How to become a Data Scientist

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.

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.

Data Scientist Skills and Requirements

To become a data scientist, candidates need a combination of technical and non-technical skills. Some of them include the following:

Basic Knowledge of Programming

Knowledge of programming languages is a must to become a data scientist. Students can start by learning programming languages like Python, C++ and C, SQL server and Java. While python is the most common language used for coding, knowledge of other languages will help extend their expertise domain.

These programming languages are essential as they help to restructure the data set to make it more understandable. Students can also opt to use R, a free software that helps them understand statistical computing. It supports most machine learning algorithms for advanced analytics and data management.

Know the Analytical Tools

A data scientist armed with the proper analytical tool could find the best patterns and trends in any data. The IT-enabled tools such as SAS, Spark, Pig, Hive, Hadoop, Talend, Scoop, Flume and others make it easier to analyse large amounts of data and find its patterns and trends. This makes the process of extracting data much more effortless. They can opt for various certificates and free courses offline and online to get the hang of any of these analytical tools.

Statistical Knowledge is a Must

Statistics forms a strong base for data scientists’ jobs. Learning probability and mathematical analysis is an essential part of data science. Statistics include collecting, analysing, interpreting, and presenting data in a form that makes sense to everyone.

The skill of probability ensures that the data scientist can see the big picture and ensure that none of the precious data goes to waste of none of the unrelated data is included in the final result.

While mathematical analysis deals with limits and theories related to data science, it also teaches the tools like integration, measure, differentiation, analytic functions and infinite series, all of which enable a data scientist to process a large amount of data in a structured manner.

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