Let’s start from the beginning. “Data scientist” and “data analyst” are not the same thing. There’s really a distinction between the two in terms of skill set and experience.
Data scientists can perform deep information analysis and can combine quantitative modelling like computer science, physics, statistics, or applied mathematics with the knowledge to invent new algorithms to solve data problems. They are extremely valuable assets to their companies due to their businesses acumen that can help increasing their company’s business results by identifying opportunities of earning or saving the organization money by identifying hidden patterns in data.
The following is an example of how complex the data scientist’s toolkit can be:
- Java, R, Python.
- NoSQL, MongoDB, CouchBase, Cassandra.
- Hadoop, HDFS & MapReduce.
- HBase, Pig&Hive.
- Google Cloud (BIGQUERY, DATAFLOW, DATAPROC, DATALAB, PUBSUB).
- SPSS, Matlab, SAS.
- ETL, Webscrapers, Flume, Sqoop.
- D3.js, Gephi, ggplot2, Tableau, Flare, Qlik, Kplipfolio, Flare.
- SQL, RDBMS, DW, OLAP and, why not Excel?
Some of the most valuable nontechnical skill a data scientist brings to the table is an intense inquisitiveness. Data scientists have to be driven to pose questions and hunt down solutions, and in so doing to unearth information that could transform a business.
Doclens had designed a very accurate infographic about the Data Scientist hard and soft required skills.
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