Rising growth in internet adoption and rapid technological advances in device connectivity are driving the flow of data at an exponential rate, prompting organizations to find different ways of transforming the data influx into business insights that facilitate more informed, smarter decisions.

Today, most people are familiar with how eBay, Amazon, YouTube, or Netflix augment the user experience by providing personalized recommendations on what to buy and what to watch. Performing such tasks would be impossible without gaining insights from data related to the search history of users. That's where data science comes in.

Data science, sometime in the year 2008, shot to prominence and has since then gathered upward momentum to become a dominant trend in the IT field. The popularity and acceptance of data science have soared over time because it enables businesses of all sizes to identify patterns in data, consequently helping to explore new markets, manage costs, increase operational efficiency, and build a competitive advantage.

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The following document presents data science facts and data science stats that every aspiring data scientist should know in 2021.

First, What Is Data Science? 

An interdisciplinary field related to big data and machine learning, data science leverages scientific processes, methods, and algorithms to extract insights and business intelligence from diverse unstructured and structured data.

The data science workflow involves a series of complex processes, including data acquisition, data warehousing, data cleansing, data processing, data staging, data clustering, data modeling, and insights summarizing.

Once the insights are obtained, data scientists perform exploratory work, regression, text mining, predictive analysis, and qualitative analysis. Finally, the insights are communicated via data visualization, which helps executives make intelligent business decisions.

Data Science Facts 2021 - Data Sources

Unstructured, semi-structured, and structured are the 3 major groups that data scientists categorize data into. Unstructured data is data that is not organized, semi-structured data is organized, but it is not standardized, while structured data refers to data that is well-organized. Irrespective of the data classification, data scientists need to ensure that computers understand the data that is being used. 

Here are some interesting data science stats about data sources.

  • Text data comprise 91 percent of the data used in data science
  • 41 percent of the data in the data science pipeline comes from public data
  • Internal systems generate nearly 78 percent of the input data utilized in data science
  • 33 percent of the data is images, 15 percent is video, and 11 percent is audio

Data Science Facts in 2021 - Data Science Benefits

Data science enables organizations to identify patterns and trends in data, facilitating better-informed business decisions that deliver superior outcomes while minimizing risks. Below are some impressive data science stats on the benefits of data science.

  • For a Fortune 1000 enterprise, only a 10 percent increase in the accessibility of data will lead to net additional revenue of 65 million dollars
  • 47 percent of organizations believe that data analytics has fundamentally or significantly transformed how their industries compete
  • Almost 20 percent of the total technology budget is being spent on data analytics by 73 percent of companies
  • Retail companies, nearly 62 percent, have gained a competitive advantage from data analytics
  • Effectively managing unstructured data to extract meaningful business insights is a top priority for 40 percent of businesses
  • Individuals create 70 percent of the online data, of which 80 percent is stored, managed, and analyzed by enterprises
  • The Open Graph API from Facebook aggregates 1 billion content pieces per day
  • Over 75 billion IoT (Internet of Things) connected devices will be in use by 2025, a 3x increase over IoT-enabled devices in 2019
  • 15 million dollars a year is the financial loss organizations suffer due to poor quality of data

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Data Science Facts: Popular Programming Languages for Data Science

Emerging technology domains, such as Artificial Intelligence, Machine Learning, and Data Science, require robust algorithms for running intelligent models. One needs to be proficient in programming languages to gain a deep understanding of how algorithms work. There are a variety of programming languages to perform data science tasks. The most popular programming languages for data science include:

According to a data science report published by software company Anaconda, 75 percent of data scientists say that they always or frequently use the open-source Python programming language for data science-related tasks. Python dominates the data science landscape, and the trend is expected to continue in 2021.

Listed below are the stats for other popular programming languages:

  • A worldwide survey carried out by Kaggle, a subsidiary of Google LLC, reveals that 36 percent of data scientists favor the R programming language for data science work
  • 15 percent of data scientists use Javascript, 10 percent rely on Java, 9 percent prefer C/C++, and 4 percent employ C#

Data Science Facts 2021: Data Science Job and Salary

Company review website Glassdoor has named "data scientist" as the #1 job in the United States for 4 consecutive years. 

The Bureau of Labor Statistics, on the other hand, predicts that there will be a 27.9% increase in job opportunities through 2026 because of the growing demand for skills linked to data science. 

Here are some fascinating data science stats on employment and salary.

  • According to a survey conducted by CrowdFlower, 50% of surveyed data scientists said that they are "thrilled" with their jobs, and 90% said they feel happy with what they do.
  • The CrowdFlower survey also reports that employers contact 30% of data scientists multiple times a week for new employment opportunities, 50% are contacted once a week, and 90% are approached by employers once per month
  • While 80% of data scientists spend their time finding, organizing, and cleansing data, only 20% perform data analysis, an IBM study states
  • The earnings of a data science engineer range from $65k/year to $153k/year
  • An Analytics Insight survey forecasts that data science will create 3,037,809 new openings by the end of 2021
  • Over 60% of companies believe that it is not easy to fill data science roles because of severe talent shortages

Major job boards, such as Dice.com, LinkedIn, and Glassdoor, have published several reports on the growth prospects of a career in data science, and the strong demand for qualified data scientists in recent years. There is, however, a massive skill gap of 58 percent at a global level in this emerging domain. This shortage of talent presents a fantastic opportunity for not just practicing IT professionals but also for individuals from non-IT backgrounds. 

Looking forward to becoming a Data Scientist? Check out the Data Scientist Course and get certified today.

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Many fresh graduates believe that they cannot pursue a career in data science because their university course did not cover essential skills related to big data analytics. Likewise, experienced professionals think they lack confidence because they never had the chance to upskill, which would have given them hands-on experience that most employers demand today. If you are a data science aspirant and feel the same way, Simplilearn can help. 

As the world's number one online bootcamp and certification course provider, Simplilearn has launched a SkillUp program, which incorporates free resources that attendees can access from anywhere, anytime. Employees from multinational organizations, including Bosch, PepsiCo, Microsoft, Amazon, Citibank, Dell, and VMware have already enrolled with Simplilearn's SkillUp program for skill-based learning. You can join their ranks today. Click here to learn more about Simplilearn's industry-recognized SkillUp program. 

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Simplilearn is one of the world’s leading providers of online training for Digital Marketing, Cloud Computing, Project Management, Data Science, IT, Software Development, and many other emerging technologies.

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