How Long Does It Take To Learn Data Visualization?

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    Data visualisation is an essential skill in today's data-driven world. It is a powerful tool that helps individuals, and organisations make sense of complex data sets. However, many people wonder how long it takes to learn data visualisation.

    The truth is, the amount of time it takes to learn data visualisation varies depending on several factors. These factors include prior experience with data analysis and visualisation, the complexity of the data sets being analysed, and the tools being used for data visualisation.

    If you are new to data analysis and visualisation, it can take several weeks or even months to become proficient in data visualisation techniques. This process involves learning the basics of data analysis, such as data cleaning and transformation, as well as learning how to use data visualisation tools effectively.

    However, if you already have experience with data analysis and visualisation, you may be able to learn data visualisation more quickly. In this case, the learning process may only take a few weeks or even days.

    Another factor that affects the learning process is the complexity of the data sets being analysed. If the data sets are relatively simple, it may take less time to become proficient in data visualisation techniques. On the other hand, if the data sets are complex, it may take longer to learn how to visualise the data effectively.

    Lastly, the tools being used for data visualisation can affect the learning process. There are many data visualisation tools available, and each has its own learning curve. Some tools are more user-friendly and intuitive, while others require more time and effort to learn.

    Quick Links To Online Data Science Courses



    • Duration: As little as 8 months
    • 4 courses
    • Study Intakes: January, March, May, July, September and October



    • Part-time 8 months intensive
    • AU$3,840 per course (2023)
    • Next intake:
      January, March, May, July, September, October



    • 16 months, Part-time
    • 8 (One subject per each 7-week study period)
    • $3,700 per subject, FEE-HELP is available

    University Of Technology Sydney

    Applied Data Science for Innovation (Microcredential)

    • 6 weeks
    • Avg 14 hrs/wk
    • $1,435.00

    What is Data Visualization?

    For the purpose of representing data results, data visualisation makes use of graphical representations such as plots, charts, and animations. 

    These visual portrayals communicate complex data linkages in a style that is both accessible and entertaining, and they have a variety of uses for communicating data-driven insights with the internal and external stakeholders of a business

    Because visual learners make up an estimated 65 percent of the population, data visualisation offers a method for swiftly and effectively communicating the findings of data analyses. 

    Although though it can take many different shapes, the purpose of data visualisation is always the same: to communicate otherwise difficult-to-grasp data insights in a way that is memorable and clear, particularly for individuals who do not come from a technical background.

    Every day, 2.5 quintillion bytes of data are produced, and this number is expected to continue to increase at an exponential rate. 

    Because of the rise in the amount of data, there is now a greater requirement for the use of visualisation approaches in order to communicate the conclusions that can be drawn from the figures. The practice of data visualisation is applicable to a wide range of domains and fields of work. 

    Applications of this technology include revealing hidden trends or patterns in data, as well as expressing organisational hierarchies and systems. 

    In most cases, the process of data visualisation may be divided down into four different purposes: to develop ideas, to depict concepts, to give pathways into visual exploration, and to present data in the form of visualisations.

    What Can You Do with Data Visualization?

    Individuals who are able to develop data visualisations that are both interesting and successful have the ability to communicate complex data insights and discoveries to members of an audience who come from a variety of different backgrounds. 

    The depiction of data patterns may be made clearer and more engaging via the use of data visualisations. 

    Information, such as sales trends, may be presented using visualisations in a way that makes use of colour and contrast, allowing audience members to immediately observe information that could be difficult to detect when presented in the form of a graph or spreadsheet. 

    In addition, working with data visualisations has the additional advantage of providing a new perspective on the data by putting it in the context of a larger story. 

    This helps audience members understand the information in relation to an organisation's overall objectives or bottom line, which is a significant advantage of working with data visualisations. 

    It would be impossible to explain the full of the data that is being portrayed by simply displaying the numbers in a table; therefore, context plays an essential part in assisting viewers in understanding the data.

    A deeper comprehension of the inner workings of a company may also be gained via the display of data. Data visualisations give a tool that may illustrate multidimensional operations and how diverse company activities relate with one another. 

    Operating a business demands staying on top of numerous moving pieces, and data visualisations provide this capability. In addition, doing data analysis and visualisation yields insights into significant business KPIs, which, when used appropriately, may help everyday operations run more smoothly, and project execution go off without a hitch. 

    Data visualisations provide businesses with essential insights into the preferences, demographics, and buying behaviours of their clients. These businesses provide goods or services to end users. 

    They make it possible for businesses to investigate consumer perceptions of the brand, customer feelings, and even what customers post about the product or service on social media outlets by using data that is centred on the customer. 

    After that, companies may apply these information to adjust their marketing strategy and expand their brand presence.

    A narrative may be conveyed by data visualisations in addition to the insights about customers they provide and the depiction of significant trends in the data. The information is provided in a way that is simple to comprehend, and the viewer is led on a trip that ultimately results in a conclusion that is unavoidably supported by the evidence. 

    A powerful beginning, a basic storyline, and an ending that looks into the future and provides ideas for how to move ahead are the components that make up a visual tale. These elements are identical to those that make up a written story. 

    Rather than requiring tens of thousands of words to tell this story, data visualisations can do so with a series of well-placed infographics, carefully worded labels, and brief descriptions of the content. 

    All of these elements tell the story of data in the larger context of an organisation's needs and goals. Data visualisations can do this instead of requiring tens of thousands of words.

    Average Time it Takes to Learn Data Visualization

    It is challenging to estimate how long it will take each person to learn the necessary skills for data visualisation because the process requires numerous interconnected skill sets. 

    It's possible that some people will decide to acquire one skill first, and then move on to another tool or programme after that. Some people may decide to study them all at once, which may result in a different amount of time being required to generate data visualisations. 

    The following is a rundown of some of the talents that are utilised by those that deal with data visualisation, as well as a general estimate of how long it typically takes for an individual to acquire these skills:

    • When it comes to jobs involving data analytics and visualisation, Python is one of the most popular computer languages to use. It offers a wide variety of free libraries that are committed to the process of data visualisation, including Seborn, Ggplot, Plotly, and Matplotlib. Python is a widely used programming language that can be learned by beginners in as little as five to ten weeks, depending on how much prior experience the student has had with other programming languages. It may take a few months until one is comfortable with the data visualisation packages provided by Python.
    • Microsoft Excel is the application that serves as the industry standard for spreadsheets, and as a result, it is one of the programmes that people who work with data turn to the most frequently. The ability of this application to assist data professionals in organising, analysing, and visually representing data discoveries is essential. The majority of people agree that Excel is a programme that can be learned with little effort. It takes the typical user somewhere between 18 and 20 hours to become accustomed to the interface of this tool as well as its fundamental spreadsheet functions.
    • Tableau is a widely used tool for business intelligence, data analytics, and the display of data. This piece of software makes it easier to create individualised data visualisations that bring attention to major data discoveries in a way that is both understandable and straightforward. The straightforward drag-and-drop interface that Tableau offers makes it relatively simple to use, according to the majority of people that work with this application. On the other hand, if you are new to the field of business intelligence, it can take you anywhere from two months to six months to grasp the data visualisation aspects of this application.

    Required Skills to Become a Data Analyst

    SQL: Key Data Analyst Skill

    Structured Query, often known as SQL Language employed by the majority of structured database systems. So what exactly does the term structure mean? These are databases that record data about to items and financial transactions, as well as client lists. 

    To put it another way, this refers to normal corporate data. Just learning SQL is all that is required of you if you want to finish your investigation and have access to the data.

    On the surface, SQL appears to be quite complicated since it contains a lot of characters, code, and rows. 

    Yet, in point of fact, it's not that complicated. It is straightforward to grasp due to the obvious structure it possesses. The nice part is that once you have that structure figured out, you can apply it to any and all organised databases.

    • You will be the one to specify where in the database to look.
    • Indicate if you want calculations or certain columns.
    • Please explain how to filter the data.
    • In the event that you need to group at the very conclusion
    • Following the completion of the grouping step, you may next proceed to add further logic.
    • Someone who is familiar with SQL will inevitably assert that there is more. There is, without a doubt; nonetheless, this ought to do for the time being.

    The fact that SQL is capable of managing enormous volumes of data is one of its many strengths. So, when you are conducting your study, you should utilise SQL as much as you possibly can. 

    The information that you obtain from the SQL query should then be combined with the results of additional tools, which we will now cover. Thus leading to you gaining all of the necessary information.

    Caution is advised since SQL does not support rapid filtering, does not exhibit data in graphs, and does not do more sophisticated computations. As a result, I only utilise SQL in the beginning of my analysis the majority of the time.

    Microsoft Excel: Key Data Analyst Skill

    Excel is probably already familiar to you to some degree. If not, don't worry about it! There is a wealth of information, covering the fundamentals as well as more advanced topics, available online. The following is a list of items you have to be proficient at in Excel (this is not an entire list):

    • Tables with if statements and pivots
    • Index and Vlookup 
    • Match 
    • Vlookups 
    • SumIfs

    When I have executed the SQL query, I will have a collection of data, which, depending on the situation, I will either group, fill, or merge several data tables. In essence, you will be preparing the data for the subsequent steps. 

    After that, I could open up Excel and perform some basic filtering, pivoting, and rudimentary reasoning in order to begin gaining some first insights into the data. If it turns out to be required, draw out some preliminary graphs. 

    It's possible that I'll switch between SQL and Excel several times before I'm satisfied with the final data collection.

    Excel's problems are rather uncommon and rarely necessitate the use of additional software programmes.

    • Excel cannot manage big data sets.
    • Spreadsheets will perform slowly if they contain a large number of calculations, Vlookups, and other similar features.
    • Excel will only pick up the first match in a situation where there might be two or more matches if you use the Vlookup function.
    • When you filter based on numerous criteria at once, the process becomes more laborious.

    As a result, we are going to have to utilise Excel in conjunction with some other technologies, such as SQL or Python.

    R or Python: Key Data Analyst Skill

    From this point on, everything will move along at a little faster pace. When people tell you that you need to become proficient in R or Python, you automatically assume that this means you will need to work as a programmer. Indeed, you will be required to become proficient in coding. 

    Nevertheless, to a far smaller extent than what you need to know in order to construct applications.

    The purpose of this section is for you to acquire libraries (also known as extensions) that will assist you with data analysis and automation. There are many excellent classes that can be taken online. In addition to that, I have written a guide for beginners entitled Python Data Analysis. 

    In the beginning, you will just be required to study the most important information, which ought to help you save both time and money. You may learn more about it at this link: python course.

    Let's say you've completed the preparation of your SQL data and conducted some Excel analysis on a subset of it. Yet, the real data set is a lot more extensive. Your objective is to convert and clean the data in a quick and automatic manner, and you may also want to do some pivots. Excel is unable to process the SQL data set because it is simply too huge.

    Thus, R and Python are going to be your best bets for that. Due to a variety of circumstances. The introduction to my class provides a lot of background information about them. 

    After becoming familiar with these tools, you will be able to construct a straightforward programme in a single day which will organise your data in preparation for the subsequent phase, which is Visualization. The tools that you develop have a great potential for reuse, and you may even choose to extend their capabilities at a later time.

    Python and R, on the other hand, will grow more difficult to use since they will get more technically advanced. Yet, I suppose this is where the help of a professional data analyst is needed. Individuals who are familiar with these technologies have a greater degree of leeway to tackle a variety of data-related difficulties.

    In case you were wondering, both Python and R offer some degree of visualisation. Despite the fact that it is time-consuming to establish each graph using code, the resulting dashboard is less attractive. 

    It is ideal for situations in which you require only one or two graphs out of an enormous data source. Yet, if you are looking for dashboards, it would be much simpler for you to utilise the tool that follows.

    Data Visualisation: Key Data Analyst Skill

    Power BI is the programme that I prefer to use for data visualisation. This is one of the many reasons why it is quite comparable to other Microsoft Office programmes. As a result, the steepness of the learning curve is significantly reduced. 

    It is possible to acquire it for free if you have a corporate email address for one user, which grants you access to nearly all of the available functions and features. This will allow you to start learning much more rapidly. Tableau and Qlik Sense are two other excellent examples of useful technologies.

    Data visualisation tools allow users to merge different data sets, perform cleaning, transformations, and computations (for more expert users), and then utilise integrated visual tools to produce spectacular interactive graphs. 

    These tools are used by both people and businesses. Last but not least, it often possesses superior sharing capabilities, making it possible to disseminate knowledge to a wider audience.

    People are more likely to retain information when it is presented in a visual format; you are able to provide prompt responses to queries that have been posed on the spot within the context of data sets. As a result, a single dashboard may include an infinite number of ideas and insights that the firm may investigate and use to create value.

    Continuing with our previous illustration: After using SQL to build a data collection, performing some preliminary exploration using Excel, automating the procedures of cleaning and transformation with Python, and now it is time to develop a dashboard, you are ready. The data may be imported into Power BI using a variety of different methods. 

    While it is in the tool, you may use it to move graphs around and choose the column you wish to work with. When you have finished using the filter tools, you may lock it down and make it available for use by businesses.

    To be fair, Power BI is able to take data directly from SQL databases and finish the process of cleaning and transforming the data. Hence, in the end, choose which tool to use and when it boils down to where the data is located, what you are attempting to do, and what is just more convenient.

    Presentation Skills: Key Data Analyst Skill

    When you have finished your comprehensive analysis, you will need to devise a method for presenting the results. The reality of the matter is that the vast majority of individuals do not find an Excel spreadsheet to be very appealing. 

    They are looking for digestible summaries that include essential points, insights, interactive graphs, and info graphs.

    Thus, it is up to you to take all of the information you have obtained from comprehending the material and present it in a manner that is crystal clear and simple to take in. It makes no difference how insightful your analysis is if no one is interested in looking at it. 

    Thus, you should make certain that the outcome is so easy to get that people will. In my opinion, it is not easy. Mark Twain once quipped, "I didn't have time to write a short letter, so I wrote a long one instead." In this case, the long letter was more appropriate. It is also relevant to the display of the data!

    Power BI makes it very simple to show data in a manner that is clear and succinct. But, the combination of a PDF report, a PowerPoint presentation, and tidy Excel Tabs is another option for bringing attention to the most important aspect of the research.

    Can I Learn Data Analysis on My Own?

    You can, in fact, educate yourself independently on the principles of data analysis. As data analysis leans heavily on very focused knowledge of a discrete range of programming languages, as well as a range of easily developed soft skills, committed professionals with a knack for scripting can develop job-ready data skills over a relatively short period of time.

    For example, learning the fundamentals of SQL may be accomplished in as short as two to three weeks by someone who is just starting out. The languages used to programme computers are a little bit more difficult. 

    An individual who already has a solid understanding of the principles of programming can often take up the basics of Python in around eight weeks. A person who is already familiar with programming may be able to learn R in as little as one week of part-time study, whereas a person who is just starting out will probably require closer to three weeks of full-time study.

    Obviously, none of these abilities is utilised in a vacuum at any point. It usually takes at least 12 to 14 weeks to become proficient in all of the aforementioned talents at the basic level, in addition to a battery of additional abilities that any Data Analyst should be familiar with. 

    The fundamentals you can acquire in that time will equip you to enter the field and begin working right away; to become fully proficient at a grandmaster level (something that can really only be done on the job) may take years.

    It’s also worth mentioning that acquiring this knowledge isn’t one-and-done; as the industry continues to evolve, Data Analysts will likewise need to stay committed to ongoing learning to stay on top of changes and updates to languages, tools, and trends.


    In conclusion, learning data visualisation is an essential skill that can help individuals and organisations make sense of complex data sets. While the amount of time it takes to learn data visualisation varies depending on several factors, such as prior experience with data analysis and visualisation, the complexity of the data sets being analysed, and the tools being used for data visualisation, it is a skill that anyone can learn with practice and dedication.

    Whether you are new to data analysis and visualisation or have experience in these areas, there are many resources available to help you learn data visualisation techniques. These resources include online courses, tutorials, books, and communities of data visualisation enthusiasts.

    Learning data visualisation can be a challenging and rewarding process. By mastering data visualisation techniques, you can gain insights and make informed decisions that can help you succeed in your personal and professional endeavours.

    So, if you're interested in learning data visualisation, don't be discouraged by the learning curve. With the right resources and a commitment to learning, you can become proficient in data visualisation techniques and gain valuable skills that can help you throughout your career.

    Content Summary

    • One powerful tool that you may not be using to its full potential is data visualisation.
    • Data visualisation is the process of representing data graphically, allowing you to quickly and easily analyse complex information.
    • In this article, we will explore the many benefits of data visualisation, and show you how it can help you to improve your marketing efforts.
    • Any business intelligence (BI) and analytics system should prioritise the visualisation of data as one of its most critical skills.
    • Tableau, Yellowfin, and Microsoft Power BI are the three most effective technologies currently available for data visualisation.
    • According to Wolf, the ability to see is hardwired into the human brain, however, the ability to read does not come naturally.
    • She uses the same terminology that computer scientists would use to describe the brain, referring to it as having an "open architecture."
    • She says that the brain's system "is adaptable enough to alter — or reorganise — to fit the changing demands on it."
    • By taking into account how important data visualisation is in the sales process, Wolf's findings are quite noteworthy.
    • This will allow you to make the data.
    • The most effective data visualisation technologies, such as Yellowfin and Power BI, make trend analysis simple and straightforward.
    • While conducting trend analysis, you have a variety of different graphical options to choose from.
    • Data visualisation is a technique that analysts frequently refer to as a tool for bringing order out of chaos.
    • As a result of this, one of the most important aspects of operating a successful company is ensuring that the whole team has access to high-quality data visualisations in a timely way.
    • When it comes to communicating the performance of people, departments, and entire organisations, these KPI reports mainly rely on data visualisation technologies.
    • Using data visualisation as part of your reporting efforts (and adhering to best practices) will encourage your users to explore and manipulate data in order to uncover important factors using the aforementioned tools, which will help foster a data-driven culture as opposed to the traditional, intuition-based approach to decision-making.
    • The accuracy of the results obtained using powerful data visualisation solutions, such as Yellowfin BI and Power BI, may be improved over time through the support and integration of such technologies into the visualisation tools provided by these solutions (via auto-generated comparisons, explanations, alerts and more). Moreover, Yellowfin provides support for yet another essential analytics tool known as Natural Language Query (NLQ).
    • As a direct consequence of this, you will obtain additional measures that can be put into practice from the datasets.
    • Discovering previously unknown patterns is made easier by using data visualisation tools.
    • They assist you in discovering patterns and links among complicated datasets that are not immediately evident without their assistance.
    • Because these tools make use of cutting-edge technology to uncover previously undiscovered patterns.
    • According to findings published by experts at the University of Pennsylvania School of Medicine, the human retina is capable of transmitting data at a rate of around 10 million bits per second.
    • The notion of how rapidly our brains can detect pictures and make sense of them is at the heart of data visualisation.
    • Data visualisation and the use of online technologies that facilitate data visualisation assist make it easier to grasp the information that is provided rapidly.
    • The ability to graphically exhibit data and derive insights from that data is the goal of the online data visualisation tool.
    • The ability to immediately get a feel for the temperature of the business is extremely valuable to everyone in the company, from salesmen to CEOs.
    • It is to derive meaning from the data and make use of the information for the advantage of the organisation.
    • By incorporating data visualisation tools into their business strategy, businesses can gain a significant edge in the market.
    • If you haven't already, now is the time to consider incorporating data visualisation tools into your business strategy.
    • Start exploring data visualisation tools today and see how they can benefit your business.

    Frequently Asked Questions

    Data visualization is the process of representing data and information in visual formats, such as charts, graphs, maps, and diagrams. It helps to communicate complex data patterns, trends, and insights in a more understandable and visually appealing manner.

    Data visualization is important because it allows us to comprehend and interpret large and complex datasets quickly and effectively. It enables us to identify patterns, relationships, and outliers and communicate data-driven insights to stakeholders more efficiently, leading to better decision-making and understanding.

    Using data visualization has several benefits. It helps in identifying trends, patterns, and correlations that might not be apparent in raw data. It aids in exploring data from different angles and gaining deeper insights. Data visualization also enhances data storytelling, making it easier to communicate findings and engage with the audience.

    There are various types of data visualizations, including bar charts, line graphs, scatter plots, pie charts, histograms, heatmaps, treemaps, and geographic maps. Each type has its own strengths and is suitable for different data types and purposes. Choosing the right visualization depends on the data characteristics and the story you want to tell.


    To create effective data visualizations, it is important to follow some best practices. These include understanding the audience and their data literacy level, choosing the appropriate visualization type for the data and message, keeping the design simple and clutter-free, using color and visual cues purposefully, providing clear labels and titles, and adding context and storytelling elements to make the visualization more meaningful and engaging.

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