colour charts and graphs for data visualisations

Academy Xi Blog

How to choose colour charts and graphs for data visualisations

By Academy Xi

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colour charts and graphs for data visualisations

Don’t underestimate the power of colour when displaying data. Read on to discover why colour selection is important and approaches you can take to choose the right shades to support your data.

Why is colour important in data visualisation?

Colour is important in data visualisation because it can help to convey information and enhance the overall impact and understanding of the data being presented.

It is important to use colour effectively in data visualisation to ensure that it supports, rather than detracts from, the overall message of the data. When using colour, it is important to choose colours that are easily distinguishable and have appropriate contrasts, and to use colour consistently throughout the visualisation to maintain clarity and consistency.

How to choose best colours for data visualisations

Whichever colour scheme you select, ensure that it’s consistent throughout. This will support clarity and make it easier for your viewer to understand the data. In terms of how to choose the best colours, here are a few approaches to consider.

  • Analyse collected data insights

By analysing the data insights, you will be able to identify patterns and relationships between the variables included. You can then highlight certain patterns or emphasise the relationships using specific colours, perhaps choosing colours that best represent the patterns.

  • Evaluate other visualisations

Consider what you think works, or doesn’t, in other visualisations you have seen when it comes to the colour selection. Finding examples that reflect your data could also be helpful in assisting you with your colour choices.

  •  Highlight important data points

If you want to draw attention to particular sections of data or specific points, consider using a colour that is in high contrast to the rest of your selection so it will stand out. 

Types of colour palette

The following palette types are worth considering when selecting colours for your data.

  • Qualitative palette

This palette is specifically for categorical data where the emphasis is on the distinction between categories rather than on quantitative differences. Qualitative palettes use a limited number of colours with high saturation and good contrast. This ensures each category is easily distinguishable from the next. 

  • Sequential palette

Sequential palette’s use a range of colours that change smoothly from one colour to another to represent a progression of values. This palette is often used to show a continuous variable that has a clear ordering, such as temperature, rainfall, or population density. The colour palette is chosen such that each colour represents a different range of values, with the first colour representing the lowest values and the last colour representing the highest values. For example, with temperature, blue to represent cooler temperatures and red the warmer.

  • Diverging colour palette

A typical diverging colour palette has a neutral colour in the middle, such as white or grey, that represents the baseline or reference value. The colours on either side of the neutral colour represent values that deviate from the reference value, with one colour representing values above the reference and the other colour representing values below the reference. The change from one colour to the next is usually gradual, allowing the viewer to easily see the progression of values represented by the data.

Diverging colour palettes are useful for showing the spread of data around a central value and for highlighting the difference between two opposite values. They are often used in data visualisations such as heat maps and bar charts.

Tools for using colours

It’s well worth checking out these tools for supporting your colour selection process.

  • ColorBrewer

Providing pre-designed colour palettes for a wide range of data visualisation types including sequential and diverging palettes. Designed to be colour-blind friendly, ColorBrewer is widely used by data scientists, cartographers and designers – a popular resource for creating effective and accessible data visualisations. 

  • Color Thief

A small library for grabbing the dominant colour or a representative colour palette from an existing image is the central focus of Color Thief. It was created for web developers who need to extract colours from images to use in their designs. The tool analyses the pixels of an image and finds the most frequently occuring colours, then returns the dominant colour palette of the image. 

  • Viz Palette

Viz Palette helps to create colour palettes based on colour theory, or provides predefined colour palettes for use in visualisations. This tool can ensure colour consistency across different visualisations and presentations, making it easier to compare and interpret data. 

How to get into 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 data governance

Academy Xi Blog

What is data governance and why does it matter?

By Academy Xi

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what is data governance

Well managed, high quality data can truly support meaningful decision making for organisations, which can have a great impact on their bottom line. Discover Data Governance and why every organisation needs it.

What is data governance?

Data Governance is the set of processes, policies, standards, and roles responsible for managing data throughout its lifecycle in an organisation. It ensures that data is managed and used in an appropriate, consistent, and efficient manner, which is critical for making informed business decisions.

In recent years due to the growing volume of data generated by organisations (for mind-blowing reference, predictions estimate the world will generate 181 zettabytes of data by 2025!) data governance has become increasingly important. This data can be used to make informed decisions, but only if it is of high quality and managed appropriately. Data Governance helps to ensure that data is consistent, reliable, and secure, which is essential for making effective decisions.

What are the benefits of data governance?

  • Improved quality of data

By establishing a set of policies, procedures and standards for managing data, accuracy, consistency and reliability of data can be improved. 

  • A common understanding of data

Data governance can help ensure that everyone within an organisation is using the same definitions and standards for data. This can reduce the risk of confusion, errors and misinterpretation.

  • Increased trust in data

Ensuring that data is properly managed and protected and used in an ethical and compliant manner will increase user trust in it.

  • Improved data management

Organisations are able to better manage their data, reduce duplication and generally make it more efficient. 

  • Consistent compliance

Data governance can assist organisations comply with regulations, regulators and any clients that require proper management of sensitive information. 

Data governance in the cloud

As with any data in an organisation, processes and practices for data management regardless of its location are required if you want to embrace quality data governance. Benefits of properly implementing data governance in a cloud computing environment include:

  • easy scalability for data storage requirements
  • improved security and privacy
  • cost savings
  • increased collaboration and accessibility  

It’s important to note that Data Governance in the cloud is not a one-time process, but rather a continuous effort to maintain and improve the management of data. Organisations must regularly review and update their policies and procedures to ensure they align with changes in technology, regulations, and business needs.

Who’s responsible for data governance?

It’s vital that everyone in an organisation has a clear understanding of what data governance is and their role within it. The following key people are generally responsible for data governance:

  • Chief data officer (CDO)

A CDO is responsible for overseeing the data governance strategy for the organisation and ensuring that company data is managed, protected and used in compliance with legal and ethical standards.

  • Data owners

Individuals or departments within a company that have control over specific types of data and are responsible for defining and implementing policies for its use are data owners. 

  • Data stewards

Individuals responsible for maintaining policies established by data owners are sometimes referred to as data stewards. Their role is to ensure the data is properly classified, stored and maintained and that data access and usage is consistent with company policy.

Types of data governance tools

The following tools will assist in creating sound structures for your company data:

  • Data cataloguing

A central repository of information about the organisation’s data assets is known as the data catalogue. It includes a running record of the data, its definition, structure and usage.

  • Visualisation

This tool helps organisations better understand and manage their data assets by presenting data in a graphic format. Data becomes more user-friendly in this format and can enable opportunities to be identified for improvement in data quality, security and compliance. 

  • Data lineage

Data lineage provides a complete and accurate record of the history and movement of data from its origin to its current state. This tool helps organisations to understand how data is transformed, moved, and used over time and to track the relationships between different data assets. 

  • Threat detection

Organisations can identify potential security threats to their data assets and prevent unauthorised access or misuse of sensitive data. Automated tools and processes that monitor and  analyse data access patterns and trigger alerts in the event of any suspect activity. 

How to get into 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.

Introduction to Pandas Python webinar

Academy Xi Webinars

The power of Pandas for Data Analytics

By Academy Xi

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Want to bring the power of Pandas to your Data Analytics process? In this introductory webinar, you’ll get to grips with Pandas and learn how to use external data sources to create data structures.

By the end of the webinar, you’ll be comfy with Pandas fundamentals and able to build your own data structures from scratch!

Topics include:

  • Building Series & DataFrame data structures
  • Creating & Importing data
  • Indexing & filtering data

Reserve your spot now and add this powerful data tool to your toolkit.

Save your seat now!

Register here

chatbot chatGPT

Academy Xi Blog

The world’s most advanced AI chatbot: ChatGPT

By Academy Xi

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chatbot chatGPT

An OpenAI bot called ChatGPT is causing a stir online after more than one million users signed up in the first few days of its release. Read on to find out what ChatGPT is, how it works, and what it means for the future of tech and employment.  

Let’s face it, we’ve all dropped the ball at some point. You open up your Google calendar expecting to find a breezy Friday morning ahead of you, but realise you’ve got an important meeting in less than half an hour. 

You’re meant to be presenting an updated company value proposition to the board and you haven’t even started it. In the past, you’d say you need a miracle. These days, all you need is ChatGBT. Freakout averted.

What’s ChatGPT?

ChatGPT is a prototype AI chatbot that’s capable of understanding human language and generating impressively human-like written text. It’s the latest evolution of the GPT – short for Generative Pre-Trained Transformer – a family of AI that specialises in generating written text.

what is chatGBT

To give an example, if you log into ChatGPT and input a command of “write a company value proposition for Academy Xi”, ChatGPT will rattle off a written response in about 20 seconds. It will be grammatically flawless and astoundingly detailed. 

The possibilities are limitless. You can use ChatGPT to whip up a snappy bio for your LinkedIn page, write a corporate white paper, pen the script for a movie, or even compose a degree-level essay. In short, ChatGPT gives you whatever written content you need, whenever you need it. 

Beyond taking the heavy lifting out of writing, ChatGPT has a vast array of applications, including: 

  • Solving mathematical equations
  • Debugging and fixing code
  • Translating languages
  • Making classifications and explaining what something does (such as defining the functionality of a code block). 

In other words, ChatGBT has the potential to simplify all kinds of everyday tasks for a lot of different people.

How does ChatGPT work?

Trained by AI and machine learning, the ChatGPT system is designed to provide information and answer questions through a conversational interface. ChatGPT’s AI is powered by a form of deep learning called neural networking. Neural networks are a series of algorithms that replicate the neuro functions of the human brain. Each of these neurons:

  • Receives data from the input layer
  • Processes it by performing calculations 
  • Transmits the processed data to another neuron

How data moves between neurons within a network and the calculations performed will depend on what data findings are uncovered along the way. Though a neural network makes decisions about what to do with data all by itself, it first needs to be trained with data inputs. ChatGPT’s AI has been trained with an enormous sample of written text taken from the internet. 

Who invented ChatGPT?

The research company who developed ChatGPT, OpenAI, said the new AI was created with a focus on ease-of-use. “The dialogue format makes it possible for ChatGPT to answer follow-up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests,” the company said in a recent statement. While chatbots are nothing new in the tech industry, the sophistication of ChatGPT makes previous iterations seem like child’s play by comparison. 

OpenAI is San Francisco-based, led by Sam Altman and receives financial backing from Microsoft, LinkedIn co-founder Reid Hoffman and Khosla Ventures. In what’s been a grim year for the tech sector, with mass layoffs, wrecked stock prices, high-profile cyber breaches and crypto catastrophes, ChatGPT has served as a timely reminder that the industry is still defined by jaw-dropping innovations.

A new era of natural language processing

Tech experts and venture capitalists have gushed about ChatGPT on Twitter, some even comparing it to Apple’s release of the first iPhone in 2007. Five days after ChatGPT was launched, Altman tweeted that the research tool had “crossed 1 million users!”. 

Back in 2016, tech bluechips like Facebook, Google and Microsoft were talking up their digital assistants as the future of human and AI interaction. They boasted of the potential for chatbots to book Uber rides, purchase plane tickets and answer customer service queries with a human touch.

Six years later, chatbot progress has been slower than expected. The majority of chatbots we interact with on a daily basis are still relatively primitive, often installed on customer help pages and only capable of answering rudimentary questions.

With early ChatGPT adopters demonstrating its ability to carry a conversation through multiple queries, deftly navigating a range of intricacies, the world of so-called natural language processing appears to be entering a new era. 

What does ChatGPT mean for employment? 

A person wrote this blog. Honestly. But how long before AI is able to write it instead?

In the days since ChatGPT was released, there’s been speculation that professions dependent on content production might be rendered obsolete, including everything from playwrights and copywriters to programmers and journalists.

Academics have generated responses to exam questions they admit would result in full marks if submitted by an undergraduate, and programmers have used ChatGPT to solve coding challenges in obscure programming languages in a matter of seconds.

However, in its current stage ChatGPT is not without its flaws. Its current knowledge base ends in 2021, rendering some queries and searches outside of its expertise. 

I asked it to write a blog article about “the future of employment factoring in the impact of ChatGPT” and was served a reply of “ChatGPT does not have the ability to provide information or opinions on current events or trends, or to speculate on the potential implications of new technologies”.

It’s also important to keep in mind that ChatGPT can occasionally give factually incorrect answers and authoritatively present the misinformation as truth, with OpenAI conceding that the AI can sometimes produce “plausible-sounding but incorrect or nonsensical answers”.

While ChatGPT might be able to generate a string of related facts, even presenting them in polished written language, it can’t be counted on to place those facts into dialogue and generate complex arguments. In short, critical-thinking and formulating opinions are not its biggest strengths. Without a doubt, it does have many others.  

OpenAI said the platform has “limitations and cannot replace humans”. If you’re in a profession that seems to be threatened by this emerging tech, wipe the sweat from your brow – AI isn’t taking over just yet.

The question remains though, how will ChatGPT be used by companies and people? The tech is versatile enough to be deployed in a wide range of settings to perform all kinds of tasks, so watch this space. One thing’s for sure – whatever ChatGBT is used for is bound to be groundbreaking.


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