Data Analytics Vs Data Science

While the terms Data Science and Data Analytics are used interchangeably, both are quite unique fields, with the only difference being in terms of its scope.

A data scientist is expected to forecast future based patterns, data analysts extract meaningful insights from various data sources. A data scientist creates questions, while a data analyst finds answers to the existing set of questions.

While data analytics and data scientists both work with data, the main difference in their job lies with what they do with it. Data Analysts examine large data sets to identify trends development charts and create visual presentations to help businesses make more strategic decisions.

Data Scientists, on the other hand, deal with the designs and construct new processes for data, modeling, and production using prototypes, algorithms, predictive models, and custom analysis.

Fundamentally, data analytics uses data to use to draw meaningful insight and solve problems. They analyze a well-designed data with the set of tools to solve tangible business needs. For instance, analyzing data so as to answer: why sales dropped in a certain quarter?

Skills Required

Data Analytics:

  • knowledge of intermediate statistics
  • excellent problem-solving skills
  • Dexterity in Excel and SQL database to slice and dice data.
  • Experience working with BI tools like Power BI for reporting.
  • Knowledge of Stats tools like Python, R, or SAS.

To become a data analyst, one need not necessarily hail from an engineering background but having strong skills in statistics suffices the requirement; predictive analytics being an added advantage.

Data Science:

  • Math’s, advanced statistics, predictive modeling
  • Machine Learning programming
  • Big Data tools like Hadoop and shark.
  • Expertise in SQL and NoSQL databases like Cassandra and MongoDB.
  • Experience with data visualization tools like Qlikview, D3.JS, and Tableau.
  • Dexterity in programming languages like python, R, and Scala.  
Also Read  The Relationship between Analytics and Product Management

Jobs Roles

Data analyst job

  • Processing, cleansing, and verifying the integrity of data.
  • Exploratory data analysis
  • Gleaning business insights using machine learning techniques and algorithms.
  • Identifying new trends in data to make predictions for the future.
  • develop visualizations and KPI’S.
  • discover new patterns using various statistical tools 

Data Science Job:

  • Working closely with the stakeholders to understand their goals
  • determine how data can be used to achieve their goals
  • design data modeling processes
  • create algorithms and predictive models to extract the data into the business needs
  • extract the data and drive and share it with the peers

Data Analysis approach is Focused i.e. it serves as a means to answer the questions that are very specific based on existing data. Data Science produces broader insights that concentrate on which questions should be asked, while big data analytics emphasizes discovering answers to questions being asked.

The two fields are considered different sides of the same coin and their functions are highly interconnected. Data science lays important foundations and parses big data sets to create initial observations, future trends, and potential insights that can be important. By adding Data Analytics into the mix, we can turn around “we know, we don’t know” data into actionable insights with practical applications. Choose your strengths and interest and decide accordingly.

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