that consist of multiple elements. 

By subconsciously arranging an image’s parts into an organised system, our brains instinctively aim to create and perceive a whole (rather than experiencing a design as a cluster of disparate elements). 

In response to gestalt theory, Graphic Designers will implement the five design principles to ensure their images are viewer-friendly.

Following the five principles of alignment, repetition, contrast, hierarchy and balance not only help Graphic Designers craft beautiful content, but also increases the accessibility of a design, ensuring it serves its intended purpose and makes a lasting impression on the viewer. 

The five principles are:

  • Alignment – Aligning the elements of a page creates a unified design and can be used to establish visual connections. Most importantly, it brings a sense of order to your design, which makes for easier viewing.

  • Repetition – Repeating certain design elements can build familiarity and create strong visual associations. Graphic Designers use repetitions to formulate a consistent brand image that can be quickly and easily identified across all its visual content.

  • Contrast – It’s possible to create contrast by combining two elements that are complete opposites, such as a classic and contemporary font, or warm and cool colours. This generates impact and can increase a viewer’s emotional response to an image. 

  • Hierarchy – Grouping similar things close to each other implies that they are related in some way – hierarchy is fundamental in creating organisation in a design.

  • Balance – this refers to how the elements are placed and distributed across an image. There are two kinds; symmetrical balance and balance by tension, and both can bring stability and structure to the layout of a design.

Added together, all five design principles ensure a design is visually appealing and formulated in a way that promotes legibility and the viewer’s comfort.

Colour theory clarifies how and why designers should use a specific colour or colour palette, and is based on the idea that people often have particular and predictable responses to certain colours and colour combinations.

Colour theory can be used by Graphic Designers to stimulate an emotional response that entices or persuades an audience. For example, brands that use the colour red in their designs (Coca Cola, YouTube and Netflix) tap into its associations with passion and strength, effectively demanding our attention.

There are a raft of essential skills that a Graphic Designer needs to produce eye-catching, functional assets. Here are a few of the essential capabilities that Graphic Designers rely on to thrive in the industry: 

  • Working with briefs

To make sure you clear the first hurdle, it’s vital that you’re able to effectively interpret and respond to Graphic Design briefs. This might entail working with too much information, or even too little. Just as no two clients will be the same, every brief will be unique, and the best designers approach each brief with a fresh mindset.

As a Graphic Designer, it’s your responsibility to understand the client’s specifications and the problems they are hoping to solve. In order to find effective long-term solutions, the best designers will religiously refer back to the original brief throughout a project.  

  • Illustration

It’s liberating to know that you don’t have to be a professional illustrator to be a Graphic Designer. That said, Graphic Designers do need to be able to explore different design concepts. 

One of the best ways to formulate your Graphic Design ideas is through sketching. These sketches are a visual means to get ideas down on paper, and don’t need to be perfect.

That said, there’s always an advantage to being able to illustrate to a high level as a Graphic Designer. Today’s illustrators use pads and tablets (such as Wacom), which come with built-in pressure-sensitive features, enabling you to intuitively sketch, draw and paint with complete precision. 

  • Still and motion graphics

As well as working with still images (digital or print), such as posters and magazines, contemporary Graphic Designers incorporate movement into their creations, at which stage Graphic Design becomes motion graphics.

Motion Graphic Designers produce assets for the web, television and film, using visual effects, animation and other cinematic techniques to breathe life into their designs. 

With the increasing popularity of online video content, you could find yourself working with motion graphics on almost any platform, from a catchy YouTube ad, to a short and snappy TikTok video.   

  • Communication

As well as expressing ideas visually, Graphic Designers need to have excellent communication skills when dealing with clients, stakeholders and teammates. 

Being able to confidently pitch concepts is essential for attracting new clients, while maintaining positive interactions with any existing clients ensures projects reach a satisfying outcome. 

Ideation and planning are often collaborative exercises, so you’ll need the soft-skills to share ideas, brainstorm and work as part of a synchronised team.

  • Portfolio building  

It almost goes without saying that a stand-out portfolio is a must-have for any graphic designer (or, for that matter, anyone working in a creative field!). A portfolio displays the originality of your Graphic Design brand and serves as a platform for potential clients to view your work. 

A well curated portfolio is your most important piece of advertising, concretely demonstrating your talents and work experience, while inspiring clients with a vision of what your skills could do to improve their business.

Graphic Designers rely on the ever-expanding possibilities of software to bring their ideas to life. Because of rapid tech innovations, designers have more software to choose from than ever before, enabling them to combine different forms of media and create attention-grabbing interactive content. 

Designers need to be fully aware of the strengths and weaknesses that come with each piece of software, which enables them to choose the best tools for the job in hand. 

Today’s industry is largely driven by the Adobe suite, which has developed side-by-side with Graphic Design practice. 

Here’s a breakdown of the essential Adobe apps that power-up the work of modern Graphic Designers:

  • Adobe Photoshop

A must-have for any serious Graphic Designer, Photoshop is your go-to application for working with pixel-based images for print, web, and mobile apps.

Photoshop’s powerful editing tools let you correct exposure and colour balance, crop and straighten images, erase blemishes from a portrait, or combine multiple images to formulate a completely new scene.

  • Adobe Illustrator

Adobe Illustrator is a software application for creating drawings, illustrations, and other vector-based designs. It offers designers a comprehensive toolkit that enables them to create designs from scratch, just as they would on a drawing board.

Used as part of a larger design workflow, Illustrator helps with the creation of everything from single design elements to entire compositions. Graphic Designers use Illustrator to create posters, symbols, logos, patterns, icons and much more.

  • Adobe InDesign 

Adobe InDesign is the industry-standard layout and page design software used in print and digital media. Graphic Designers use InDesign to create and publish books, magazines, brochures, posters and customised stationery.

InDesign offers a wide range of typography from the world’s top foundries and imagery from Adobe Stock. Its latest features allow designers to create interactive publications, such as iPad apps, interactive PDFs, online magazines and eBooks.

  • Adobe After Effects

Adobe After Effects is a digital visual effects and motion graphics application that’s used in the post-production process of film, television and web video production. 

After Effects can be used to create animations, layer text over images, and edit and composite footage. It also allows designers to create special effects, such as adding dramatic lighting, making moody colour changes, or layering up patterns and textures. 

  • Adobe XD

Adobe XD is a prototyping tool for user experience and interaction designers (both of which Graphic Designers regularly delve into). Adobe XD features are handy for creating wireframes, prototypes, and screen designs for digital products, including websites and mobile apps.

  • Adobe Premiere Pro

Premiere Pro is industry-standard video editing software, used for film, TV and the web. Intuitive tools and Premiere Pro’s simple integration with other apps helps users composite clips, create smooth transitions and turn footage into polished videos.

  • Adobe Animate

Animate allows designers to produce high-quality vector graphics that are scalable, reusable and adaptable for cartoons, banners, games and other interactive content. 

Animate graphics can easily be imported to After Effects, allowing you to add post-production effects and publish your animated videos through multiple platforms with just a few clicks.

 

Career paths in Graphic Design

Becoming a professional Graphic Designer opens a career full of opportunities, rewards and personal fulfilment. It’s a passion job that allows you to solve problems you care about and shift perspectives through powerful visual communications.

As a Graphic Designer, there are a range of pathways that you can choose to follow, depending on your professional goals and lifestyle preferences. These include:   

  • Becoming a permanent in-house Graphic Designer for a business or organisation. 
  • Working for a design consultancy or agency that carries out design projects for a variety of clients.
  • Going freelance or self-employed and effectively running your own Graphic Design business.
  • A combination of the above.

With the nation’s freelance employment market currently growing three times faster than the employment market as a whole, Australia offers an abundance of opportunities for freelance Graphic Designers.

There are a number of benefits that come with striking it out alone as a freelance Graphic Designer. Some of the biggest perks include:

  • Choosing your clients – you’ll have the unique ability to select the clients you work with. You might pick clients based on their brand image or stellar reputation, or because of a personal affinity with a particular product or service.

     

  • Managing your workload – Work as much or as little as you like; if you want to work full-time most of the year and only part-time during the summer months, you’ll have the flexibility to make that a reality. Because freelancing is often remote, you can work anywhere wifi-connected.

     

  • Diversifying your exposure – you’ll get the chance to work on projects for clients in a variety of industries, enabling you to broaden your horizons, diversify your professional exposure and build a specialised portfolio.

Industry demand for Graphic Designers

With more businesses than ever vying for visibility, Graphic Designers are in high demand worldwide and Australia is no exception. The nation’s Graphic Design industry is forecast to grow by 12.9% throughout the next five years.

Seek is currently advertising over 16,500 Graphic Design roles in Australia alone. A Graphic Designer’s skills are universally sought after, so wherever you end up in life, you’ll have a CV that employers are searching for.

The pay opportunities for Australian Graphic Designers are representative of an industry that’s growing fast and on the lookout for more capable professionals.  

Talent.com records the average Graphic Designer salary in Australia at $82,758 per year. Even entry-level positions start at $68,629 per year, while the most experienced designers make up to $107,121 annually. 

Added to this, a career in Graphic Design is acknowledged by those already in the profession to be highly rewarding, scoring 4.1 out of 5 for employee satisfaction on Seek.

Becoming a Graphic Designer

Believe it or not, learning Graphic Design is not hard, but does require creative thinking, a basic level of aptitude towards art and design, and at least a little familiarity working with tech and digital platforms.

It takes time and dedication to understand and apply the principles of design theory, while you’ll also need plenty of hands-on practice in order to get to grips with the necessary software.

Many Graphic Designers will specialise their skillset on the job, but it’s important to start with a solid foundational grasp of the essential tools and techniques.

The chances of anyone learning these alone online are slim, so anyone keen to enter the industry should consider earning a formal Graphic Design certification. Without this, it’s difficult to get hired into that first role or attract clients, since so many other designers will have a proven, certified skillset.

There are many options when it comes to qualifying as a Graphic Designer. You can undertake a Diploma of Graphic Design, which can take between 1- 2 years to complete.

Traditional universities offer Bachelor of Graphic Design degrees, which normally take 3-4 years to complete. Some Graphic Design degrees tend to spend a disproportionate amount of time covering Graphic Design theory. While understanding the principles of effective design work is crucial, most people will only fully absorb the theory by putting it into practice.

These days, there’s far less expectation for Graphic Designers to be university qualified, with most employers and prospective clients prioritising skills, experience and a good portfolio above formal qualifications.

As a result, more people are enrolling in condensed bootcamp-style courses, which leave graduates industry-ready in a much shorter period of time by focusing on the tangible skills that today’s Graphic Designers need most.

 

Whether you want to venture into a new profession as a Graphic Designer, build your own money-spinning design business, or upskill and test the waters of a design career, Academy Xi offers a range of Graphic Design courses suited to your goals. 

  • Graphic Design: Transform – For those who want to kickstart a new career as a Graphic Designer, including 24 weeks of access to a Career Support Program that helps 90% of graduates straight into industry.

     

  • Graphic Design: Elevate – For those who want to boost their career with in-demand Graphic Design skills.

     

  • Graphic Design: Elevate (self-paced) – For those who want to boost their career with in-demand Graphic Design skills, while also enjoying the flexibility of self-paced learning. 

Not sure which course is right for you? Chat to a course advisor and we’ll help you find the perfect match.

Thinking a Graphic Design course could be right for you?

Get in touch with our Course Advisors to discuss training options or, if you’re ready to go, simply enrol now.

Academy Xi Blog

FAQs: Data Analytics

By Academy Xi

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We’ve compiled this list of questions most frequently asked about Data Analytics to help you understand what it’s like to work with data.

Already know you’d like to study Data Analytics?

Get in touch with our Course Advisors to discuss training options. Check out upcoming intake dates.

Data Analytics and why it’s important

Data analytics is the process of sourcing, cleaning and analysing raw data to identify meaningful patterns, trends and insights.

Insights extracted from data can be used to find answers to all kinds of questions, and solutions for even the trickiest problems.

Data analytics helps us understand the effects of what we’ve been doing, what we could be doing, and any probable outcomes that come with a new course of action.

At its core, data analytics tells us what to do next. The ultimate aim is to use hard facts to make well-informed decisions that help a project reach its goals.

  • Data analysts start by consulting with teammates and stakeholders to define the objectives of a data analysis project.
  • With the informational goals set, they gather and combine data which might come from a range of sources – both internal and external.
  • They then clean, manipulate and reorganise data, removing any outdated or unrelated data and getting ready for analysis.
  • Now it’s time for an analyst to live up to their title, as they spot trends and patterns that can be translated into actionable insights.
  • Finally, they present their insights in a clear and convincing way to inform decision making.

There are three main categories of data analytics that businesses use to drive their decision making:

  • Descriptive analytics – this tells a business what has already happened.
  • Predictive analytics – this helps a business understand what could happen.
  • Prescriptive analytics – this enables a business to make proactive decisions about what should happen in the future.

 

Many businesses have a wealth of raw data at their fingertips, but translating it into actionable insights is easier said than done.

Data analytics helps a business tap into a vital resource and better understand its products, customers and competitors, as well as its own operational procedures and capabilities.

Armed with this knowledge, businesses are able to identify inefficiencies and opportunities. Because data is not an opinion or a theory, it can act as an impartial source of truth when making important decisions.

  • Quantitative Analysis
    Quantitative data analysis is all about analysing numerical data to draw statistical conclusions. To give a simple example, a business could assess the popularity of its products in various parts of the world by comparing sales figures in different nations.

  • What is Qualitative Data Analysis?
    Qualitative data analysis normally refers to analysing text and verbal information, such as open-ended responses to survey questions or user interviews. It can also incorporate images, audio files and videos.

    The same business could perform qualitative data analysis by examining customer surveys, which might clarify why a product is performing well in one country and not another.

While data analysts normally work with structured data to solve tangible business problems, data scientists often deal with the unknown, using more advanced techniques to make predictions about the future.

Data scientists are widely thought of as more professionally advanced than data analysts, and often have a computer science or mathematics background. With enough experience and training, a data analyst could eventually find themselves in a data scientist role.

A data scientist’s role might include:

  • Designing predictive models and machine learning algorithms for mining large data sets.
  • Developing tools and processes to monitor data accuracy.
  • Building custom data visualisation tools and creating advanced dashboards and reports.
  • Writing programs to automate data collection and processing.

Data Analytics skills & tools

Regression modelling is a reliable data analysis technique that enables analysts to accurately identify which variables impact a topic of interest. It helps analysts confidently determine which factors matter most and which can be ignored, as well as how important factors influence each other.

To give a simple example, a person’s weight will definitely increase as their height increases. Analysts look for these kinds of relationships between statistics because they clarify how one factor will affect another, making outcomes predictable.

Because data that sits untouched in a spreadsheet is of minimal value to a business, Data Analysts are responsible for visually and verbally sharing their data insights.  

Data analysts will convert their findings into high-impact visualisations (charts, graphs, tables and other infographics) that make data insights easy to comprehend. 

Our ability to absorb visual information is far speedier than our ability to grasp the meaning of words and numbers. If a picture is worth a thousand words, a clear data visualisation might be worth a million data points.

As well as being able to visualise data, data analysts need to be able to tell compelling stories about their data insights.

Data analysts need to deliver simple, linear narratives that clarify what their data uncovers about a business. This can create an “aha” moment, when a deep insight is fully understood by an audience.

Data analysts don’t need to be bestselling authors, but they do need to tailor their pitch. To secure stakeholder buy-in for a recommended initiative, it’s vital to build a narrative that’s meaningful and relevant to whoever is listening.

 

Data Analysts rely on a wide variety of tools to be more effective and efficient in their day-to-day work. Some of the handiest tools that Data Analysts use include:   

  • Excel

Beautifully simple and perfectly functional, an Excel spreadsheet organises raw data into a readable format and makes it easier to extract insights. With more complex data, Excel allows you to customise fields and perform automated calculations.

  • Python Programming

Python is a multi-purpose programming language that’s popular among data analysts due to its extensive collection of libraries, which are useful for combining multiple datasets and performing complex calculations.       

Python coding skills come in handy at all stages of a data research project, helping data analysts clean, manipulate, examine and visualise data. 

  • SQL

As its name suggests, Structured Query Language (SQL) is a standardised programming language that’s used to retrieve and query data. Because SQL is standardised, it’s easy to understand and learn. 

SQL can be used to access and communicate with large amounts of data wherever it’s stored. This means analysts don’t have to copy data into other applications. Instead, they can instantly start organising and analysing data at the source. 

  • Power BI

Microsoft Power BI is a collection of software services, apps, and connectors that work together to turn disparate sources of data into coherent insights. Whether it’s stored in a spreadsheet or a cloud database, businesses can aggregate data into a single workable data model. 

Assisting data analysis projects from end-to-end, Power BI also has the tools to turn insights into interactive, immersive visuals, which can be shared with other Power BI users throughout an organisation.  

R is a programming language and a free, open-source software library that’s used for cleansing and prepping data, generating statistics and creating visualisations. R’s coding language is simple but powerful and often used by data scientists to train machine and deep learning algorithms. 

R can be used directly and interactively on the web, and also easily integrates with BI software, helping analysts combine a range of critical data.

  • Google Colaboratory

‘Colab’ is a Google Research product that allows anybody to write and execute Python code through a browser. Data can be drawn directly from GoogleDrive, or imported from an external source. It also works as a comprehensive notebook where analysts can write code, run code, see the output and then share the whole process with teammates.

  • Google BigQuery

BigQuery is a cloud-based architecture that allows analysts to query masses of data without the need for a database management system. Analysts can auto-scale their search results up and down, and only pay for the data they process. 

  • Tableau

Analytics teams use Tableau to drill deep into data and then convert uncovered insights into clear infographics. Tableau’s emphasis on visuals makes it a great tool for quickly exploring data and packaging it in a way that’s interactive, collaborative and easy on the eye.

  • Datawrapper

Data from various sources can be copied into Datawrapper, which then converts information into interactive pie charts, line charts, bar charts and maps. These can be embedded into a website and even customised to suit the aesthetic of a particular brand or platform.

Industry demand for Data Analysts

Data analysts are in demand worldwide and Australia is no exception. With big data exploding across all industries, the demand for data analysts is now outstripping the supply of qualified professionals. 

Seek is currently advertising over 19,200 data analytics roles in Australia alone. Data analysts are globally sought after, so wherever you end up in life, you’ll have a skill-set that employers are searching for. 

The pay opportunities for Australian data analysts are representative of an industry that’s growing fast and underserved by skilled professionals. 

Data analyst salary in Australia at $103,380 per year. Even entry-level positions start at $90,992 per year, while the most experienced analysts make up to $129,500 annually.

Becoming a Data Analyst

Believe it or not, being a data analyst is not as academic as the title suggests. You don’t need to be a mathematical genius or a programming whiz to break into the field. However, there is a wide variety of skills that go into being an effective data analyst, and some of them are quite technical.

Many of the most sophisticated skills a data analyst uses can be learned on the job, but it’s important to start with a solid foundation of the essential tools and techniques.

The chances of anyone learning these alone online are slim, so anyone keen to enter the industry should consider earning a formal certification. Without this, it’s difficult to get hired into that first role, since so many other candidates will have a proven, certified skill-set.

There are many options for qualifying as a data analyst. Traditional universities offer Data Analytics and Data Science degrees, which normally take 3-4 years to complete.

These days, there’s less expectation for data analysts to be university qualified, with many employers, including the biggest blue chip companies, valuing skills and experience above a degree.

As a result, more people are enrolling in condensed bootcamp-style courses, which leave graduates job-ready in a much shorter period of time and focus on up-to-date practical skills that the industry needs most.

Data analysts help businesses make informed choices (rather than relying on somebody’s best guess!), enabling businesses to function intelligently and grow. Do you want to bring that level of capability to your career?

Whether you want to become a data analyst, upskill and test the waters of a data-driven career, or simply harness the power of data for your existing role, Academy Xi offers a range of Data Analytics courses suited to your ambitions. 

 

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