Data Science vs Data Analytics

A data scientist is someone who can predict the future based on past patterns whereas a data analyst is someone who merely curates meaningful insights from data. To know more about their differences read the blog.

Posted by Akshay Nair P R on 3rd May 2018

What is Data?

Data can be termed as information that has been transferred into a form that is efficient for movement and processing.

We if talk about the current scenario in computers data is the information that has been converted to binary digit form.

The importance of data can be understood just be the following quote from Daniel Keys Morgan

 “You can have data without information, but you cannot have information without data.”

Data is, in fact, increasing so fast that according to IBM 2.5 billion gigabytes of data was generated every day in 2012.

And, in an article according to Forbes that Data is growing faster than ever and by the year 2020 about 1.7 megabytes of new information will be created for every human being every second.


Data Science

Data Science is the mixture of records, arithmetic, programming, problem-fixing, shooting information in creative methods, the ability to look at matters differently, and the activity of cleansing, getting ready and aligning the information.

It is like dealing with structured an unstructured data.

Data Science can be best explained by the Venn diagram created by Hugh Conway in 2010, it consists of three circles; maths and statistics, subject expertise and hacking skills.

Source:- drewconway.com


Data Analytics

Data Analytics the science of examining information with the aim of drawing conclusions about that data.

Data Analytics involves applying associate algorithmic or mechanical method to derive insights. for instance, running through a variety of information sets to seem for meaningful correlations between each other.

It is utilized in a variety of industries to permit the organizations and corporations to create higher selections, in addition, to verify and contradict existing theories or models.

The focus of data Analytics lies in reasoning, that is the method of deriving conclusions that are exclusively based on what the research worker already is aware of.

Thus anyone interested in this career should have a number of skills upon there sleeves.

Source:- simplilearn.com


Data Science vs Data Analytics

Data Analyst Skills

Data Scientist Skills

Math & Statistics

Math & Statistics

Programming languages like Python, R, SQL, HTML, JavaScript

Programming languages like Python, R, SAS, Matlab, SQL, Pig, Hive, and Scala.

Spreadsheet Tools (Excel)

Business Acumen

Data Visualization Tools like Tableau

Story-telling and Data Visualization.


Responsibilities

Data Analyst Responsibilities

  • Writes convention SQL queries to search out answers to complicated business queries.

  • Analyse and mines business information to spot correlations and find out patterns from varied information points.

  • Identify any data quality problems and partialities in information acquisition.

  • Map and trace the information from the system to system for solving a given business drawback.

  • Coordinates with the engineering team to collect progressive new information.

  • Design and create|and build} data reports using varied news tools to assist executive to make higher selections.

  • Applying applied math analysis

Data Scientist Responsibilities

  • Become a thought leader on the value of data by finding new options or products by unlocking the worth of data.

  • Data Cleansing and processing -Clean, Massage and organize data for analysis.

  • Identify new business questions that may add price.

  • Develop new analytical methods and machine learning models.

  • Correlate disparate datasets.

  • Conduct relation experiments by applying A/B experiments or epidemiological approach to spot the root issues of an ascertained result.

  • Data Storytelling and visual image.


Conclusion

Data science is liable to transfer massive data, looking out compelling patterns, and eventually advising call manufacturers to bring in the changes effectively to suit the business wants. Data analytics and machine learning are two of the various tools and processes that data science uses.

Data science, data analytics, and machine learning are a number of the most in-demand domains within the industry right now. A mix of the proper ability sets and real-world expertise will assist you to secure a robust career in these trending domains.