Data Scientist Job Overview
Data Scientist requires real and practical knowledge of statistical data analysis methods
The Data Scientist is an analytical data expert who has the technical skills to tackle complex problems.
... as well as the curiosity that drives those challenges. They are part mathematicians, part computer scientists, and part trend spotters.
Data Scientist requires real and practical knowledge of statistical data analysis methods, skills in building mathematical models (from neural networks to clustering, from factorial to correlation analyzes), working with large data sets, and a unique ability to find patterns. But this is all lyrics. Let's get down to business now.
What you need to start
Knowledge of mathematical statistics, basic programming and data analysis skills are required to enter any field where a datascientist may be employed. The next steps will require deeper knowledge. The set of necessary skills and tools will largely depend on the tasks of a particular company.
“Basic knowledge of machine learning, mathematical apparatus and programming is enough to solve simple problems and get to the junior level. A specialist at the level of middle and senior is already required to be able to fine-tune the parameters that affect the overall quality of the result. The list of sections from higher mathematics and understanding of the mathematical formulation of each model at this level is an order of magnitude higher than for a junior "- Anna Chuvilina, author and manager of the Data Analyst program (see Jobs in Data Science for reference).
As a rule, in Data Science they use SQL, Python, for complex calculations - C / C ++. A good level of English will help you grow faster by reading professional literature and interacting with other industry professionals.
The developer's background is well suited for retraining into datascientists. Developers know programming languages, understand algorithms, and have an understanding of how IT tools work. In this case, the transition to a new specialty will take several months. Important competitive advantages available to professionals from other fields: better understanding of the subject area, strong communication skills.
Experience in working with real business projects for an employer is more important than an academic degree or specialized higher education. Diplomas from strong universities and thematic research papers are more valuable when choosing involved consultants for strategic projects. Just like they say in the article covering Data scientist jobs, based on practical experience, they choose a datascientist to solve the daily tasks of the company.
The dataset is not challenged to cover all areas of mathematical knowledge or master every software tool that can be used to analyze data and build a model. Large and complex projects are usually handled by teams of specialists. Here, the skills and knowledge of each complement the general toolbox. To get started in the profession, it is enough to love programming, mathematics and not be afraid of difficult problems.
As DJ Patil, former Chief Scientist for Science and Technology Policy for the United States, said, "A data scientist is a unique blend of skills who make amazing discoveries and bring fantasy stories to life — all through data."
What do Big Data specialists really do? They are constantly faced with constraints - technical, methodological and any other - and find ways for new solutions. They make discoveries by analyzing and predicting. Data Science also has a place for creativity: experts invent elegant solutions to complex problems, as well as high-quality visualization of information, make templates understandable and convincing - Data Engineer Jobs is a good example.
A Data Scientist example: “Jonatant Goldman, a physicist at Stanford, got a job at LinkedIn and started doing something that could not be measured in KPIs or looked at the end result: website, bug fix, feature implementation. While the development team was racking their brains over how to modernize the site and cope with the influx of visitors, Goldman built a predictive model that told the owner of the LinkedIn account which other users of the site might be familiar. By convincing the company's management to try his new model, Goldman brings millions of new views to the social network and significantly accelerates its growth. "
There is no definitive description of this profession - it all depends on the scope of the data skills. However, there are things that any Data Scientist does.
So, you already understood that Data Scientist is a person who can not only extract and analyze, but also process large amounts of data, performing truly magic with the help of many tools. If you want to really do Data Science, then prepare not just Excel, but also knowledge of Python, a textbook on calculus, and get ready to learn.