Let’s say we have a task: to draw up a work schedule for call center operators, which works 24 hours a day.
It is clear that the number of incoming calls at night is several times less than during the day, so fewer employees need to be replaced. However, this is not the only factor that needs to be taken into account. Hereabouts are the most reliable data organizations to run on in 2021:
- Jp morgan chase;
What does data science mean?
We took into account the patient’s age and gender, as well as previous diagnoses, prescribed procedures and history of visits. In addition to these ai development company statistics, we also needed a list of episodes that occur for each individual client, indicating the time, type of service, type of treatment, etc.
What do Data scientists do?
Training should be based on the tasks assigned to the specialist. At the same time, tasks may differ depending on the field of activity of the company. Here are some examples:
- detection of anomalies – for example, non-standard actions with a bank card, fraud;
- analysis and forecasting – performance indicators, quality of advertising campaigns;
- scoring and grading systems – processing large amounts for making decisions, for example, on granting a loan;
- basic interaction with the client – automatic replies in chats, voice assistants, sorting letters into folders.
But for any of the above tasks, you always need to follow approximately the same steps:
- collection – search for sources and methods of obtaining information, as well as the collection process itself.
- Checking – validation, removal of anomalies.
- Analysis – the study, making assumptions, conclusions.
- Visualization – bringing DataScience UA into a human-readable form (graphs and diagrams).
The result is making decisions based on the analyzed data, for example, about changing the marketing strategy or increasing the budget for any of the company’s activities.
What do you need to know?
Despite the fact that you need to know quite a lot, there are now a huge number of online courses and books that will help you get the skills you need much faster.
- Statistics, mathematics, linear algebra
You will need to study a fundamental course in probability theory, calculus, linear algebra, and mathematical statistics. Mathematical knowledge is important in order to be able to analyze the results of applying data processing algorithms.
To master it from scratch, the first step is to learn three main areas of machine learning:
- Supervised Learning
Allows you to predict the result using pre-marked data. If you need to predict several values (for example, distinguish photographs of cars from airplanes and trains), then this is a classification problem, if one (say, assume the price of an apartment depending on its characteristics) is a regression problem.
- Unsupervised learning
Here, the input data is not marked up, that is, neither the result nor the method of data processing is known in advance. An example is a search for anomalies – unusual credit card transactions, erroneous sensor readings, and the like.