What is GitHub

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What is GitHub and how do Software Engineers use it?

By Academy Xi

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What is GitHub

GitHub is a buzzword in the tech space – it’s the name on every coder’s lips. Maybe you’re wondering ‘What even is GitHub and how do developers use it?’. The short answer – in a multitude of ways. Let’s explore them……

What is GitHub and how do developers use it?

If you enter the searchterm “what is GitHub?” on Google, you’ll be served over 615 million responses. Amongst other things, you’ll be told GitHub is a cloud storage site, a version control system, a social networking site for programmers, and a code-sharing site. 

Does GitHub actually do all these things? It certainly does, and so much more. 

Broadly speaking, GitHub is a cloud-based service designed to help developers store, manage and share code while working on development projects. Crucially, GitHub also helps team members collaborate effectively by tracking and controlling changes to their code, known in the industry as ‘version control’. 

What is Git?

Before we get ahead of ourselves, let’s cover the basics. Git was first developed in 2005 by tech figurehead Linus Torvalds, who had already famously created the Linux operating system kernel. 

In a nutshell, Git is a DevOps tool used for source code management that helps to efficiently handle coding and development projects of all shapes and sizes. Git is used to track changes in source code, enabling multiple developers to work together on nonlinear projects without any confusion or conflict. As such, Git is the world’s most popular free and open-source version control system 

So, what’s the difference between Git and GitHub? Good question! Git is used to manage multiple versions of source code edits, which are transferred to files in a Git repository. GitHub serves as a location for all repositories, where all files are uploaded and stored. 

How do version control systems help developers?

Imagine you’re part of a team of developers working on a web application with multiple features. Your app may have a large, complex code base, involving several projects and developers working on different parts of the software.

This type of scenario is quite common and can easily lead to problems:

😬Imagine someone is working on a new feature and something they change breaks the app. How can you “back up” to the most recent working version of the code?

😬Or, there could be some part of the application that overlaps with the work being done by other developers. How can you ensure there are no inconsistencies or conflicts in the code?

The term Version Control System (VCS) describes a type of software that is designed specifically to help manage the potential pitfalls described above. 

Version control keeps a complete history of the changes to the code, which makes it easy to revert to a working state. This gives you the freedom to experiment, throw away bad ideas, and instantly get back to your last-known “good” state if anything breaks (something always breaks, right?!).

There are a number of benefits that come with using a VCS to manage your work:

✔️Automatically creating a backup of your work

✔️Providing an easy way to undo mistakes and restore a previous version of your work

✔️Documenting changes with a log that clearly describes what’s been changed and why

✔️Keeping file names and hierarchies consistent and organised

✔️Breaking off from the source code to work in multiple “sandboxes” (called branches in Git), allowing developers to experiment without impacting other branches

✔️Merging branch code back into the source, once the developer gets his or her part of the code working properly

✔️Collaborating with others without disturbing each other’s work

The world’s most popular VCS software is Git. As you’ve probably guessed, GitHub is the platform that hosts the Git VCS. 

GitHub’s other features and uses

Although GitHub was primarily developed for its version control capabilities, its functionality has evolved with time. A host of handy new features have been added over the years, enabling programmers to use GitHub for a range of vital day-to-day tasks, including:

  • Improved project management

The ‘GitHub Issues’ feature allows you to manage a development project from end-to-end. You can use it to define tasks, track their progress, and communicate with coworkers. This typically involves a description of the coding task, with additional comments, assignees, labels and milestones.

  • Easy file management

GitHub makes managing your files easy-as-pie. When you edit or save a file, Git effectively takes a series of snapshots of it in its various states. Everything that’s happened to your file along the way is captured, meaning you can access it in any state it’s been in. Impressive, huh?! 

  • Increased safety with additional packages

Although GitHub is cloud-based, it incorporates security features that help keep your code and sensitive data secure across the repositories. Many of these features are available as standard with all GitHub plans, but for those projects that demand even tighter levels of security, additional protection is available with GitHub’s Advanced Security packages.

  • Effective team management

GitHub’s project boards can be used to organise and manage a team, enabling you to pull requests, add notes, allocate tasks to team members and assign due dates. You’re also able to arrange cards into columns that enable your entire team to visualise and track workflows and easily understand how a project is progressing as a whole.

  • Simple code hosting

Once you’ve set up your GitHub account, all your code can be placed in a Git repository. You’ll have options on where you choose to host your code, but the simplest way is to do it directly via GitHub. This gives you the convenience of all your code and documentation being hosted in a single location. Beautifully simple!

  • Social Networking

In its essence, GitHub is all about developers working together to complete team projects. This dynamic naturally extends to social networking. Tech professionals all over the world use GitHub to share code and practical advice, showcase their skills, and pitch project ideas. As well as connecting like-minded professionals, this makes GitHub a perfect place to host a portfolio. It’s great for job networking too! 

  • Improved code writing

GitHub doesn’t just facilitate coding projects, it actually has the potential to improve your code writing. You can plug GitHub Copilot directly into your editor and it will convert natural language prompts into coding suggestions across dozens of languages. Trained by billions of lines of code, Copilot’s suggestions are almost always spot-on and will take your coding to the next level.

Want to become a Software Engineer?

At Academy Xi, we offer a range of Software Engineering courses that are designed and delivered by experienced industry professionals. Our hands-on, practical training will take you from beginner to job-ready in a matter of weeks.

You’ll complete all your projects in GitHub and walk away with a GitHub portfolio showcasing your new coding skills to prospective employers. 

Best of all, our Transform courses come with access to a Career Support Program that helps 97% of graduates land their dream role.

To discuss your transferable skills and course options, speak to a friendly course advisor today and take the first steps toward an exciting new career in tech.

what is data mining, data mining benefits

Academy Xi Blog

What is data mining, types of data mining techniques

By Academy Xi

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what is data mining, data mining benefits

Discover the power of big data and how it can benefit every industry from better decision making to improved customer outcomes.

What is data mining?

The process of discovering patterns and relationships in large datasets using a range of computational and statistical techniques is known as data mining. It involves analysing data from multiple sources including databases, websites and social media platforms to identify insights that can be used to make better decisions, predict future trends and discover correlations. It’s increasingly used across a range of industries, most commonly in marketing, finance, healthcare and customer relationship management.

History of data mining

Data mining was sparked back in 1763 by Bayes’ Theorem, with many discoveries from this point onward that laid the foundation for our current definition and understanding of what data mining is in 2023, including a term coined in the 1980’s by data scientist Greogory Piatetsky-Shapiro, ‘Knowledge Discovery in Databases’, which described the process of extracting meaningful information from large datasets.

 With the rapid increase of computer technology throughout the 1990’s and 2000’s, data sets have grown exponentially in quantity and size, while data mining has also become more popular, with companies collecting and analysing large quantities of information to harness the power of big data.

How data mining works

Data mining involves collecting and preprocessing, exploring and modelling the data, evaluating the model, and deploying it in a real-world application. Let’s explore the basic steps involved:

Data collection

The first step in data mining is to collect and gather data from various sources. The data can be in a structured or unstructured format, with sources including databases, web pages, social media platforms, or sensors. 

Data pre-processing

Once the data is collected, it needs to be cleaned, transformed, and prepared for analysis. This involves removing missing values, handling outliers, and normalising or standardising the data.

Data exploration

Visualising and exploring the data helps to gain a better understanding of its characteristics and patterns. Techniques such as scatter plots, histograms, and heat maps can be used to identify patterns and relationships.

Data modelling

After exploring the data, the next step is to build a model that can identify patterns and make predictions. There are several machine learning algorithms that can be used for this step, such as decision trees, neural networks, and support vector machines.

Model evaluation

Once the model is built, it needs to be evaluated to ensure its accuracy and effectiveness. This can be done by testing the model on a separate dataset and comparing the predicted results to the actual outcomes.


The final step is to deploy the model in a real-world application or system. This can involve integrating the model with existing software or creating a new system that can use the insights generated from the data.

Differences between data mining and machine learning

While data mining and machine learning are certainly related fields, they also have some fundamental differences.

 Data mining is sometimes used as a broader term to include various techniques such as clustering, regression and association rule mining, whereas machine learning is a specific subset of data mining that focuses on building predictive models using algorithms.

 Ultimately, data mining is about discovering new insights from information, whereas machine learning is a process of building predictive models based on data.  

Benefits of data mining

Data mining offers several benefits to organisations and businesses, including:

Improved decision making

One of the most discussed and popular benefits of data mining is how the patterns and trends identified can inform better business outcomes, including increased sales.

Increased efficiency

Identifying areas of inefficiency or waste enables companies to optimise their operations. This can result in cost savings and improved productivity.

Enhanced customer insights

By analysing customer data, businesses can gain insights into customer behaviour and preferences. This can help them develop more targeted marketing strategies, improve customer service, and ultimately increase customer satisfaction.

Fraud detection

Data mining can be used to identify fraudulent activities and prevent financial losses. For example, credit card companies use data mining to detect fraudulent transactions and prevent credit card fraud.

 Competitive advantage

By leveraging data mining techniques, businesses can gain a competitive advantage by identifying opportunities that their competitors may have missed.

Personalised recommendations

Based on customer preferences and behaviour, businesses can provide personalised recommendations, potentially leading to increased engagement and loyalty.

Types of data mining techniques

There are several approaches that can be taken, with the choice depending on the specific task, the type of data and the desired outcomes. Here is some further detail on common techniques:

Predictive analysis

Using historical data, predictions are made about future events or trends. It can be used for various purposes, such as forecasting sales, predicting customer behaviour, or identifying potential risks.

Decision trees

A type of data mining algorithm that can be used for classification and prediction. They use a tree-like structure to represent decisions and their possible consequences.


Categorising data into predefined classes or categories is known as classification. It is often used for tasks such as spam filtering, fraud detection, or image recognition.


Grouping similar data points together based on their characteristics or features is another approach known as clustering. It is often used for segmentation, customer profiling, or anomaly detection.

Association rules

This technique involves discovering relationships or patterns between variables in a dataset. It is often used for tasks such as market basket analysis, where the goal is to identify which products are frequently purchased together.

Limitations of data mining

Potential downsides of data mining include the fact that training is required to effectively use the software and tools, which can be a complex process. If the data is false or biassed, the insights won’t accurately reflect reality and could potentially negatively impact any subsequent decision making.

On the security front, there is the potential for mined data to be misused or stolen, leading to bad consequences for businesses and consumers.

How to get into User Interface Design

Academy Xi UX UI Design courses give you all the practical skills needed to break into the industry. You’ll learn to place the user’s needs at the centre of the design process, so you can create smooth and functional products that leave a lasting impression.

Whether you’re looking to upskill and test the waters of the industry, or launch a completely new career as a UX Designer, we’ve got the perfect course for you:

  • UX UI Design: Elevate (Self-Paced) – For upskillers looking to boost their career prospects with in-demand creative capabilities, whilst enjoying the flexibility of self-paced learning.
  • UX UI Design: Transform – For career changers, looking to transform their profession with a course offering access to a Career Support Program that helps 97% of graduates land their dream role. 

Not sure which is the best course for you? Chat to one of our course advisors and we’ll help you break into one of Australia’s most creative industries.

what is machine learning

Academy Xi Blog

Machine learning – what is it and why does it matter?

By Academy Xi

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what is machine learning

Automated learning by machines to solve complex problems and make solid predictions is the essence of machine learning, and it’s proving to be a game changer. Read on to find out how and why.

Why is machine learning important?

Machine learning enables computers to automatically learn and improve from experience as opposed to being specifically programmed. As a result, it’s possible to create highly accurate and efficient models that can solve complex problems and make predictions based on new and unseen data. 

Advances in computing power, data storage and algorithm development have made machine learning more accessible and scalable, making it an essential tool for a wide range of applications across fields such as finance, healthcare and transportation. 

Where machine learning is used today 

You are likely interacting with some form or another of machine learning tech in your daily life, perhaps without even realising it. Applications currently using the tech include:


  • Image recognition in social media apps
  • Personalised recommendations in e-commerce websites
  • Fraud detection in financial transactions
  • Speech recognition in virtual assistants, such as Siri and Alexa
  • Medical diagnosis and treatment recommendation systems
  • Self-driving cars and autonomous vehicles
  • Predictive maintenance in industrial settings
  • Natural language processing for translation and sentiment analysis
  • Credit scoring and risk assessment in lending and insurance 
  • Energy load forecasting for efficient energy management.

What are the main types of machine learning?

There are three main varieties of machine learning: supervised, unsupervised and reinforcement learning. Let’s take a look at each.

  • Supervised learning

With this approach, the algorithm is trained on a labelled dataset, where we already know the correct output. The algorithm then learns to map input variables to the correct output and can then make predictions on new, unseen data based on this learning. Supervised learning examples include image classification, speech recognition and regression analysis. 

  • Unsupervised learning

Unlabelled datasets are used in unsupervised learning, so the algorithm needs to work to find patterns or structure within the data on its own. Tasks such as anomaly detection, clustering and dimensionality reduction are examples of this method.

  • Reinforcement learning

With reinforcement, an agent is used to interact with an environment to learn how to make decisions that maximise a reward. The agent then receives feedback in the form of a reward signal and then uses this feedback to adjust its behaviour over time. This kind of learning is often used in game playing, robotics and other dynamic decision making tasks. 

What’s required to create quality machine learning systems?

Several key elements are required to create good learning systems. We’ve covered some of the top contenders to get you started. 

High-quality data

It’s crucial that the data used to train a machine learning model is of decent quality. Data ideally should be clean, representative and relevant to the problem of focus.

Appropriate algorithms

Choosing the right learning algorithm for a given problem is important. Each algorithm has its own strengths and weaknesses and the accurate choice will depend on the nature of the data and the problem being solved. 

Feature engineering

The process of selecting, transforming and scaling the input variables to a machine learning model is feature engineering. Quality feature engineering can greatly improve the accuracy of a model. 

Robust evaluation

It’s important to evaluate a machine learning model on a separate, unbiased dataset to ensure that it is not overfitting to the training data.

Iterative development

The development of good machine learning systems very often involves an iterative process of experimentation and refinement, where different algorithms, parameters and features are tested and improved over time. 

Domain knowledge

A deep understanding of the problem domain and the data is often critical to developing effective machine learning systems. This can help guide feature engineering algorithm selection and evaluation.

Adequate computing resources

Machine learning often involves processing large amounts of data, and may require substantial computing resources such as powerful GPUs and cloud-based computing services.

What is bootstrapping machine learning?

A technique of resampling data to improve the stability and accuracy of a model is known as bootstrapping. It involves randomly sampling the data with replacements to create multiple data sets, training a model on each dataset, and aggregating the results to get a more robust estimate of model performance. 

What are some popular machine learning methods?

There are a wide range of learning methods to choose from, with the best method being dependent upon the specific problem being solved and the nature of the data. Here are some of the most popular:

  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Naive Bayes
  • Support Vector Machines (SVMs)
  • K-Nearest Neighbours (KNN)
  • Neural Networks (including deep learning)
  • Gradient Boosting 
  • Clustering (such as K-Means or Hierarchical clustering)

What is AlphaGo?

A computer program developed by a British artificial intelligence company, DeepMind, which was acquired by Google in 2015, AlphaGo is a deep learning neural network. It uses a combination of supervised learning and reinforcement learning to play the board game ‘Go’. In 2016, AlphaGo defeated Lee Sedol, one of the world’s top-ranked Go players, in a five-game match, making the first time an AI program had defeated a human professional Go player. This was considered a significant milestone in the field of AI and demonstrated the power of machine learning for complex decision making tasks.

Interested in getting to Data Analytics?

At Academy Xi, we offer flexible study options in Data Analytics that will suit your lifestyle and training needs, giving you the perfect foundation for your future in data modelling.

Whether you’re looking to upskill or entirely transform your career path, we have industry designed training to provide you with the practical skills and experience needed.

If you have any questions, our experienced team is here to discuss your training options. Speak to a course advisor and take the first steps in your Data Analytics journey.

what is the dark web, should you access it?

Academy Xi Blog

What is the dark web and should you access it?

By Academy Xi

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what is the dark web, should you access it?

The dark web is a popular hangout for activists, whistle blowers and shadier types like drug and weapons dealers. Let’s dive into the dark web to discover what it’s all about and decide if we want to take a visit. Or not.

What is the dark web?

Part of the internet’s digital landscape is not indexed by search engines. Known as the dark web, it is often accessed by those who lead or participate in illegal activities, including the sale of prohibited firearms, drugs, stolen data and hacking services. Due to the anonymity of the dark web, law enforcement agencies find it challenging to monitor and prosecute. 

While the dark web has a reputation for illegal activities, it’s important to acknowledge that not all content found there is illegal. There are legitimate purposes the dark web can serve, particularly the identity and privacy protection of reporters in repressive regimes, activists and whistleblowers. 

In this article we delve into different aspects of the dark web, including:

  • Characteristics 
  • Types of threats
  • Dark vs deep vs surface web
  • Pros and cons
  • Example of the dark web
  • Is it illegal in Australia to access the dark web?
  • How do you get on the dark web?

Characteristics of the dark web

Core characteristics of the dark web include no webpage indexing, the use of virtual traffic tunnels, and no access via traditional browsers, all aiding in keeping content off the radar.

  • No webpage indexing

Not indexed by standard search engines, the dark web uses a system that enables users to access content and sites that are invisible to anyone using what’s known as the ‘surface web’.

  • Virtual traffic tunnels

Anyone who wants to access the dark web needs specialist software, such as Tor, which generates virtual tunnels for their internet traffic. These tunnels make it incredibly difficult for anyone to track user activity, or figure out their location. This anonymity acts like an invisibility cloak for criminals while they go about their illegal business.

  • Inaccessible by traditional browsers

Standard browsers such as Safari, Chrome or Firefox won’t allow you to access the dark web. Those wanting to access the ‘dark side’ need specialised browsers such as ‘Tor Browser’ or ‘Tails’, both of which will give users access to hidden services and websites on the dark web.

Types of threats on the dark web

Similar to using the surface web, there are unsurprisingly a number of threats you may encounter if using the dark web. Let’s take a look at a few common contenders.

  • Malicious software

Rife with malware and viruses that can infect and compromise a user’s device, malware is often spread through phishing emails or malicious links. 

  • Government monitoring

In an attempt to identify illegal online activity, government and law enforcement agencies still monitor the network. Monitoring approaches could include tracking user activity and attempts to de-anonymise Tor traffic.

  • Scams

False marketplaces and phishing emails are just some of the scams you could encounter on the dark web. Other common scams include Ponzi schemes and ‘investment opportunities’ that try to trick users into sharing their login details. Much like the surface web, theft of personal information and money are target areas.

What’s the difference between the Dark, deep and surface web?

You may have heard the terms dark, deep and surface web bandied about. They are essentially the three layers of the internet. 

Deep web

Also referred to as the hidden or invisible web, the deep web is not indexed, so it cannot be accessed by standard browsers. It’s unknown how big the deep web is, but it’s believed to be larger than the surface web. The deep web includes password-protected websites, content behind paywalls and subscription based services.

Dark web

Like the deep web, the dark web is also not indexed and cannot be accessed by regular browsers like Chrome and Safari. The difference between deep and dark is that the dark web uses encryption software to create even more security and is often completely inaccessible to the average internet user.

Surface web

This is the section of the internet that the majority of us engage with on a daily basis, but is speculated to only represent 4% of the total internet. Also referred to as the ‘visible web’ it’s the section that can be accessed and indexed by popular, standard search engines such as Google, Bing or Yahoo. The surface web consists of websites, web pages, and online content that are publicly available. 

Pros and cons of the dark web

The primary advantage of the dark web is anonymity. It is also the main disadvantage.

Those living in countries with repressive regimes can use this area of the internet to communicate without being monitored or arrested. The same benefit is available to whistleblowers, activists and journalists, enabling their privacy to be protected and the ability to release details that may otherwise be too dangerous to reveal. 

 It depends on who you speak to when it comes to perceived disadvantages of the dark web. The lack of regulation means users are more vulnerable to scams, fraud and hacking attempts, while it also lends itself to being used by criminals taking part in illegal activities.

Example of the dark web 

One of the most famous examples of the dark web is the Silk Road marketplace. An area where users could purchase illegal goods anonymously, the site was shut down by the FBI in 2013. Many copycat sites have since appeared to pick up where Silk Road left off. 

Is it illegal to go on the dark web in Australia?

While it isn’t illegal to access the dark web in Australia, or most other parts of the world, it is important to be mindful that much of the activity taking place is illegal. Accessing illegal content or taking part in any criminal activity is punishable by law, regardless of where the user is located geographically.

Free open-source software such as Tor is used to protect online privacy and enable anonymous communication. While used by some for legal activity, there are certainly illegal events taking place within the network too. As mentioned, users need to be aware of the risks involved before venturing in. 

How to get into Cyber Security

Completing practical, hands-on training in cyber security is a great way to get a foothold in the industry. Whether you’re already an IT professional seeking to upskill, or keen to launch a tech career from scratch, ensuring you have all the fundamental skills under your belt is a must. 

Our Cyber Security Engineering: Transform course will give you technical skills and strategic mindset that today’s Cyber Security Professional needs, taking you from beginner to job-ready and also offering access to a Career Support Program that helps 97% of graduates straight into the industry. 

If you have any questions, our experienced team is here to discuss your training options. Speak to a course advisor today and take the first steps in your Cyber Security journey.

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