Data Science

Data science is an interdisciplinary field that provides the methodology to gain insights from structured and unstructured data (including but not limited to Big Data) using approaches ranging from statistical analysis to machine learning.

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Here are some examples of data used to get meaningful information:

  • Information about equipment operation
  • Weather data
  • Research results
  • Search engine queries for a specific period
  • Sports competitions results
  • Financial data.

The main methods of processing this information are mathematical statistics and machine learning. After all, it 's necessary not only to structure the data, but also to identify patterns in accordance with the customer's task.


IT specialists who deal with all of this are called data scientists . Their task is to build a predictive model based on the analysis of processed information. The content of forecasts depends on the tasks set by the customer.

 

Scope 

The average user encounters predictive models created by a data scientist when getting the results of the search query from the search engine.

In addition to search algorithms, the results of a data analysis specialist can include the algorithms for:

  • Chatbots
  • Lists of recommended video or audio
  • Lists of possible friends in the social network
  • Facial recognition software
  • Application scoring
  • Insurance event forecasting
  • Software for building genetic maps.


This list of a data scientist ’s work results is not exhaustive.

 


Job responsibilities

Some stages of work of a specialist in this field may differ depending on the specifics of the company , but the main responsibilities are:

  • Defining the task set by the customer
  • Evaluating the ability to solve the assigned task using machine learning
  • Preparing data for analysis
  • Searching for data evaluation criteria
  • Programming and training the developed forecast model
  • Evaluating the feasibility of the created model
  • Implementing the model in a given area of activity (production cycle or product)
  • Modifying the model as necessary during operation, taking into account current requirements.

 

Required knowledge 

Since the main material of a data scientist activity is data arrays, and the main task is to analyze data and develop a predictive model, one of the basic skills is mathematical knowledge (for example, understanding of differentials, derivatives, and matrix determinants) and experience with large amounts of information.

 

The necessary skills and knowledge include:

  • Mathematical analysis
  • Fundamentals of statistics
  • Mathematical statistics
  • Python and R programming languages
  • Understanding of machine learning and experience with its algorithms 
  • English.

 

A data scientist should also be able to create visual representations of the results of their work.



FAQ

We have prepared answers to the most common questions about data scientists . If you need more information, just contact us.

1. How do I find a data science specialist?

When searching for a data science specialist on your own, it's easy to make a mistake. The job market is dominated by beginners who are acquiring professional skills. Specialists of this level can be entrusted with the work of a data scientist assistant . Experienced specialists are unlikely to be actively searching for a job , so it’s reasonable to turn to a professional recruiting agency if you need an expert.
In addition to basic analytical skills and experience in building predictive models, it is preferable that a Data Scientist understands the specific field or business domain in which he or she will work. For example, it would be quite difficult for a specialist who processes weather data to switch to predicting technological equipment failures.
If you can understand the basics of Further Mathematics and Mathematical Statistics, you will only need to take a course in programming and machine learning methods. It’s important to have not only knowledge but also an understanding of how to apply it in practice.
The basic component of both professions is data processing. But the cardinal difference is that a Data Scientist develops an event prediction algorithm in addition to data analysis. We have dedicated a separate page on our website to Business Analysts.