Overview of the profession Data Scientist
Data Scientist is an analytic data expert who has the technical skills to solve complex problems, as well as curiosity, which helps to set these tasks. They are partly mathematicians, partly computer scientists and partly trend spotters.
Data Scientist requires real and practical knowledge of statistical data analysis methods, skills for constructing mathematical models (from neural networks to clustering, from factor to correlation analyzes), working with large data arrays and the unique ability to find patterns. But this is all the lyrics. Let's get down to business now.
The average salary in the USA Data Scientist is $ 91,000 per year. But the graph of earnings from work experience.
PayScale data
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In Russia, the figure ranges from 60-70 thousand rubles a month for very "green" newcomers and reaches up to 220 for experienced specialists.
As DJ Patil, the former chief scientist of the United States science and technology policy department, said, “A data scientist is a specialist with a unique fusion of skills who makes amazing discoveries and embodies fantastic stories — all thanks to this.”
What do Big Data Specialists actually do? They constantly face constraints - technical, methodological, and any other - and find ways for new solutions. Make discoveries, analyzing and predicting. There is a place for creativity in Data Science: experts invent elegant solutions to complex problems, as well as qualitatively visualize information, make templates understandable and convincing.
Data Scientist life example: “Jonathant Goldman, a physicist from Stanford, got a job on the social network LinkedIn, and started doing something that could not be measured in KPI or look at the final result: the site, fix the bug, introduce features. While the development team puzzled over how to modernize the site and cope with the influx of visitors, Goldman built a predictive model that prompted the LinkedIn account owner who other site users might find him familiar with. Convincing the company's management to try out its new model, Goldman brings millions of new views to the social networks and significantly accelerates its growth. ”
There is no definite description of this profession - it all depends on the scope of application of data skills. However, there are things that any Data Scientist does:
But the terms and technologies that the future Data Scientist needs to know:
Among other things, you need to know and understand:
Here you can read how Beeline interviews Data Scientist in his company: “The process begins with a telephone interview with questions on some sections of mathematics. After the candidate is waiting for the test task - a specific machine learning task, similar to the tasks on kaggle.com. Having built a good algorithm and obtained a high value of the quality metric on the test sample, the candidate is allowed until the next stage - an immediate interview, which tests knowledge of machine learning and data analysis methods, and also sets non-trivial questions from practice and logic tasks. ”
And yes, you can get into Data Scientist not from scratch, but with a good base. Here is what a physicist who graduated from a university and exchanged science for Big Data writes : “The office, called Bidgely, offered me a Data Scientist position with a salary of $ 130k a year dirty (about $ 7400 a month clean): to work in an office located in the town Sunnyvale, in Silicon Valley, is a few kilometers from Google, Linkedin, Apple headquarters. ” In January, he thought that he should go to Data Science, and in October he worked in the USA, graduating from the university in June.
So, you already understand that Data Scientist is a person who can not only extract and analyze, but also process large data arrays, performing truly magic with the help of a variety of tools. If you really want to do Data Science, then prepare not just Excel , but also knowledge of Python, a textbook on mathematical analysis, and get ready to learn.
Well, in the end, we just wanted to make you happy. Here are some useful links. The first is with 51 free books related to Data Science. And here is the largest Data Science community . Still there is Peter Flach's excellent textbook Machine Learning. Science and art of constructing algorithms that extract knowledge from data ”, translated into Russian.
If you want to become a Data Scientist, then we recommend to sign up for our full-time course , which lasts 5 months. After training, you will receive a diploma of vocational retraining in the specialty "Data Analyst / Machine Learning Specialist". The teachers are real experts from Yandex Data Factory, OWOX, Rambler, Sberbank-Technologies, Microsoft, MTS and others. All training is based not only on theory, but also on the mandatory practical development. Therefore, after the full-time course, you will emerge as a trained specialist who can go to any area of interest to him: retail, banks, startups, IT, and telecom. All the details here .
Data Scientist requires real and practical knowledge of statistical data analysis methods, skills for constructing mathematical models (from neural networks to clustering, from factor to correlation analyzes), working with large data arrays and the unique ability to find patterns. But this is all the lyrics. Let's get down to business now.
The average salary in the USA Data Scientist is $ 91,000 per year. But the graph of earnings from work experience.
PayScale data
')
In Russia, the figure ranges from 60-70 thousand rubles a month for very "green" newcomers and reaches up to 220 for experienced specialists.
As DJ Patil, the former chief scientist of the United States science and technology policy department, said, “A data scientist is a specialist with a unique fusion of skills who makes amazing discoveries and embodies fantastic stories — all thanks to this.”
What do Big Data Specialists actually do? They constantly face constraints - technical, methodological, and any other - and find ways for new solutions. Make discoveries, analyzing and predicting. There is a place for creativity in Data Science: experts invent elegant solutions to complex problems, as well as qualitatively visualize information, make templates understandable and convincing.
Data Scientist life example: “Jonathant Goldman, a physicist from Stanford, got a job on the social network LinkedIn, and started doing something that could not be measured in KPI or look at the final result: the site, fix the bug, introduce features. While the development team puzzled over how to modernize the site and cope with the influx of visitors, Goldman built a predictive model that prompted the LinkedIn account owner who other site users might find him familiar with. Convincing the company's management to try out its new model, Goldman brings millions of new views to the social networks and significantly accelerates its growth. ”
There is no definite description of this profession - it all depends on the scope of application of data skills. However, there are things that any Data Scientist does:
- Collect a large amount of unmanaged data and convert them to a more convenient format.
- Solving business problems using data.
- Work with various programming languages, including SAS, R and Python .
- Work with statistics, including statistical tests and distributions.
- The use of analytical methods such as machine learning, deep learning and text analytics.
- Collaboration with IT and business in equal measure.
- Finding order and data patterns, as well as identifying trends that can help achieve the final business result.
But the terms and technologies that the future Data Scientist needs to know:
- Data Visualization : Presentation of data in graphical format so that they can be easily analyzed.
- Machine learning: a field of artificial intelligence based on mathematical algorithms and automation.
- Deep Learning: An area of learning machine learning that uses data to model complex abstractions.
- Pattern Recognition: A technology that recognizes patterns in data (often used interchangeably with machine learning).
- Data preparation: the process of converting raw data to another format to make it easier to consume.
- Text analytics: the process of analyzing unstructured data to generate key business ideas.
Among other things, you need to know and understand:
- Statistics and machine learning.
- Programming languages SAS, R or Python .
- MySQL and Postgres databases.
- Data visualization and reporting technologies.
- Hadoop and MapReduce.
Here you can read how Beeline interviews Data Scientist in his company: “The process begins with a telephone interview with questions on some sections of mathematics. After the candidate is waiting for the test task - a specific machine learning task, similar to the tasks on kaggle.com. Having built a good algorithm and obtained a high value of the quality metric on the test sample, the candidate is allowed until the next stage - an immediate interview, which tests knowledge of machine learning and data analysis methods, and also sets non-trivial questions from practice and logic tasks. ”
And yes, you can get into Data Scientist not from scratch, but with a good base. Here is what a physicist who graduated from a university and exchanged science for Big Data writes : “The office, called Bidgely, offered me a Data Scientist position with a salary of $ 130k a year dirty (about $ 7400 a month clean): to work in an office located in the town Sunnyvale, in Silicon Valley, is a few kilometers from Google, Linkedin, Apple headquarters. ” In January, he thought that he should go to Data Science, and in October he worked in the USA, graduating from the university in June.
So, you already understand that Data Scientist is a person who can not only extract and analyze, but also process large data arrays, performing truly magic with the help of a variety of tools. If you really want to do Data Science, then prepare not just Excel , but also knowledge of Python, a textbook on mathematical analysis, and get ready to learn.
Well, in the end, we just wanted to make you happy. Here are some useful links. The first is with 51 free books related to Data Science. And here is the largest Data Science community . Still there is Peter Flach's excellent textbook Machine Learning. Science and art of constructing algorithms that extract knowledge from data ”, translated into Russian.
From the Editor
If you want to become a Data Scientist, then we recommend to sign up for our full-time course , which lasts 5 months. After training, you will receive a diploma of vocational retraining in the specialty "Data Analyst / Machine Learning Specialist". The teachers are real experts from Yandex Data Factory, OWOX, Rambler, Sberbank-Technologies, Microsoft, MTS and others. All training is based not only on theory, but also on the mandatory practical development. Therefore, after the full-time course, you will emerge as a trained specialist who can go to any area of interest to him: retail, banks, startups, IT, and telecom. All the details here .
Source: https://habr.com/ru/post/329068/
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