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 2023.

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 2023 - Data Sources

A massive amount of data is produced every day as a result of the growth in the number of mobile users, rising internet penetration rates, and the accessibility of different eCommerce apps. Data science is a discipline that is in charge of gathering, processing, modeling, and analyzing data in order to acquire a better understanding of the data. Businesses use data science to improve decision-making, boost revenues, and accomplish growth.

Here are some updated facts connected to Data Sources:

  • If we take into account all of the data that is currently available internationally, around 70% of it is user-generated, according to a DM News report.

All types of content, such as photos, videos, reels, text, and audio, are considered user-generated content. UGC refers to user-generated content that is published anywhere online or on social media, including blogs, forums, websites, and online reviews. These data science statistics let us get a good understanding of how much data is produced globally and how unprepared we are to process it.

  • According to one estimate, 1.145 trillion megabytes of data are produced daily.
  • Statista estimates that in the previous year (2021), there were around 79 Zettabytes of data/information created, consumed, collected, and duplicated globally.
  • According to forecasts made by CrowdFlower in its Data Scientist Report, text data makes up 91% of the data utilized in data science. According to the same survey, unstructured data consists of 33% images, 11% audio, 15% video, and 20% other types of data in addition to text.
  • The global datasphere has 90% replicated data and 10% unique data.
  • In the worldwide digital universe, between 80 and 90% of the data is unstructured, according to one of the articles published on CIO.
  • A user of the internet today would need 181 million years to download all the data from the internet.
  • In 2020, about two professionals joined LinkedIn per second.
  • The United States had 2670 data centers, making it the largest in the world in 2021.
  • In 2020, according to Domo, every person on earth generated almost 2.5 quintillion bytes of data each day.
  • According to the same report from DOMO, in 2020, each person generated around 1.7 MB of data each second.

Let us now look at some of the Benefits of Data Science in 2023.

Data Science Facts in 2023 - Data Science Benefits

There are several benefits of Data Science, and every major and minor company in the world relies on its data to run its business. Let us look at some quick facts to understand better:

  • The BCG-WEF project report details the findings that 72 percent of manufacturing organizations use advanced data analytics to increase productivity.
  • By 2025, the market for big data analytics in healthcare might be worth $67.82 billion.
  • About 68% of international travel brands made significant investments in business intelligence and predictive analytics capabilities in 2019, according to Statista Research Department.
  • By 2023, the big data analytics market is anticipated to grow to $103 billion.
  • Around 1400 colleges and universities worldwide use predictive analytics to improve low graduation rates, redefine the college experience, and guide students down a direct, data-driven road to graduation with fewer dead ends and erroneous turns.
  • 95% of companies say that managing unstructured data is a challenge for their industry.
  • The competition in their industry has changed as a result of data analytics, according to about 47% of McKinsey survey respondents, and data science has helped businesses gain a competitive advantage.
  • Daily message exchange on WhatsApp can reach 65 billion.
  • Netflix saves about $1 billion annually on user retention thanks to big data.

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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 2023: Data Science Job and Salary

Data science has been one of the top jobs in recent times with a good salary statistic. Let us look at some facts to analyze the current Data Science Job and Salary insights:

  • The average base salary for Data Scientists in the United States is $117,212 per year, according to Glassdoor. Given that the estimate was created from a sample of 18,000 incomes, there is a very high degree of confidence in it.
  • An annual upgrade increases a Data Scientist's compensation by USD 2,000–2,500 on average.
  • According to PayScale, the average compensation for prospective Data Scientists seeking their first position in the industry is $85,000. 
  • On the other hand, Data Scientists with 1-4 years of experience may anticipate a total pay of $96,000, while those with 5-9 years of experience can expect an average salary of roughly $110,000. Only with seniority does the pay increase.
  • 11.5 million jobs for Data Scientists will be generated by 2026.

Now, let us look at the Data Science Statistics Facts that give us an insight into the future.

Data Science Statistics For Future

To provide a clearer picture of what the future holds, consider some of the most significant data science statistics. Below are some astonishing Data Science Statistics for the future:

  • In 2023, 66% of people on the planet will be connected to the internet.
  • By the end of 2025, there will be more than 75 billion Internet of Things (IoT) connected devices in operation, according to data science statistics in one Statista. According to the projection, there will be almost three times as many IoT devices in 2020 as there were in 2019.
  • Data scientists are expected to earn between $65k and $153k per year, making it the next ideal occupation.
  • In 2023, there will be three times as many linked gadgets as there are people on the planet.
  • By 2023, there will be 1.6 networked mobile devices and connections per person.
  • By 2024, 149 zettabytes of data will have been copied, collected, and organized. Compared to the two zettabytes we produced in 2010, that is enormous.
  • In 2026, it is anticipated that the market for data science platforms will be worth 322.9 USD billion.

Let us now look at quick FAQs to clarify what we have learned till now.

FAQs

1. Does data science have a future?

The field of data science is still young and has a promising future that will persist for many years. The two main causes of the rising demand for data science are advancing technology and the production of enormous amounts of data. Data science will have a better and longer-lasting future as a result of anything from businesses' inability to handle enormous amounts of data to changes in data control rules to the astounding growth in data creation and handling.

2. Can I learn data science online, or do I need a degree from a university?

A university degree in data science is very acceptable, but you must never forget that time is of the essence. A strong, well-rounded university degree in data science could be an excellent option if you are considering your options for continuing your education after graduating from college. If you are changing careers, you probably won't want to continue your education for at least another two years before finding a job.

It is irrelevant in the world of work how long it takes you to learn data science or whether you have a top-notch certification. A business is most interested in hiring a tech-savvy individual with a set of demonstrated talents (supported by a portfolio of completed projects).

3. How do careers in data science work?

You may have realized by now that data science is a permanent force. It has aided organizations in developing past the limitations of data consolidation conventions. As a result, Data Scientists will be required until data science is established. To become a Data Scientist, you must possess extremely specialized knowledge and abilities. In the US alone, there is now a need for more than 150,000 Data Scientists. A global skills gap in data science also exists in Europe and Asia. Since 2011, 94% of workers with a degree in data science are now seasoned Data Scientists. As a result, if you decide to pursue a career in data science, you can feel very at ease and confident.

4. How good of a coder should a Data Scientist be?

Although knowing how to code is a need for every data science profession, prior programming experience is not necessary to get started in this field. It goes without saying that a person seeking employment in data science should be knowledgeable of specific programming languages and related technical tools, and employers of Data Scientists typically demand such skills. However, a Data Scientist's arsenal of coding tools is unquestionably less comprehensive than, say, that of a software engineer or computer scientist. Since there aren't many programming languages that can be used to solve data science problems, learning the fundamental data-related methods and techniques of just one of them can be a great place to start.

Data science is a broad area of study that requires a variety of skills and competencies in addition to coding, such as an analytical mentality, knowledge of statistics, probability, and linear algebra, effective storytelling, and business domain expertise.

5. What are the forecasts for the application of data science in 2023?

IoT technological innovation will be required, and it is anticipated that it will work in tandem with data science to realize predictable, repeatable, and measurable outcomes.

The use of AI-driven support will increase. It's likely that it will take the place of the present dashboards and do away with the "Swivel Chair" interface.

NLP, or natural language processing, is becoming more and more important as firms find innovative uses for artificially intelligent data science applications. NLP is anticipated to increase in stature, usage, use cases, and data science applications during the next few years.

Numerous similar forecasts point to the future expansion and development of data science platforms and related technologies.

6. What qualifications and skills do employers want in a Data Scientist?

The most fundamental technical abilities that employers typically want from a Data Scientist are as follows:

  • Debugging
  • A Decent command of R or Python (especially the popular data science modules of these languages)
  • Using storytelling with unstructured data
  • Knowledge of statistical principles, aptitude for wrangling, cleaning, analyzing, and visualizing data
  • Proficiency with SQL and command-line functionality
  • Using machine learning or deep learning techniques, predictive modeling, and model estimate
  • Web harvesting

This does not imply that any data science position would require you to have all of those abilities. You should study the job description for each organization in question and compile a list of the unique technical skills and equipment they demand in order to understand what they are looking for in a Data Scientist.

The most sought-after soft skills for a Data Scientist are those listed under:

  • The capacity to meet deadlines
  • Effective communication
  • Flexibility
  • Business subject expertise
  • Critical analysis
  • Creativity 
  • Multitasking
  • Teamwork
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. 

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