Because our world is becoming increasingly data-driven, it is critical to have multiple methods to access and comprehend data. After all, the demand for data capabilities grows year after year. Employees and employers at all levels must comprehend data and its implications.

We demonstrate the importance of data visualization in this context. Visualizing data in the form of dashboards is the primary tool for many firms to analyze and distribute information, with the goal of making data more accessible and intelligible.

In this article, we’ll cover:

• The definition of data visualization.
• Why is data visualization is essential?
• Data visualization and big data.
• General Types of Visualizations.
• Data visualization use case(LinkedIn job scraping)

 

The graphical display of information and data is known as data visualization. Data visualization tools, which include visual components such as charts, graphs, and maps, make it easy to view and comprehend trends, outliers, and patterns in data. Furthermore, it allows employees or business owners to deliver facts to non-technical audiences without confusion.

Data visualization tools and technologies are critical in the Big Data age for analyzing huge volumes of data and making data-driven decisions.

 

It allows people to perceive, interact with, and comprehend data. The appropriate impression, whether basic or complicated, may get everyone on the same page, regardless of their expertise.

Every Major in science, technology, engineering, and Mathematics disciplines, as well as government, banking, marketing, history, consumer products, service sectors, education, sports, and so on, benefits from data understanding.

Visualization is one of the most important professional skills to master. The more value you receive from this information, the better you can visually explain your arguments, whether on a dashboard or slides.

Professionals must be able to use data to make decisions and provide stories about when, how, and why data is reported.

 

As the “age of Big Data” grows, visualization is becoming a critical tool for making sense of the billions of rows of data generated every day. By translating data into a more intelligible format and displaying trends and outliers, data visualization helps to convey stories. A good visualization tells a story by removing noise from data and emphasizing key points.

It is not, however, as simple as drawing a graph or referring to an infographic’s features. A careful balance of design and function is required for effective data display. The simplest graph may be too repetitive to be noticed, or it may convey an important message.

• Table: A series of figures arranged in rows and columns.
• Chart: The data is provided in a tabular and graphical style, with data displayed along two axes. It could take the form of a graph, diagram, or map.
• Geospatial:  A data visualization that uses distinct shapes and colors to highlight the relationship between different types of data and specific locations on a map.
• Graph:  A diagram consisting of dots, lines, segments, curves, or regions that depict different variables about one another, typically along two axes at a right angle.
• Dashboards: A collection of visualizations and data displayed in one location to aid in data analysis and presentation.
• Infographic: Data is represented via a combination of graphics and text. Typically employs charts or diagrams.

 

Data visualization use case (LinkedIn job scraping)

Of course, one of the best ways to understand the perception of data is to see it.

We’ll visualize different data variables scraped from LinkedIn jobs. Having scraped over 350 jobs from LinkedIn, we displayed them in interactive dashboards to show some insights.

Top 10 Job Titles:

Here we show the top 10 required job titles.

Why this Chart?

We use Bar charts which represent numerical values compared to each other. The length of the bar represents the value of each variable.

Analysis

We found out that there are 194 unique job titles, 214 companies, and 80 locations in the Middle East hiring for data jobs.

Conclusion

The most required title is “Data analyst” followed by “Data scientist” and “Data engineer”.

Top 10 Hiring Companies:

Here we show the Top 10 Hiring Companies.

Why this Chart?

We use Bar charts which represent numerical values compared to each other. The length of the bar represents the value of each variable.

Conclusion

We found out that the company that is hiring the majority of jobs is “Crossover” (27 jobs) followed by “Turing” (15 jobs), and “Recruit Reel” (13 jobs).

Top 10 Hiring Locations:

Here we show the Top 10 locations.

Why this Chart?

We use Bar charts which represent numerical values compared to each other. The length of the bar represents the value of each variable.

Conclusion

We found out that the majority of the job offers are in Cairo, Egypt (72 jobs), followed by Dubai, United Arab Emirates (29 jobs), and Riyadh, Saudi Arabia (28 jobs).

Seniority Level:

Here we show the seniority levels, which show you the most wanted seniority level, and the least wanted levels.

Why this Chart?

We use a pie chart which is a circular chart with triangular segments that shows data as a percentage of a whole.

Conclusion

We found out that the most wanted seniority level is the Mid-Senior level (144 jobs) followed by the Entry level (108 jobs). the least wanted levels are the “Internship” & “Executive” levels with only 3 jobs each.

Employment Type

Here we show the employment Type, that shows you the most required employment type.

Why this Chart?

We use a pie chart which is a circular chart with triangular segments that shows data as a percentage of a whole.

Conclusion

We found out that the most required employment type in the data market is “Full-time” with 192 jobs while the least required one is “Part-time“.

Top 10 Job Functions

Here we show the top 10 job functions that show you the most prevalent job function.

Why this Chart?

We use Bar charts which represent numerical values compared to each other. The length of the bar represents the value of each variable.

Conclusion

We found out that the most prevalent job function is “Information Technology” (84 jobs) followed by “Engineering & Information Technology” (68) and “Engineering” with 42 jobs.

Top 10 Hiring Industries

Here we show the top 10 Hiring Industries that show you the most common industry.

Why this Chart?

We use Bar charts which represent numerical values compared to each other. The length of the bar represents the value of each variable.

Conclusion

We found out that the most common industry is the Technology, Information, and Internet industry (51 jobs) followed by Software Development (30 jobs) and IT Services and IT Consulting (28 jobs).

Top 10 Required Skills

Here we show the top 10 Required Skills that show you the most required skill .

Why this Chart?

We use Bar charts which represent numerical values compared to each other. The length of the bar represents the value of each variable.

Analysis

After analyzing 350 job descriptions

Conclusion

We found out that Excel is the most required skill among all skills, followed by Deep Learning, SQL, Machine Learning, and Scala.

The least mentioned tools are:

Microsoft SQL Server

Jupyter

Theano

Nodejs

PrestoDB