Data Science and Why Data Science Jobs Are High on Demand

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Data Science is the study of data in which we can process data to extract pieces of information to use for data science consulting for business companies. The field of data science is now a massive market that has a fair share of advantages and limitations. Though data science has limitations it is still leading in the market. Data science consulting, Artificial Intelligence consulting, consulting big data is now used as a package in business companies. Business companies’ models generate an enormous amount of data that must be processed and analyzed for further use to grow their businesses in numbers. Machine learning consulting companies are on-demand for giving services of AI consulting, consulting big data, and other consultancy services based on data science.


Data Science is greatly in demand nowadays. The fastest-growing job is a data scientist job. According to Linkedin, there is a prediction that by 2026 there will be 11.5 million jobs in this sector. There are a few people who are capable of those skills that are required for a data science job. These make data science jobs less saturated than any other IT sector jobs. For this reason, data scientists got the highest payment for their hard work. According to Glassdoor, an average of $116,000 per year is the earnings of an individual data scientist. Data Scientists are also needed for AI consulting


The dominant skill sets for data scientists are Statistical machine learning optimization, Programming CS fundamentals, business and Domain knowledge, Visualization of the shelf toolboxes, Big data cloud computing, and Communication storytelling. Among all software engineers, a data scientist is a better statistician compared to others. More likely a data science job is the sexiest job of the 21st century. So that, for data science consulting, data scientists are needed for machine learning consulting companies.


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Because of that data can be used for many different purposes so that data scientists are high on demand. They know everything about managing data, interpret data, and know-how to protect data. It is predicted that data science skills will drive a 27.9% rise in employment in the field by 2026. In the United States, data scientists, data developers, and data engineers reach nearly 700,000 jobs. A minimum salary of a data engineer is approximately $80,000 each year. Here we can see the demand for data scientists.


Let’s see the main reasons that make data science job demanding -

  • Nowadays every company is facing troubles in organizing data on a large scale.
  • Many companies have a shortage of skilled resources.
  • The salary for this job is high.
  • There is an X factor for data scientists because they are almost irreplaceable.
  • More skillful personnel can make companies more productive by analyzing and organizing data.
  • Anyone from any background with good skills in data science can join as a data scientist.
  • Almost every organization needs a data scientist to appoint to manage, analyze, and organize a large amount of data.
  • There are many roles classified from data science as Data Scientist, Data Architect, BI engineer, Business analyst, Data Analyst, Database Administrator, and etc. are highly required for the IT market.

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    Every year almost 14% of jobs in data science are increasing. Which is a huge number of jobs. Solving business problems and processing enormous data for companies is not very easy to work at all. Only skilled persons are hired as Data Analysts or as Data Scientists. Data science consulting, Artificial Intelligent consulting, consulting Big Data are basic services served by an individual data scientist or a machine learning consulting company do this job for organizations. This can grow the organization and can save money, time, and many more valuable things. Data Engineering can add more value to a company by increasing the annual revenue of the company. So, if you want to be a fast grower just add a data analyst to your organization or give this responsibility to a machine learning consulting company.

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    Advantages of Machine Learning in Financial Forecasting

    Using AI to robotize the monetary estimating measure presents a few remarkable advantages for senior money heads and their groups. The key advantages are summed up beneath.

    1. Capacity to Produce More Accurate Forecasts, Faster

    As referenced before, AI empowered estimating can free monetary gauging of the concentrated work of gathering and accommodating information. The apparatuses can be designed to gather and accommodate extremely enormous informational indexes in a mechanized style. In addition, AI apparatuses can assist with deciding business drivers and enormously diminish estimate blunder. AI calculations are intended to gain from the information after some time and foresee which drivers have the best effect on monetary execution. After some time, the model turns out to be more precise and produces figures all the more rapidly.

    2. Hedge between best and worst case scenario

    With accounting page driven gauging measures, there are cutoff points to the number of information sources and how much information can be processed and burned-through inside anticipating models. AI devices can significantly upgrade the volume and sorts of information that can be utilized on the grounds that the apparatuses can hold more information and process it quicker than people. Through this way we can have the better chance to hedge between the best and worst case scenario.

    3. Empowering Value-Adding Activities

    Customary estimating measures ordinarily expect examiners to invest the vast majority of their energy accommodating and gathering information as opposed to chipping away at esteem added investigation and collaborating with the business. Utilizing an AI answer for production at any rate a standard estimate can help investigators move away from these commonplace assignments and spotlight on understanding operational drivers, key business occasions, and microeconomic and macroeconomic elements that may affect the business, carrying those experiences into the determining cycle. Utilizing AI can eventually help monetary investigators accomplice all the more intimately with the business and backing dynamic.

    The Emergence of AI in Accounting and Finance

    There are sure businesses that are generally helpless to the effects of AI. The most referred to report from NPR predicts that:

    • Bookkeepers have a 97.6 percent possibility of seeing their positions computerized.
    • Accountants and examiners have a 93.5 percent possibility of seeing their positions mechanized.
    • Financial examiners have a 23.3 percent possibility of seeing their positions mechanized.

    To summarize, occupations that are comprised of repeatable, precise errands have higher danger of being mechanized than those that require judgment, examination, and relationship building abilities. Thus, in the event that your present place of employment regularly expects you to show those attributes, at that point you need not concern. For Finance and Accounting, AI and robotization are viewed as suitable answers for successfully managing consistency and danger challenges across different areas. To stay serious, organizations are moving from work exchange and seaward unrest to mechanization insurgency. Man-made intelligence speaks to an occasion to decrease the weight on money experts, especially around conventional monetary exercises, for example, exchange handling, review and consistency. These exercises in their present structure keep money from being more essential colleagues.

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