One of the most common questions that comes up when we counsel aspirants at Springboard who want to get started with learning data science or analytics is – “I don’t know where to begin !”.

Often many aspirants conclude that getting started with Python or R  programming language is one of the best ways to begin learning data science or analytics. While this does help you choose the programming language to get started with, it does not tell you the skills you should focus on. Choosing the skills can be overwhelming for a beginner. Mentors at Springboard suggest that data aspirants should always begin with learning data visualization and then focus on data manipulation.

Data Visualization – A Wise Investment and a Must Learn Skill 

According to a 2019 LinkedIn Research,  data visualization is one of the top skills graduates are learning  6 months after graduation.

“A picture tells a thousand words,” said Frederick R. Barnard in 1921. Or in this case –“A picture is worth thousands of lines of data.”

Almost 100 years later, Frederick’s statement has, if anything, become true for every data aspirant out there.

Human eyes are drawn to patterns and colors. We can easily identify green from blue, a circle from a triangle. We live in a visual culture comprising of art and advertorials to TV shows and movies. Data visualization is another kind of visual art that helps decision-makers visualize analysis results to identify new trends and outliers quickly for profitable business decision-making.

Data visualization today is a must-learn skill for any career; from professors in the university trying to make sense of students’ exam results to data scientists trying to innovate and develop the next big thing using AI. It is very difficult to find a field where people might not want to better understand their data. Every STEM field benefits from making data more understandable – and so do other fields in service industries, governments, education, finance, healthcare, marketing, consumer goods, sports and so on. 

With the concept of citizen data scientists on the rise, skill sets are continually changing to adapt to the data-driven world. Professionals who can use data to make business decisions and visuals to tell stories of when the data informs the what, when, where, who, and how are likely to succeed in today’s data-focused business world. Data visualization is a critical skill for every data aspirant as it sits in the middle of analysis and data storytelling.

Data Visualization – The Defacto Standard for Business Intelligence

According to Nucleus Research, business intelligence, when combined with data visualization capabilities, offers an ROI of $13.01 back per dollar spent. 

Research by  Cornell University found that a scientific claim presented in numbers or text is considered accurate and truthful by 68% of the people. But if the same claim is supported by a simple graph, the number increases to 97%.

Regardless of whether you are in operations managing manufacturing efficiencies or in sales trying to convince leads to buy your services/products. Or a manager looking forward to optimizing employee performance or in any other field – everything today is measurable with data and all decisions can be scored against various data-driven key performance indicators. Analyzing thousands of 0’s and 1’s with the hope of finding something meaningful does not come naturally.  You need to be skilled in data visualization to better understand what is happening with the business and explain it to your stakeholders to persuade them to make the right decisions.

For aspirants who don’t know what exactly is data visualization –

It is the process of transforming the analysis of a data set into visual representations like charts, graphs, or other appealing formats for effective interpretation of analytic insights. In simpler terms, it turns information sets into effective visual stories and provides insights in a way that the target audience can understand. For instance, converting the numbers from an excel sheet into a series of pie charts or bar charts makes it easy to digest the information. The main goal of a good visualization is to add meaning to a complicated dataset so that the message is clear and concise.

Data visualization helps a data scientist/analyst –

  • Get a clear overview of the factors that influence customer behavior.
  • Predict sales volumes
  • Gain a better understanding of areas that need improvement or attention 

Why every Data Scientist / Analyst Must Learn Data Visualization?

  • To Discover Insights 

By making use of visualizations of business information, data scientists/analysts can see huge volumes of data in a clear and cohesive manner and derive conclusions. This makes data more accessible and less confusing. Visualizations foster faster analysis of information to help data scientists/analysts tackle problems and find solutions in a more timely manner. Data visualizations are perhaps the highest ROI method for gleaning meaningful insights from data. (It becomes even more powerful when combined with data manipulation.) As you sharpen your data visualization skills, you are likely to reach a bottleneck wherein the data will either be in incorrect format or you will require more data than you already have. This is the point where you need to learn and do some data manipulation to aggregate the data, subset your data, or rather transform the data for more sophisticated data exploration that will help you glean valuable insights.

  • Identify Correlations and Patterns with Ease

As data volume increases, visualizations help analysts manage the influx of new information, making it easy it spot novel trends and relationships. Some of the correlations and relationships might be obvious, but others are not. Using visualizations, analysts can identify parameters that are highly correlated in a complicated dataset and connect the dots to make sense of those relationships.

  • Spot Emerging Trends

Analysts can identify emerging trends that will give them an edge over their competitors and have an impact on the bottom-line of business operations. For instance, visualizations help identify any outliers that affect the customer churn and product/service quality so that such issues can be addressed much before they turn out to become bigger problems for the business.

By now, it’s evident that if you are a data aspirant, you need to have data visualization skills in your 9 to5 life. Remember that good data visualization theory and skills will increase your ROI as a data scientist/analyst. When you are learning this skill, focus on the best practices and explore your own interest of data visualization tools and products. Data visualization is an important skill that is here to stay. Build a strong foundation of analysis, storytelling, and exploration with Springboard’s comprehensive data science course that has been designed in accordance with the need of all data aspirants.