There is a new career field that has been hyped up because of its relevance and this field is Data Science. New-age technology aims to make everything accessible with just one tap.
From analytics to algorithms and statistics to documentation, Data Science as a career option is quickly growing. Here are ten astonishing facts about Data Science that will leave you stunned. We also provide you with a lot more information about what else this bizarre yet mathematically intriguing field offers.
Data Science Has A Web Of Departments
According to the survey The State of Science 2020: Moving from hype toward maturity conducted by Anaconda, there is a big increase of almost 60% Data Science related jobs in R&D centers, IT companies, multinationals, digital marketing companies, and businesses.
While the future focuses on digital and IT companies, many young people can look to Data Science as a viable career option. As per the survey, 22% of all services provided in the research and development departments are done by Data Scientists.
In the current market, 21% of the Data Scientists are found in Human Resource (HR) and marketing departments, and IT companies hire 15% of the Data Science graduates. 28% of Data Science professionals work in the Data Science Center of Excellence (DSCoE) in different organizations.
Data Scientists Love Their Jobs And Salaries
Believe it or not, according to the CrowdFlower survey, 50% of the Data Scientists are happy with their jobs, and 90% are satisfied with what they are doing. Out of these, 64% of Data Scientists believe that they are doing the best job in the world.
One of the prime reasons they are so happy at their work is because there is no Big Data to be analyzed. Most Data Scientists have to organize, find, and cleanse data, whereas big tasks like data analysis are only performed once in a while. For all the work that they put in, the average earnings of Data Science Engineers range from $65,000 – $153,000 yearly.
Apart from it, many employers contact Data Scientists and engineers multiple times for short projects, for which they are paid separately. An Analytics Insight survey has predicted that Data Science alone as an employment generating field will generate 3,037,809 new job openings by the end of 2021.
Data Science Stats On Data Sources That Will Leave You Biting Your Nails
There are three major groups in which the data has been categorized by Data Scientists – Structured, Semi-structured, and Unstructured. Structured data is well organized and standardized, while semi-structured data is organized but incompletely or not standardized. At the same time, unstructured data is not at all organized.
As per the 2017 Data Scientist Report by CrowdFlower, the text data comprises 91% of the data utilized in Data Science.
While in the unorganized data that has been collected, 33% of it are images and pictures, 11% comprises audio, and 15% is video. Internal systems generate approximately 78% of input data that has been used in Data Science and is considered important.
Popular Languages For Data Science
Data Science as a domain requires robust algorithms to run intelligent models, research various topics and analyze heavy data. This proficiency comes with multiple languages that can perform Data Science tasks. The most common Data Science languages are JavaScript, Java, C/C++, and C#.
According to the science report represented by Anaconda, the most frequently and widely accepted programming language that Data Scientists use is Python, which counts for nearly 75% of the population of Data Scientists and engineers.
A subsidiary of Google LLC, Kaggle conducted a research report in the year 2018 on a global level that revealed that 36% of data engineers and scientists prefer R language, 15% use JavaScript, 10% depend on Java, 9% are into C/C++, and 4% use only C#.
You Don’t Need To Have A Ph.D. Or Be Tech-Savvy To Be A Data Scientist
The hype about Data Science often misleads the aspirants in terms of qualifications and skills they need to have to become a Data Scientist. A person with average intelligence can learn Data Science and pursue it as a career option.
But before stepping into the world of Data Science, a person needs to upskill specific fields like statistical modeling, predictive modeling, machine learning and have basic information about programming languages.
Apart from it, their knowledge in algorithms and analytics must be profound because these are the major areas that Data Science focuses on. To do so, you can enroll yourself in various internships and online seminars that teach you about Data Science.
Data Science Does Not Just Produces Data Scientists But Also Offers Other Work Opportunities
This is an exciting fact because most people are still unaware of what they can become after pursuing Data Science as their career. Other than Data Scientists, Data Science offers a lot of job opportunities in this domain.
The first career option is Data Engineers, who are currently in high demand. They are responsible for looking at and managing the overall data infrastructure. To become a Data Engineer, the key skills required are a strong grip in programming languages like Python and knowing how to use database tools like NoSQL and even big data tools like Hadoop.
One can also be captivated by Data Analysts, as their main job is to find the answers by working through data available with appropriate tools. Their key skills are programming, visualization, stats, mathematics, and data analysis.
Data Science Is Way Beyond Excel Sheets
Many people live in their own bubble of thoughts and beliefs that the life of a Data Scientist is restricted to Excel Sheets only. This is anything but true. Data Science is a very extensive field that has a focused work target and intended outcomes for the same.
There are many techniques, tools, languages, and software that make Data Science what it is. SQL, query, predictive analysis, machine learning, statistical analysis all form part of these tools and techniques.
Indeed, there was a time when Excel Sheets had a rock-solid role in Data Science, but the present-day tools like Python and R language don’t allow Excel Sheets to stand a chance. Today, Excel is just used for calculations and formulas.
Data Science In Real Projects And Online Data Science Competition Are Worlds Apart
Being successful in Online Data Science Competitions will surely boost your confidence, but it’s not actually what real-life Data Science project scenarios are. There is a huge gap in virtual and on-field practice, but online platforms help you learn Data Science at a beginner’s level.
Data Science Competitions have a limited number of datasets; most of the time, a warning is given whenever an error is made, the coding has to be done just once, there is no need to deploy your model, and there is no authentication or security.
Whereas real-life Data Science projects have unlimited datasets, there is no warning before a mistake is about to happen (in that case, you will have to rework and correct it); the code has to be rewritten every 5-15 mins. Apart from it, your model needs to be deployed, and authentication and the security of data are of utmost importance.
Data Science Is For Small Businesses And Not Restricted To Big Companies Only
It’s a myth that Data Science is meant for big companies, not for small businesses and companies. There are generalized rumors about Data Science that believe it to be made up of machines, heavy tools, and the size of working resources.
Rather it is a collection of big data, records, analysis, programming, presentation, and basic requirements that any company, small or big, includes in its work-frame. Data Scientists or people in the business of Data Science are smart enough to use these resources to add value to the organization.
And when it comes to infrastructure, only a computer device is needed to achieve the targeted results with an active internet connection and some small tools to make the work happen. These tools are readily available online and can be downloaded as and when required to get the ball rolling.
Data Is Not Always Clean
Data Scientists face a challenging time categorizing and managing data as it is not always clean and sometimes riddled with many problems. With an eagle’s eye, a Data Scientist has to examine the data and eliminate all the incomplete, duplicate, irrelevant, inaccurate, incorrect, or misspelled.
And a collection of data that is “dirty” is itself a big problem. But the major concern is joining these multiple databases, which are a mix and match of useful and not so useful information into a single entity.
Data Scientists cleans data through re-formatting, screening, and organizing. There are tools and techniques that can help data scientists to clean the data.
The Bottom Line
Gone are those days when all the data was collected and stored in print because Data Science is in practice today. Hence, the above-mentioned facts about Data Science will help you better understand Data Science as a career option.