In order to solve problems or make a point, data scientists acquire and analyse vast amounts of data. They gather enormous collections of subject-specific structured or unstructured data and utilise a variety of methods from computer science, mathematics, and statistics to interpret the data in order to produce more understandable outcomes.The need for data scientists has increased significantly over the past decade or so as big data technologies have advanced. The position of a data scientist is now very in-demand and well-paid due to the dramatic rise in unprocessed data across all industries. It has developed increasingly as an intellectual function that transforms data into profit or effective policy, rather than just being an IT byproduct.
Data scientists are analytical experts who gather and analyse large amounts of data to solve problems or prove a point. They collect large sets of structured or unstructured data based on the subject and use various computer science, math and statistics tools to interpret data for more comprehensible results.
As big data technologies have been rising for a decade or two, the demand for data scientists has grown exponentially. Given the drastic increase in unprocessed data in all the domains of the world, the job of a data scientist is highly sought-after and well-paid these days. It is not just a by-product of IT but has evolved more as an academic role that turns data into profit or efficient policies.
To become a data scientist, a person must have strong analytical skills and knowledge of math, statistics and programming languages. If the data in daily life makes sense to someone, they might have what it takes to become a data scientist.
Typically, it takes a strong interest in math and computer science aided by a curiosity about finding patterns among numbers. An expert data scientist knows where to find the data can be collected from as they are not limited to conventional sources of information.
It could come from emails, social media posts etc. They must have good data intuition to understand and find valuable information in any of these sources. The insights are not always easily found in the given data set while working on the projects.
Data scientists must be knowledgeable enough to understand which data to use and which needs to be discarded. This will make them more efficient. They can attend training sessions and boot camps to acquire these data-driven skills.
They have to be data-driven and have strong data visualisation to become an expert in the job, as, without these skills, they will not be able to find the patterns in the data.
Some experience in working with large amounts of data is a plus when it comes to becoming a data scientist. Along with this, if they can get some experience in machine learning and statistical modelling, it could prove beneficial.
A data scientist must also be a good communicator, as the ideas and patterns found from the complex data need to be disseminated to the organisation’s other stakeholders. Strong communication skills will enable them to make the data analysis and predictions interesting and easy to understand for others.
Data scientist vs. Data Analyst
The difference between data science and data analytics is the scope of the field. Data science is a collective terminology for a group of domains used to understand large amounts of data. Data analytics has a more extensive process and is more focused than data science. It is related to understanding the valuable insights that can be applied based on an existing problem.
Another significant difference between Data Science and Data Analytics is exploration. Data science does not deal with specific queries. Instead, it focuses on an extensive unstructured data set to find insights. Data analysis focuses on one problem at a time with the existing data. Data science produces more comprehensive insights concentrating on questions, while data analytics searches for answers to data-related questions.
Significantly, data science is more about specific questions than finding specific answers. It is concentrated on searching for potential trends depending on the existing data and realising better ways to analyse and model data.
The two fields are highly interconnected. Data science lays the foundation and creates primary observations, future trends and patterns, and potential insights that could prove crucial.
The information derived from the large data set is helpful for some fields, like improving machine learning, forming models for the data, and enhancing AI algorithms, as it can improve how information is understood. On the other hand, data analytics works on the more practical side of the data set.
Link: https://www.cheggindia.com/career-guidance/how-to-become-a-data-scientist/
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