Key differences between Data Analytics and Data science Explained

 Data Analytics and Data science

Today, the entire world contributes to massive data growth in gigantic volumes, therefore the name, Big Data. Big data has brought two other popular streams in the digital industry which is Data Analytics and Data Science. The World Economic Forum states that by the end of 2020, the daily global data generation would reach 44 Zettabytes. By 2025, this number would reach 463 Exabytes of data!

 

 

Data Analytics

Data Analytics is a process of displaying, defining and cleaning data using BI tools. We can convert raw data into useful statistics and explanation. Data analytics is knowledge of organizing data related to numbers and brilliant problem solving skills along with:

 

• Creating and maintaining databases, and data systems – organizing data in a proper format
• Using automated softwares to fetch the data from primary and secondary sources
• Knowledge of SQL database, Python or R  to align the data

 

As a data analyst, you should possess strong mathematical skills and proficiency in statistics and statistical tools like SAS and Excel.

 

There are different types of Data analysts

• Business Analyst
• Research Analyst
• Intelligence Analyst

 

Students from mathematics, computer science background have a higher chance to choose their career in data analytics. However, online bootcamp or course from the well reputed institution will land you in the ever-growing data analytics industry.

 

Data science

Data science mainly focuses on machine learning, predictive modelling and data visualization. Data scientist is an professional who is responsible for organizing, analyzing and interpreting the data.

As a data scientist, industry professionals have to work with cyber security threats, market conditions, financial risks and so on.

 

 

They should also possess the following skills:

-Experience in programming languages like Python,Scala and SQL.

-Strong knowledge in big data tools like Spark,MongoDB and Spark

-Proficiency in Data visulaization tools like Tableau and others

 

Data scientist are also responsible for defining the data collection and analysis. They would also work in AI driven technologies like Automatic machines and Self-driving cars.

 

Some of the crucial differences between Data science and Data Analytics are:

 

• -Data science often focuses to systems that can predict future outcomes while Data Analytics mainly focuses on analyzing the past data to make the effective decisions in the present
• - Scope of data science is Macro while the scope of Data analytics is compartively Micro
• -Major fields in data science are Machine learning & AI. Data analytics on the other hand helps in Travel,gaming & Health care industries.

These are some of the main differences between Data science and Data analytics. Stay connected with us to gain lot of insights about ever growing digital marketing industry.