We’ve all heard of data visualization and Python, but do you know how they work? Data visualization is taking large amounts of data from various sources and turning it into meaningful visuals that help make sense of the information. Python is a programming language that can create powerful applications to crunch this data and make it more digestible. This blog post will dive into what makes data visualization and Python essential for today’s businesses. We’ll explore how they interact and why they are crucial and give tips on getting started with them both!

What is Data Visualisation & its importance for businesses in 2023?

Data Visualisation is vital for businesses in 2023 because it allows businesses to understand their data better. Data Visualisation helps businesses to see patterns and trends in their data, which enables them to make better decisions. Python is a programming language that is well-suited for Data Visualisation. Python has many libraries that can be used for Data Visualisation, such as matplotlib and seaborn.

What is Data Visualisation, and how does it works using Python to bring better output?

Data visualization is the process of creating visual representations of data. This can be done using a variety of methods, including charts, graphs, and maps. Python is a popular programming language that can be used to create data visualizations. Several libraries can be used to create data visualizations in Python, such as matplotlib, seaborn, and bokeh.

Visual visualization can explore data, find patterns, and spot trends. It can also communicate data, tell stories, and enable decision-making. Python’s versatility makes it a good choice for data visualization. It’s relatively easy to learn, and there are many libraries available that make it possible to create sophisticated visualizations.

Why should you make a career in Data Analytics, and what is the career scope?

There are many reasons to make a career in data analytics. The first reason is that data analytics is one of the most in-demand fields. Companies are always looking for ways to understand their customers better, and data analytics can provide that insight.

Another reason to pursue a career in data analytics is the salary. Data analysts are some of the highest-paid employees in the tech industry. In addition, the job market for data analytics is expected to grow by 27% over the next ten years.

Lastly, data analytics is a field that is constantly evolving. As new technologies emerge, data analysts will need to learn how to use them to extract insights from data. This means that a career in data analytics can be both challenging and rewarding.

The scope of a career in data analytics depends on your skillset and experience. Junior data analysts typically conduct basic analyses and report their findings to senior staff. Senior data analysts may lead projects, develop new methodologies, or manage teams of analysts. Data scientists are the top professionals in the field and often work on complex problems such as predictive modeling or machine learning.

Data analytics certification course for a Career in Data Visualisation?

A data analytics certification course can help you make a career in Data Visualisation in several ways. Firstly, the course will provide the necessary skills and knowledge to understand and work with data visualization tools and techniques. Secondly, the system will also give you practical experience working with data visualization software so that you can apply your skills to real-world scenarios. Finally, the certification will demonstrate to potential employers that you can visualize data effectively, an essential skill for any data analyst.

Learn and Grow with Imarticus Learning:

Data visualization using Python is a new thing with Postgraduate Program in Data Science and Analytics offered by Imarticus Learning. It is the best-bet for fresh graduates or early career professionals with STEM/Tech backgrounds.

You get to learn the real-world application of data science and build analytical models that enhance business outcomes. This course is ideal for professionals who wish to develop a successful data science and analytics career. Throughout this learning journey, you gain practical knowledge about the implications of data science and analytics in real-world businesses and get prepared to work in the industry as a data science professional.

The leading-edge curriculum also covers fundamentals and more complex data science and analytics concepts. The best part is that the program adds a foundation module for those without programming knowledge.

Course USPs:

• Classroom & Live Online Training
• 1500+ Students Placed
• 22.5LPA Highest Salary
• 52% Average Salary Hike
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• 6 Months Program

For further details, contact the Live Chat Support system or visit one of our training centres in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

Data is present everywhere. We are surrounded by different forms of data, be it Google Maps or diet planners. One of the major reasons for the widespread use of data is the ease of communication. Visuals communicate more easily and effectively than words. Medieval paintings are a testament to the fact that people have used data visualisation since time immemorial for conveying complex ideas.

Keep reading to understand the meaning and logic behind data visualisation.

What is Data Visualisation?

Raw, written data is available in both organised and unorganised forms. Using data visualisation, it is translated into visible forms like graphs, pie charts, videos, GIFs, reports and images. You do not have to go through large chunks of data. You can quickly understand the trends in the data through visuals. Companies use these techniques for the process of decision-making.

Four ways to apply data visualisation in real life

Raw data is of no use to a company. The primary aim of processing data and generating visuals is to easily identify the patterns in big data. Companies all around the world are seeking individuals with knowledge of big data and its applications. Let us understand how to perform the visualisation of big data in the real world.

Bar Charts: Bar charts are one of the best visualisation options for comparing two or more quantities. You can use it to analyse your income and spending in different months of the year. Companies can use this tool for measuring the performance of their internal departments against each other.

Mapping: Maps are used for understanding the distribution of resources or products for a company. You can use a map for planning the itinerary of your international or local trips. If a company has operations overseas, a map helps in comparing the performance of different branches against that of the headquarters.

Matrix: A matrix is a visual representation that displays the existence of several factors (or groups) in a general location. If you have a big data set, a matrix helps in understanding the relationship between different elements of this data set. You can use a matrix for identifying the cause of a problem. Thus, you can allocate resources according to the needs of different areas.

Box Plots: A box plot helps in understanding the range of a data set and several other characteristics of a given group. From this representation, you can understand the outliers, range and variance of a particular data group. It can then be compared with others. Using this representation, you can check if there are any unused groups in a data set.

Is data visualisation scientific?

Data analytics (or visualisation) is much more than turning written data into visuals. The process of accomplishing this goal has a lot of scientific reasons. They show how humans gather and process any type of information.

According to several psychologists, the human thinking process can be generally categorised into two systems. System one is responsible for reflex thoughts. These refer to the ideas or solutions that the brain gives without processing any information. On the contrary, system two generates thoughts and solutions based on the results obtained after processing the information received from outside.

When system one receives data in a familiar format, it becomes easier for system two to process it more efficiently. System one is used to immediately process the information received from our visual senses. Thus, it is better if data is represented visually to allow better decision-making.

Start a Data Analytics Course with Placement

Imarticus Learning offers PG in Data Analytics and Machine Learning. This course can be helpful for aspiring data scientists. It guarantees job interviews opportunities in renowned companies. These include HSBC, Standard Chartered, ICICI Bank, Infosys, Wipro, HDFC Bank, Deloitte, Oracle, and many more. The curriculum is designed by professionals to include the latest job-relevant skills using tools, packages and programming languages. They include Python, Spyder, pandas, NumPy, OpenCV, Colab and TensorFlow.

This course demands the completion of 25 projects. These will help you understand concepts better and improve your job prospects. These real-world projects are completed in class. You can add these to your resume as a part of your portfolio. Additionally, you can be a part of competitions like Hackathon. You can also participate in mock interviews to improve your skills. The professionals at the institute will help you create LinkedIn and GitHub profiles to attract employers.

Learn data analytics by visiting the website. You can interact with professionals for solving your queries through call or chat support on the website. You can also visit one of our centres.

Many different types of data visualizations can be used in real-world scenarios. Some common examples include bar graphs, line graphs, pie charts, and scatter plots. Each type of visualization has its strengths and weaknesses, so it is vital to choose the right one for the specific scenario.

For instance, bar graphs are good for comparing data points side-by-side, but they can be challenging to read if there are a lot of data points. Line graphs are good for showing trends over time, but they can be difficult to compare multiple data sets. Pie charts are good for showing proportions, but comparing data sets with different total values can be difficult. Scatter plots are good for showing relationships between two variables, but they can be challenging to interpret if there are a lot of data points. 

Choosing the correct type of visualization is important, but it is also important to ensure it is effective. This means using colors and labels effectively, selecting an appropriate scale, and avoiding cluttered or confusing visuals. With so many things to consider, it can be helpful to look at examples of data visualizations that have been effective in real-world scenarios.

What are the Key Trends in Data Visualization in 2023? 

Data visualization is an incredibly important tool for understanding data, and the trends in data visualization are constantly evolving. Here are some of the key trends in data visualization that we anticipate seeing in 2023:

1. Augmented reality (AR) and virtual reality (VR) data visualizations will become more common as technology advances.
2. Visualizations will become more interactive, allowing users to manipulate and explore data in new ways.
3. Data visualizations will become more accessible, emphasizing mobile-friendly and web-based solutions.
4. We will see more use of natural language processing (NLP) to generate insights from data visualizations.
5. Personalized data visualizations will become more prevalent, tailored to the specific needs of individual users.

Key skills and techniques to learn from data visualization trends

To create compelling data visualizations, there are some key skills and techniques that you can learn from studying the trends in this field. Here are some of the most important things to keep in mind:

-Choose the right chart type for your data: A wide range of different chart types are available, and each one is better suited to displaying certain kinds of data. When choosing a chart type, select one that effectively showcases the information you want to communicate.

-Make use of color and other design elements: Colour can be used to significant effect in data visualizations, helping to highlight certain aspects of the data or drawing attention to particular patterns. Other design elements, such as line thickness and labels, can also be used to add further context and clarity to your visualizations.

– Keep it simple: Data visualizations should be easy to interpret at a glance. Avoid using too many colors or charts, and stick to a clear and concise layout.

– Use interactivity wisely: If using interactive elements such as tooltips or filters, ensure they genuinely add value and do not simply clutter up the visualization. Interactive features should help the viewer understand the data more quickly, not confuse things.

The future of data visualization and the input a data Analytic Course can provide

The future of data visualization is incredibly exciting. We are only just beginning to scratch the surface of what is possible with data visualization, and there are many new and innovative ways to visualize data that we have yet to explore. A data analytics course can provide the skills and knowledge you need to create unique data visualizations. You will learn how to communicate complex information using visuals effectively and how to design visualizations that are both informative and visually appealing.

Learn and Grow with Imarticus Learning:

Are you looking for a postgraduate Program in Data Science and Analytics? Here is an excellent opportunity to build your data science and analytics career with a job guarantee program. The course benefits fresh graduates/early career professionals with Tech backgrounds.

Course USPs:

• Classroom & Live Online Training
• Learn the real-world application of data science
• Build analytical models that enhance business outcomes.
• Job interview guaranteed for recent graduates and professionals. 
• Gain practical knowledge to work as a data science professional.
• Students placed at over 500 top-tier partner organizations
• Job-specific Curriculum for practical learning 
• Gain experience in Python, SQL, data analytics, power BI, and Tableau
• One-on-one career counselling to ensure employment.

For further details, contact the Live Chat Support system or visit one of our training centres in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

Color is an important element in data visualisation as it can help to draw attention to specific areas of your data, highlight patterns and make your visualizations more aesthetically pleasing. There are a few things to consider when choosing colors for your data visualizations:

– The purpose of your visualization: What are you trying to communicate with your data? Choose colors that will support this message.

– The type of data you are visualizing: Some colours work better for specific data types (e.g., using warm colours to represent increasing values or cool colours to represent decreasing values).

– The audience of your visualization: Different audiences may react differently to specific colors. Consider who will be looking at your visualization and choose accordingly.

There are many resources available online to help you choose color palettes for your data visualizations, including colour tools like Adobe Color CC and Coolors. Co. Try out a few different options and see what works best for you and your data!

How to choose the right color palette based on the type of data

There are a few key things to consider when choosing a color palette for data visualizations:

-The type of data being represented

-The purpose of the visualization

-The audience of the visualization

For example, if you are visualizing categorical data, you will want to use a color palette that is easy to distinguish between different categories. If you visualize quantitative data, use a color palette to show the magnitude or change over time. And if you are creating a visualization for a specific audience, you will want to consider what colors they are most likely to respond to.

Using Color to Emphasize Trends and Insights and Best Practices for using color in data visualization

Regarding data visualization, color can be a powerful tool for emphasizing specific trends and insights. However, choosing the right colors for your data can be challenging. In this section, we’ll share some best practices for using color in data visualization and tips for finding the right color palette for your project.

When choosing colors for your data viz, it’s essential to keep in mind both the message you’re trying to communicate and the audience you’re communicating it to. Specific colors can convey different meanings, so it’s important to choose colors that will support the message you’re trying to communicate. For example, if you’re visualizing data representing growth or progress, consider using green or blue, as these colors are often associated with positive connotations. On the other hand, if you’re visualizing data representing decline or danger, you should use red or orange, as these colors are often associated with negative connotations.

In addition to considering the message you want to communicate, it’s also important to consider the audience you’re communicating it to. Different audiences will have different reactions to other colors. Choosing colors that will resonate with your specific audience is vital.

Enroll in a Data analytics course with Imarticus Learning:

If learning python for data visualization is your goal, this data analytics course from Imarticus Learning can help you understand data visualization better. During this course, you get the following:

1. Understanding of data: You get to learn how to work with and analyze data, which is a key component of data visualization. This understanding allows you to choose the right visual representation for your data and create meaningful insights from your data. 
2. Knowledge of data visualization techniques: The course includes lessons on data visualization techniques, such as bar charts, line charts, scatter plots, and heat maps. This knowledge helps you choose suitable visual representations for your data and effectively communicate your insights. 
3. Use of tools: You get lessons on using popular data visualization tools like Tableau, Power BI, and D3.js. This hands-on experience helps you become proficient in using these tools and enables you to choose the right tool for your needs. 
4. Understanding of color theory: A data analytics course may include lessons on color theory, which is critical for choosing the right color palette for your data visualization. This knowledge helps you create visually appealing and effective visual representations of your data. 
5. Best practices: Finally, the course covers best practices for data visualization, such as how to effectively label your charts, choose the right scales, and handle outliers. This understanding helps you create visually appealing and effective visual representations of your data.

Overall, this course strengthens your foundation for data visualization, allowing you to make meaningful insights from your data.

Course USPs:

Job Interview Guarantee

Live-Learning Module

Dedicated Career Services

Real Worl Projects

KPMG India COE Organised Hackathons

For further details, contact the Live Chat Support system or visit one of our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

In the world of data science and analytics, data visualisation is one of the biggest requirements today. It combines the qualities of visual storytelling and data analysis. Since the visualisation of data can be used in multiple ways, it is one of those components that cannot be neglected at all in the data analysis industry.

For facilitating this component in analytics, Tableau is one of the most effective tools. It helps you create a visual output from the massive data sets in the spreadsheet. When this data is represented graphically, not only does it have more impact, but it also helps to spot the trends easily in data. It is certainly the biggest business need.  

Data visualisation makes it easy to understand the information available in numbers in an excel sheet. So, if you are planning a career, learn more about it.

What is data visualisation?

Data visualisation can be defined as using the raw data, usually numerical data in Excel Spreadsheets, to make a pictorial or graphical representation with the help of the information provided. This technique helps to understand data in a relatively easy manner. After that, you can transfer or present those insights to the parties that require it. In this regard, you must be familiar with some of these visual techniques. These could be charts, maps, graphs, etc. The main objective of this technique is to convert all the large data sets into graphical representations to make them easy to interpret and understand, even for the layman.  

What is the Significance of Data Visualisation?

We have already mentioned that data visualisation can convert a large amount of data into an easily understandable visual object. However, there is a deep underlying need for data visualisation techniques. They are:

1. The human subconscious can process a lot of information with the help of vision. So, data visualisation can be used to communicate the insights and patterns effectively from the provided data set. If the right colours are used, data can be more impactful. No wonder the ads and movies today are so colourful; they get the message right across to the audience.
2. Visualisation of data saves a lot of time while getting the message across to the leaders of an organisation or the stakeholders. It also reduces the margin for error while using these insights since everything has been represented in such a clear and concise manner. This helps in the rapid progression of many industries, especially the ones that are solely dependent on data and graphs.

These two are the underlying reasons why data visualisation is important. If we have to be progress-oriented, then we need to rely more on data visualisation. This is the reason why data analytics has become such an important field in today’s world.

The Considerations We Need to Make for Data Visualisation

Before you dive deep into converting your data into a visual object, make sure you are able to answer the questions that are given below:

• What is the objective of data visualisation?
• What is your target audience?
• What is the message that you want to convey?
• Have you demarcated the KPIs (Key Performance Indicators) of this process?
• Does the historical data representation have the proper implementation while reporting?

How to Bring Data Visualisation to Life?

Thankfully in today’s times, we have multiple data visualisation tools that are making our lives easier and are thus being used heavily in the data science and analytics industry. Some of the popular tools for visualising data are:

• Tableau
• Power BI
• Excel
• Python and its graphical libraries (NumPy, Pandas, etc.)

You can learn about these tools in a PG in data analytics from a good college/university/institute. The most significant benefit of using these data visualisation tools is that it helps in the development of interactive dashboards, which help us to track, analyse and display data in whichever way we like. This allows us to get many insights from data that even we could not have imagined.

But right now, the immediate need for data visualisation is in setting up an application programming interface (also called an API), which we can integrate for streamlining the data-gathering process from multiple sources. This helps to cut down on a lot of work and reduce the efforts in developing various visualisations of data like charts, tables, graphs, dashboards and infographics.  

After learning the various benefits of the visualisation of data, you must be wondering where you could learn data analytics. Do not worry, as you have Imarticus Learning, which has a good data analytics course with placement. Here, you will learn the process of using various data analytics tools and build the best dashboards from the top faculty in the country. Moreover, you will receive the placement assistance needed to find a job in one of the leading firms in the industry.

How I mastered data visualisation with these techniques

A while ago, I wanted to learn how to create stunning data visualisations. I started by reading many articles and watching videos about the subject. After a while, I realised that certain techniques were used repeatedly by the best data visualisers in the field. In this article, I will share the techniques I mastered to create beautiful data visualisations.

Be Clear On The Purpose Of The Visualisation

The first step to creating a great data visualisation is to be clear on the purpose of the visualisation. What are we trying to communicate with our data? What story are we trying to tell? Once we have a clear understanding of the purpose, we can start thinking about the best way to communicate our data.

Let’s say we want to create a visualisation that shows the number of sales made by our company each month. The purpose of this visualisation is to show the company’s growth over time. With this in mind, mapping how data can be communicated becomes easier.

Will we use a line graph to show the monthly sales over time?

Or will we use a scatter plot to show the relationship between the number of sales and the amount of money spent on marketing each month? The possibilities are endless, but ascertaining the purpose of the visualisation is crucial.

Understand The Data

The next step is to understand the data we will visualise. This means knowing what the data represents and how it is structured. The dataset may contain hundreds of columns, but it’s critical to focus only on the relevant data.

For example, if you only want to show the monthly sales for your company, you don’t need to worry about the data for the number of employees or the amount of money spent on marketing.

We should also think about the structure of the data. Is it in a format that is easy to work with? For example, if we are using a CSV file, is the data in a single column or multiple columns? If the data is in multiple columns, is it in a format that can be easily joined together?

The last thing to think about is the size of the data. How many rows and columns are in the dataset? Is it too big to work with? If so, we may need to consider using a sample of the data.

Define Our Audience

Before creating the visualisation, we must think about who our audience is. What are their needs? What do they want to see? What level of detail do they need?

The visualisation should be designed to communicate the data in a way that is easy for our audience to understand.

For example, let’s say we want to create a visualisation that shows the number of sales made by each salesperson. The audience for this visualisation might be the managers of the sales team.

They will be interested in seeing which salespeople are performing well and which ones are not. They will need to see the data for each salesperson so that they can make decisions about improving the team’s performance.

Develop Our Visualisation

Once we have selected the data and defined our audience, it’s time to start developing the visualisation. This is where the fun begins!

There are a few things to consider when developing the visualisation. The first is the type of visualisation. There are many different types of visualisations, each with its strengths and weaknesses. We must select the type of visualisation best suited to the data and the story we want to tell.

The second thing to think about is the layout of the visualisation. How are we going to arrange the data? What order should the data be in? How can we make the visualisation easy to understand?

The third thing to consider is the style of the visualisation. What colours and fonts should we use? How can we make the visualisation visually appealing?

Finally, we need to determine the interactivity of the visualisation. What features can we add to the visualisation to make it more engaging?

Test And Improve

After we have developed the visualisation, it’s important to test it. Show it to people and see what they think. Ask if they can understand the data or if they find the visualisation visually appealing.

Conclusion 

Creating data visualisations can be a lot of fun. But remember that there is a process to follow. We must first select the data, then define our audience, develop the visualisation, and finally test and improve it. By following this process, we can create informative and visually appealing data visualisations.

If you want to learn more about data visualisation, check out our Postgraduate Program in Data Analytics & Machine Learning. In this course, you will learn everything about data visualisation – from basic to advanced techniques.

If you need more help, contact us through chat support, or drive to our training centres in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad. We would be happy to assist you.

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