Data science is moving in with big data. It’s a lot of unorganized information — for example, meteorological data for a while, query statistics in research laboratories, sports events, microbiological databases, and much more. The keywords here are “massive size” and “unstructured.” To do this, they apply analytical statistics and computer training courses.
The expert who does this is called a data scientist. It provides great information for predictions. What kind of predictions – what needs to be done depends on the problem. The result of the data scientist’s work is a bad image. To illustrate, this is a software algorithm that determines the best answer to a puzzle.
Is Data Science the Same as Business Intelligence?
No, they are not the same. The main difference is in the end. The data scientist needs connections and designs in the result sets to create a text that predicts the result – you can say that the data scientist is running for the future. It uses software algorithms and analytical statistics also describe the problem at the starting point as a technical one.
The business analyst focuses on the company’s business performance, not on the technical, software side of the problem. It works with statistics and can, for example, evaluate how successful an advertising campaign has been in sales over the past month. All this information can be used to improve the performance of the company. If there is a lot of information and some kind of forecast is needed, then a business analyst can involve data scientists to solve the technical side of the problem.
Information can be applied to a product in a variety of ways. If you keep track of all your purchases, repairs, product breaks, sellers, expenses, vacation arrangements, sales, and energy consumption, you have the opportunity to add:
Speaking of data science in manufacturing, you can decide whether it is more cost-effective to repair the defect, including repairs, reconstruction or replacement, or whether it is reasonable to make further repairs.
If there are many bolts, pumps, valves and other simple mechanical devices, you can compare the failure rates from many manufacturers; Considering the loss of work due to the service provider, consider the best value of the product and make the best purchase decisions.
You can look at both your energy costs and the needs of your electrical systems to make an informed decision:
- Production hours;
- Even when there is a strong desire to use human capital on supplier payments, even consider repairs.
You can test your product’s needs in a number of dimensions, seasons, monthly deadlines, and political and industrial climates to match supply. In the near future, you can calculate the time of purchase of raw materials, taking into account market fluctuations to buy cheaper or better quality items at the same price.
Depending on the impact of the Information Science Agency on production, management costs, and training, you can estimate the cost of hiring high-paid, trained line staff, as opposed to hiring new and trained.
This is really just the tip of the iceberg, and many of the methods used to analyze large-scale cleanliness are not so difficult, but they can significantly increase the bottom line.