Data Science vs. Data Analytics

What is the difference between Data Science vs Data Analytics?

Data Science:

One who understands the data and business logic and provides predictions by sampling the current business data (also known as “data insights / business insights / data discovery / business discovery”); about the direction in which the business is heading (both good and bad) or where to head by spotting the trends; so that the business can take a right decision on their next steps; such as:

  • improving the product/feature based on user interest levels
  • driving more users
  • driving more clicks/ impressions / conversion / revenue / leads
  • user experience
  • recommendation
  • user retention

Data Scientist Qualifications:

  • Familiar on “how to use database systems (SQL interface, ad-hoc) esp. MySQL and Hive (at-least)” to begin with
  • Java / python / simple map-reduce jobs development, if needed
  • Exposure to various analytics functions (over, median, rank, etc.) and how to use them on various data sets
  • Mathematics, Statistics, Correlation, Data mining and Predictive analytics (fast to future prediction based on probability & correlation)
  • R” and/or “RStudio” (optionally excelSASIBM SPSSMATLAB)
  • Deep insights into (statistical ) data model development (in agile fashion) and in-general self learning model is the best in today’s dynamics; so that it can learn and tune from its own output by combining with performance over the period of time
  • Work with (very) large data sets, grouping together various data sets and visualizing them
  • Familiar with machine learning and/or data mining algorithms (MahoutBayesianClustering, etc.)


Data Analytics:

Data Analytics (DA) in general is a logical extension (or just a buzz word) to Data Warehousing(DW), Business Intelligence (BI); which provides complete insights into business data in most usable form. The major difference in warehousing to analytics is, analytics can be real-time and dynamic in most cases; where as warehouse is ETL driven in off-line fashion.

Every business who deals with “data”, must have “Data Analytics”; without analytics in-place; the business is treated as dead man walking without a heart, a soul and a mind.

Data Analytics (Engineer) Qualifications:

  • Familiar with data warehousing and business intelligence concepts
  • Strong in-depth exposure to SQL and analytic solutions
  • Exposure to hadoop platform based analytics solution (HBase, Hive, Map-reduce jobs, Impala, Cascading, etc.)
  • Exposure to various enterprise commercial data analytical stores (VerticaGreenplumAster DataTeradataNetezza, etc.) esp. on how to store/retrieve data in most efficient manner from these stores.
  • Familiar with various ETL tools (especially for transforming different sources of data into analytics data stores), if needed able to make everything (or some critical business features) real-time
  • Schema design for storing and retrieving data efficiently
  • Familiar with various tools and components in the data architecture
  • Decision making skills (real-time vs ETL, using X component instead of Y for implementing Z etc.)

Sometimes, A Data Analytics Engineer also plays the role of data mining on demand as needed; as he has a better understanding of the data than anyone else; and in-general they have to work closely to get better results.

Data Analytics can also be divided or shared between 4 different teams or people (as it is hard to hire a person with a complete skill-set and more over administration is different from development).

  • data architect
  • database administrator
  • analytics engineer and
  • operations