Here´s why data visualization
is a key to data scientist
What is Data Visualization?
Data Visualization is a common graphical representation process used to map out information. Visualizing data is a helpful way to make it accessible and understandable to everyone. This is done by using visual tools including charts, graphs, and maps.
Today, we share with you five reasons why data science can be a valuable asset for your finance department. As you know, we at Shapelets love to find new innovative solutions to help businesses achieve success. Let’s bring some context first.
If you wish to keep up-to-date with the latest trends in Data Visualization, you are welcome to watch our Free Webinar on Data Ingestion and Visualization on Thursday. We will show you a live data visualization app that we think will be helpful for your project.
Why Data Visualization is one of the hardest tasks in Data professionals’ daily work?
Data visualization is an important tool for identifying qualitative understandings in Data Science and Machine Learning fields. This can be helpful when exploring a dataset and extracting information that can help with identifying patterns, insights, corrupt data, outliers, and much more.
If we have some knowledge about data visualizations, we can use it to identify key factors in plots and charts that are very helpful to ourselves and stakeholders.
Data Science is the process of using data to inform decisions, and it is one of the most important skills a business can have. By understanding data, we can help others understand its importance and use it to improve not only business processes but also their lives. Visualizing data is an essential part of data science, and is one of the most powerful tools available for communicating data insights.
As a data scientist, you can’t just start a project without first getting the green light from stakeholders. Some stakeholders will not understand Data Science concepts and processes at all until you explain them well.
To summarize this process, you can create a visualization that best describes the proposed process, as well as the timeline involved. There are many ways to approach this visualization. Data Visualization can be done with a variety of libraries, including Matplotlib, Plotly, Alter, Tableau, or Microsoft Power BI.
Data Science can be complicated, but there are ways to make it easier to understand by using data visualizations. We looked at several reasons why a Data Scientist should know not only programming and statistics but also visualization methods.
The power of data visualization and some #ShapeletsTips:
The power of data visualization can help you make informed decisions about your business. Our brains are better equipped to process visual information quickly than text, which is why data-driven design is so effective. By seeing the data, it is easier for your brain to process, understand, and remember the information presented.
However, hitting a few charts together doesn’t mean you’re doing data visualization design. You’re not doing it well; you might be damaging your brand, your work, and your project. Good data design is essential for successful analytics. There are many different types of subpar data design, and it can be confusing to use. In these cases, your credibility may be at stake, and nobody wants that.
Even if you are misrepresenting data, if you’re not it in its optimal form, you’re doing a disservice to your audience (this may be an important stakeholder). Fortunately, there are many simple things you can do to ensure your data stories have the impact they should. Our team has put together some great tips to help you improve your data visualization skills. If you’re ready to take your design to the next level, we’ve got some advice for you. We hope they help:
1) Choose the chart that tells the story. There may be more than one way to visualize the data accurately. But be mindful of things like superfluous visuals, clutter on the page, and ornate embellishments. The great thing about data visualization is that it can help tell a story in a more effective way. Trust the system. Predicting the impact of short-term asset value.
2) Design for understanding is my top priority when creating content. I aim to make sure that my content is easy to follow and understand, no matter who is reading it. Once you’ve created the visualization, you can step back and look for any data that might be added, adjusted, or deleted for readers to understand. Consider simple elements. These subtle adjustments make a big difference.
3) Visual consistency so that the reader can compare at a glance. Think about what you want to achieve and then choose a few things to focus on. Don’t try to compare everything at once – it will be overwhelming.
4) Don’t over-explain. If the copy already mentions a fact, the subhead, callout, and chart header don’t have to reiterate it. Sometimes we see data visualization and copy working against each other instead of together. Make sure this does not happen to you.
5) Keep chart and graph headers simple and to the point. There’s no need to get over creative, verbose, or flowery. Keep any descriptive text above the chart brief and directly related to the chart underneath. Don’t worry about getting everything correct – just focus on understanding the material as quickly as possible
6) Use callouts wisely. Callouts are not there to fill space; they are meant to provide valuable information. They should be used intentionally to highlight relevant information or provide additional context.
7) Don’t use distracting fonts or elements. Sometimes you do need to emphasize a point. If so, only use bold or italic text to emphasize a point. Also, color is a great tool when used well. When used incorrectly, this can easily distract and misdirect the reader. Make sure to use data visualization wisely in your design so that your data is interpreted correctly and effectively.
8) Be careful with positive and negative numbers. It may seem funny but don’t use red for positive numbers or green for negative numbers. Those color associations will automatically flip the meaning in the viewer’s mind.
9) Select colors appropriately. Some colors stand out more than others, giving unnecessary weight to that data. Also, make sure there is sufficient contrast between colors. If colors are very similar (light gray vs. light, light gray), it can be hard to tell the difference. If you use any contrast color-rich combinations such as red/green or blue/yellow.
10) Try to avoid patterns. Stripes and polka dots sound fun, but they can be very distracting. If you are trying to differentiate, say, on a map, you can use different saturations of the same color. On that note, solid-colored lines are very helpful (no dashes).
11) Double-check that everything is labeled. Make sure everything that needs a label has one—and that there are no doubles or typos. All labels should be unobstructed and easily identified with the corresponding data point.
12) Order data intuitively and consistently. There should be a logical hierarchy and the items in your legend should mimic the order of the chart. Random patterns that are difficult to interpret are frustrating and detrimental to what you’re trying to communicate.
If you want to be a successful data visualization and storytelling expert, you need to be up-to-date on the latest best practices and be attentive to every step of the process.
As our goal here is to help you, you are welcome to watch our Free Webinar on Data Ingestion and Visualization. We show you a live Data Visualization app, which we are confident will help you with your project. Register for free here.
Try to understand the data and where it comes from. It’s key to understand and use the right sources, then you only have to apply the right tool that will help you make the most of it and get the right insights. All in all, data storytelling relies on your ability to extract and shape a cohesive narrative around the insights that matter most.
If you still need a little help with your data storytelling, don’t be afraid to seek outside help. We’re also happy to talk through any of your data visualization projects!
Real-time analytics allows to analyze data as it happens, rather than waiting for it to be collected. This lets for more accurate predictions about future events with data science. In fact, business intelligence and data analysis have relied mainly on structured data for the past few decades.
When analyzing data, the data imported is specific, well-organized, and could be ingested in batches over long intervals. With the increase in access to social media and online banking and the proliferation of smartphones, data emissions have been growing rapidly. Consequently, it has become more difficult to keep track of and manage this big data.
Today we require the ability to process a continuous flow of unstructured data without getting bogged down. The goal of automating real-time data inflow analysis is to help businesses understand what is happening now and use machine learning and predictive models to have a competitive edge. At its core, real-time analytics can help to personalize the customer experience to an incredibly detailed level.
Financial institutions are concerned about fraud, and it is one of their top priorities and even more because of the increase in technology. Traditional fraud detection techniques use a rule-based model that looks for unusual activities. This can often flag actions that have been considered fraudulent or that have violated company policy. The second issue with traditional fraud detection is the increasing amount of data.
For example, let’s take a look at how banks deal with their transaction fraud detection strategies. Digital payment (debit card, electronic payment, credit card) accounts for more than 70% of the total transactions while the consumption of goods and services is developing at the same time. This means that old models can’t keep up with the data flow and are slower, so they need human interaction to prevent fraud. Additionally, because people are already familiar with these models, it’s harder to spot potential fraud using them. By contrast, machine learning algorithms are able to handle a vast amount of data with many variables to find hidden correlations between user behaviors and the likelihood of fraudulent actions, and with a low human error rate.
Algorithm trading is already more efficient than human traders and does not involve emotions, which makes it an ideal choice for traders. The use of complex mathematical calculations to help advisors and financial companies facilitate the making-decision process to increase profits.
As is evidenced by the need for information, an algorithm that is well-equipped to handle and analyze data would be very useful.
Daniel is a Data Engineer for Shapelets. He is an integral part of the back-end development team, where we develop a high-quality platform ensuring the best product design with valuable functionalities for data scientists. Daniel supports the team with his diverse background in Software Engineering.