Key Differences between Data Science and Data Analytics

Data Science and Data Analytics

Data Science and Data Analytics are two disciplines that have experienced an astronomical increase in professional demands. No thanks to Big Data, corporations need to comb through the humongous data at their disposal for swift and efficient decision making.

The development has thrown up many career paths associated with data, the most prominent of which are data science and data analytics. Though both sets of professionals work with data, the difference lies in what they do with it.

Today, we look at the key differences between data science and data analytics.

Definitions

A data analyst examines data sets to decipher the information contained therein, communicates such info via descriptive analysis and visual presentations.

A data scientist, on the other hand, designs models, constructs algorithms, and uses predictive analysis to establish trends, patterns, correlations, etc. from the collected data sets.

Data analysts derive insights from data, while data scientists establish patterns from data to predict likely occurrences.

Skills required to become a Data Scientist or Data Analyst

Data science requires analytical skills and some programming. Skills required include:

  • Ability to work with structured and unstructured data sets
  • Knowledge of Python and other languages such as ‘R’ and Scala
  • Database management
  • Knowledge of analytical functions

Data analysis requires analytical skills as well alongside Maths and Statistics. Skills required include:

  • Advanced proficiency in data visualization
  • Proficiency in Python
  • Proficiency in statistical analysis and spreadsheets

Job Roles

Some data scientist job roles:

  • Decipher insights from data using Machine Learning tools and algorithms
  • Identify new trends in data for making business predictions
  • Data sourcing, investigation, and exploratory data analysis

Some data analyst job roles:

  • Collection and interpretation of data
  • Statistical analysis
  • Reports and visual presentations

Summary

Summarily, data analysis requires a statistical mindset. Data science, on the other hand, requires a mathematical mindset. Though both work with data, analysts are comprehensive, while scientists are predictive.

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