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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.

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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  • They then clean, manipulate and reorganise data, removing any outdated or unrelated data and getting ready for analysis.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.

Types of data analytics - Descriptive, predictive, prescriptive

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 Analyst Tools logos

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. 

Talent.com records the average 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. 

Academy Xi Data Analytics courses

Thinking a Data Analytics 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.

Data Analyst Working area

Academy Xi Blog

How to Become a Data Analyst

By Academy Xi

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Data Analytics explores the methods, processes, algorithms, and systems used to extract knowledge from data. Data analysts are investigators, storytellers, and most importantly, problem solvers who use raw data to draw actionable insights.

As the backbone of any well-informed decision, data has become vital in driving the strategy and future roadmap of many businesses. Statista found that the big data industry is expected to be worth US$49 billion in 2019, and by 2027 is expected to reach US $103 billion worldwide.  

As more industries become heavily data-reliant, new opportunities for data science roles have emerged including; data scientists, data analysts, data architects, data engineers, statisticians, and database administrators.

A snapshot of the data analytics industry:

  • Over 300,000 people were employed in data analytics in Australia between 2016 – 2017
  • Annual forecasted growth in the data industry is expected to be 2.4% YoY by 2022
  • 76% of companies expect to invest more in data analytics capabilities over the next two years
  • Forecasted average income for a Data Analyst is AU $130,176 by 2022.

How data analytics provides business value

With data becoming the new currency for business decisions and strategic roadmaps, some key benefits of data analytics include:

  • Helping businesses define decisions and goals: By dissecting previous performance, businesses can use data to prioritise their goals according to highest importance and optimal results. Rather than making decisions based on gut, data can prove what has worked well within a business and define what future goals they should work towards.
  • Adoption of best practices: Applying analytics to the design and control of processes enables businesses to optimise their activities to fulfil customer expectations and achieve operational excellence.
  • Test informed decisions: Effective data collection enables businesses to stay competitive by testing and validating informed decisions and anticipating market demand.
  • Reduce risk and fraud: Data Analysts are able to identify data patterns that can be used to make frameworks to detect fraud. These alerts help businesses track unusual activity and respond in an appropriate timeframe.
  • Deliver personalised experiences: By using data to tell stories, Data Analysts are able to empower sales and marketing teams to better understand their audience. With increased knowledge of buyer behaviour and motivations, organisations are then able to make personalised, well-informed solutions.

Key attributes of a Data Analyst

According to LinkedIn, data mining and statistical analysis were the second most in-demand skills requested by employers, with the most number of job openings, listed in 2016.

As the main driver of strategic business decisions and high industry demand, here are some key attributes of a successful Data Analyst:

  • Critical thinking: Before developing any hard skills such as programming, it is vital that a data analyst adopts a critical thinking mindset. To ensure useful insights can be drawn it is necessary that a data analyst has the ability to ask the correct questions in the first place. The role of the analyst is to then uncover and synthesise connections, make sense of the information connected, and present their findings in an easily digestible manner.
  • Understanding the data lifecycle: Familiarity or knowledge of the acquisition, management and pre-processing of data, as well as mathematical and statistical analysis, reporting and decision making is extremely useful. It allows a Data Analyst to interpret data and present it in a meaningful way that can be used to support business decisions.
  • Computer programming: Competency in programming languages such as R, Python, SQL, SAS, MATLAB and Excel are invaluable for Data Analysts, who use programming skills to extract, discern, and manipulate data. This information can then be presented into digestible visualisations in tools like Tableau.
  • Data visualisation and presentation: Data visualisation and presentation are hand-in-hand skills for a Data Analyst. The ability to tell a compelling story with data and drawing valuable insights is key to making any data useful.   
  • Machine learning: Machine learning and predictive modelling are increasingly becoming the most popular topics in the field of data science. To develop these skills, a Data Analyst is required to have proficiency in programming languages in order to make predictions and automate existing data systems. 

Data Analytics aligns results in a quantifiable commercial outcome that is realistic and applicable to each situation. This takes a lot of patience and creativity.” — Felipe Rego, Analytics Partner.


Opportunities in the world data analytics

On a global scale, 2.5 quintillion bytes of data are created each year, with over 90% of all the data that exists today only created within the last two years. That’s a lot of data!

With increasing demand for Data Analysts, there is a multitude of benefits of kickstarting a career in data including:

Huge job opportunities

  • With increasing demand for Data Analysts, there is a significant skills shortage in the market. With the number of unfulfilled roles set to rise, McKinsey predicted that in the US alone, there was a shortage of over 190,000 Data Analysts in 2018. For anyone on the front foot of driving a future-focused career, embarking on a career in data would avoid making their skills obsolete and assist in riding the next wave of digital change in the workforce.

Increased earning potential

  • As with the basic economics of supply and demand, a shortage in the number of Data Analysts in the market automatically means increased earning potential. For those Data Analysts currently in, or looking to transition into the field, an increasing trend of year-on-year income is set to continue.  

Expanding career development

  • Organisations of all sizes are beginning to prioritise data as an important part of their business operations. With new technology enabling increasingly sophisticated Data Analytics with large and diverse data sets, there is a multitude of different types of roles across various pathways. Anyone entering or progressing in the field can also choose from three types of Data Analytics to work in: prescriptive analytics, predictive analytics, and descriptive analytics.

Ability to work with some of the world’s biggest brands:

  • The world’s largest brands such as Apple, Amazon, and Uber are all looking into data to make well-informed decisions. For Apple, data is used to understand what additions and modifications customers need to deliver exceptional user experience. For Amazon and Uber, predictive algorithms are used to map out recommended purchases and travel routes.

In the last 30 years, the rise of data, and how it is produced, consumed, and stored has dramatically revolutionised the way businesses make decisions. With the number of career opportunities set to increase, transitioning into a role in data presents a plethora of exciting pathways.

Learn how you can use data to make informed business decisions and present compelling stories with our upcoming Data Analytics course.

Academy Xi Blog

An Introduction to Big Data Analytics

By Academy Xi

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Data has become one of today’s most important resources for businesses regardless of size. It’s invaluable when it comes to decision-making, so much so that big data is today a multi-billion dollar industry, and growing at an exponential rate.

The value of data isn’t new. In fact, collecting data and using it to make informed decisions have been around for millennia.

The history of data analytics

Statistics — where data analytics is based on — can be traced back to Ancient Egypt for the construction of the pyramids. Data collection has been used for centuries with governments collecting information for censuses, using trends and insights for planning and taxation.

By the 19th century, businesses have followed suit with the likes of Frederick Winslow Taylor and Henry Ford using data analytics to gain insights they will then use when making changes or improvements to their processes.

In the 60s, data analysis was shifting from being completely manual to being aided by computers. By the 80s, the growth of data gave rise to data warehouses. By the 1990s we were data mining; the process of finding patterns using large datasets.

Fast forward over 20 years, and the rise of big data as we know today is being used across industries from government and healthcare to retail and lifestyle. A multitude of tools are being developed constantly so we can collect, store, and assess data efficiently.

Key technologies in Big Data

Many techniques and technologies are involved in data analytics, enabling businesses to maximise their data and get the right kinds of insights.

Here are some of Big Data’s key technologies:

Data management: Before data can be analysed, it must first be high-quality. Processes must be established to maintain the quality of data. Tools like NoSQL databases and other master data management systems are used to ensure reliable and efficient data management that can be accessed by the entire enterprise.

Data mining: With the exponential growth of data, data mining is used to discover patterns and get information that can be analysed and used to inform complex business decisions. Data mining tools make it possible for data to be sifted immediately, providing only relevant insights to help companies make speedy decisions.

Predictive analytics: Data is processed using algorithms and machine learning to assess future outcomes. Through predictive analytics, businesses can determine if they’re on the right track, helping prepare them for the future and to discover, evaluate, or solve problems through predictive scenarios.

How we collect, analyse, and use data has evolved. Along with technology, the importance of data and its analysis has also evolved.

The importance of data analytics

Data analytics provides businesses with quantifiable information, enabling smarter decision-making through having the means to measure success and track goals over time. By analysing data, they can find new opportunities for improvement and growth.

Business moves are smarter, profits are higher, operations are more efficient, and customers are happier.

According to Whizlabs, the importance of data analytics is apparent through four perspectives: data science, business, real-time usability, and the job market.

  • Data science: Big Data has variety, volume, and velocity with analytics being able to extract and prepare it to provide fresh insights for researchers, analysts, engineers, and businesses.
  • Business: Businesses can keep their focus on customers by interpreting Big Data to provide them with their needs. This improves aspects of a business from efficient workflows and customer satisfaction to increased profits.
  • Real-time use: Many industries benefit from Big Data analysis like banking, technology, energy, and consumer industries. The educational sector also benefits from data analytics in real-time through study.
  • Job opportunities: Big Data and analytics have opened a wealth of lucrative opportunities in the data sciences, making it a promising profession. Those interested in data can gain opportunities like Big Data Analysts, Big Data Engineers, Solutions Architects, and more.

Making data-driven decisions

In order to survive in business, you must remain competitive. These days, where organisations use Big Data and powerful data analytics strategies every day, simply using your gut to make decisions will not get you the desired results.

Instead, you need to develop a data-driven culture.

Your business acquires data by the second — whether it’s from internal processes or customer input. You have the raw materials you need to get the answers and the best insights to inform your business decisions. These answers and insights are obtained through analytics.

Despite the value of data analytics, only 40% of businesses use data-driven strategies, with a majority still opting to trust gut instincts and experience. Making the most out of data technologies can be your strategic edge above the competition.

But how do you start making data-driven decisions? Here are five steps you can follow:

Step 1: Have a plan.

What goals do you want to achieve, or questions you want to be answered using data? This informs the kind of data that will be relevant, and the technologies and strategies you will use to analyse data.

Step 2: Identify sources of data.

Which area of your business provides the most valuable data that will help you achieve your business goals and strategies? Is it customer service? Operations?

Step 3: Target and streamline data.

With the amount of data you receive, it’s crucial to know which is most beneficial and worth investing your data management budget on. Focusing on relevant data reduces chaos and inaccuracies in data reporting.

Step 4: Collecting and analysing.

Determine the tools and technologies you will need to effectively collect and analyse data, as well as the skills and expertise of those that will handle data and analysis. Depending on the size of your organization, this can range from one person using Excel to a team of data scientists and executive level heads using a combination of technologies to acquire insights.

Step 5: Take action.

The presentation of data and insights can be crucial in helping decision-makers take the correct course of action. Visual presentation of data and analysis will help make them more clear, concise, and digestible. Decision-makers will be able to take quick action and improve the business faster.

Tools for data analysis

Whether it’s only one person doing data analysis or a whole team, there are indispensable tools that will help parse data and generate reports and statistics.

Two tools, in particular, are Excel and Tableau.


Microsoft Excel is a basic but powerful tool for data analysis used by many. It can perform simple to complex data analytics, helping users from creating simple pie charts with conditional formatting to more complex histograms, run SQL databases, search vlookups and pivot tables.


Tableau features an intuitive interface and can do everything from data sourcing through any data source, preparation and exploration of data, as well as analysis and presentation. Tableau functionality includes data visualisation and even social media sharing.

Other tools for data science

Aside from the above tools, more complicated data may benefit from more robust tools like programming languages. Here are five of the most widely used:

  • Python: one of the most popular data science languages, Python is easy to learn, has good visualisation capabilities and is scalable and faster than any other coding language. Data science and data analytics libraries also use Python. With its large community, it’s easy to find answers online too.
  • R Programming: through data wrangling, R Programming is able to clean and process messy data for easier understanding and analysis. It has many tools for data visualisation, analysis, and representation and is compatible with machine learning. It is also open source, making it a cost-effective option for organisations regardless of size.
  • Java: one of the oldest languages, Big Data tools are likely written in Java. It has a wide library with multiple tools for data science, enabling you to easily solve Big Data problems. It also has the JVM or Java Virtual Machine, a great platform for writing identical code for multiple platforms.
  • Microsoft SQL: this is a relational database management server that can store and retrieve data. It has capabilities for different workloads, whether it’s a small single-machine application to a larger Internet-based one with multiple users.
  • Scala: a high-level programming language that means “scalable language”. It is open source and like Java, runs on the JVM platform and can operate seamlessly, making it flexible and compatible with Big Data tools that also run on Java.

These are only a few of many programming languages that can aid in the analysis of data. Other languages like Julia, MATLAB, C++ and more have their own advantages and can be more preferable depending on your organisation’s needs and your data scientist’s expertise.

Through the use of tools and technologies for data science, businesses will be able to truly take advantage of data analytics.

Businesses will enjoy cost reductions through cost-effective storage options for large amounts of data, and data-driven improvements to make business more efficient. Not only will decision-making be better, but it will also be faster—even immediate.

Ultimately, data and analytics provide businesses with the ability to identify what customers want and the best way to give them exactly what they want.

Turn your data into actionable insights, and well-informed business decisions with our part-time Data Analytics course.

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