Data Science and Data Analytics

Difference Between Data Science and Data Analytics

Data Science Vs Data Analytics? This thought comes on every student mind. What is the difference between Data science and Data Analytics as both the terms are quite similar? 

So, in this blog we will find out the difference between data science and data analytics with examples in term of job responsibilities, skills required and career perspective. 

Data Science Vs Data Analytics: Which One is Right for You?

It is unfair to say which one is right because both the fields have their own perks and offer a very good career growth. The main focus of Data science is to find out the resourceful correlations between large datasets while a data analytics is intended to discover the essentials of extracted data. Now let us understand the difference between Data Science and Data Analytics in easy language

In more simple words, we can say that Data analytics is a subset of data science that emphaseson more definite answers of the questions that a Data Science fetches out.

Job Responsibilities of Data Scientists:

  • Forcleaning, processing, and validating the dataintegrity.
  • ForexecutingExperimental Data Analysis on huge datasets.
  • For accomplishing data mining viamaking the pipelines of ETL.
  • For executing statistical analysis through the Machine Learning algorithms such as Random Forest, KNN, Decision Trees, logistic regression, etc.
  • For executing code forbuilding and automation practical Machine learning libraries.
  • For gleaninginsights of business by using Machine learningalgorithmsandtools.
  • For identifyinglatest trends in data tocreate predictions of business.

Job Responsibilities of Data Analysts:

  • For interpreting and collecting the data.
  • For identifying and checkingrelatedoutlines in a dataset. 
  • For performing data querying by using SQL.
  • For experimenting with numerous tools of analytical suchprescriptive analytics, predictive analytics, diagnostic analytics and descriptive analytics
  • For using data tools of visualizationsuch as IBM Cognos Analytics, Tableau, etc., in order to present the extracted info. 

Skills difference between Data Science and Data Analytics

Let us know learn the skills required to become a Data Science and Data Analytics.

Skills to Become a Data Scientist:

  • An expert in Statistics and Probability.
  • Master in Linear Algebra.
  • An expert in Multivariate Calculus.
  • An expert in in programming in Python, MATLAB, R, Scala, Java,JuliaandSQL.
  • Skilled in database management
  • Proficient in data wrangling
  • An expert in Machine Learning.
  • Master in using Big Data platforms such as Hadoop, Apache Spark, etc.

Skills to Become a Data Analytics:

  • Experienced in Excel
  • Proficient in SQL database.
  • Skilful in using tools like Tableau, SAS, Power BI and many more. 
  • Expert inPython or R programming. 
  • Proficient in data visualization.

Conclusion:

Here we have discussed about the difference between Data Science and Data Analytics. If you are looking to find out which career option is better for you then you must check our blog on Data Science Vs Data Analytics.

About the Author

Robert Wagner (Information Technology)

Robert Wagner is an IT expert and a consultant having more than 35 years of experience in Information Technology. He loves to write about the latest skills and jobs in demand to make our generation aware about the top skills and certifications in IT.

Leave a Reply

Your email address will not be published.

You may also like these