{"id":55238,"date":"2018-01-19T12:52:49","date_gmt":"2018-01-19T07:22:49","guid":{"rendered":"https:\/\/www.whizlabs.com\/?p=55238"},"modified":"2018-01-19T12:52:49","modified_gmt":"2018-01-19T07:22:49","slug":"data-scientists-tools-to-improve-productivity","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/data-scientists-tools-to-improve-productivity\/","title":{"rendered":"Data Scientists Tools To Improve Productivity"},"content":{"rendered":"

The role of a data scientist role is merely limited to data analysis or statistical analysis. You may consider a 360-degree function of a data scientist related to business data, he is going to deal. Hence, he needs to pitch in almost all the areas of business data handling all the functions from sourcing to execution. The inclination is more on the techniques they are using to solve a problem. However, data scientists tools and technologies also play a significant role to get a productive result. <\/span><\/p>\n

Well, with the manifold of data science tools in the market, it is certainly a rising challenge for you as a data scientist or a blooming data scientist to sort out the best ones. Moreover, it depends on your solution approach towards the problem. However, every trade asks for some essential skills. Not required to mention, as a data scientist you must be getting acquainted with the available data scientists tools in the market and more importantly the essential ones.<\/span><\/p>\n

Common Data Science Tools and Technologies in the Market<\/span><\/b><\/h2>\n

“Process, perform and visualize the data” –<\/strong>\u00a0Probably this is the key \u2018mantra’ for a data scientist. Hence, a data scientist should possess a working knowledge of statistical programming languages. Along with it, he must be capable of constructing data processing systems, performing database operations, and handling visualization tools. In addition to that, the knowledge of programming language is a plus.\u00a0 So, a fair understanding of programming tools and user-friendly graphical interface help them to build predictive models more productively. <\/span><\/p>\n

Let’s have a look at the standard tools for data scientists in the stack:<\/span><\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Task of a Data scientist<\/span><\/b><\/td>\nCommonly Used Tools<\/span><\/b><\/td>\n<\/tr>\n
Data sourcing<\/span><\/td>\nMongoDB, Hadoop HDFS, Riak, SAP, Cassandra, Redis<\/span><\/td>\n<\/tr>\n
Data storing<\/span><\/td>\nOracle, SAP Sybase, MySql, Apache HBase, Neo4j<\/span><\/td>\n<\/tr>\n
Data conversion and ETL<\/span><\/td>\nSqoop<\/span><\/td>\n<\/tr>\n
Data transformation<\/span><\/td>\nHive<\/span><\/td>\n<\/tr>\n
Exploratory analysis<\/span><\/td>\nElastic search, knime<\/span><\/td>\n<\/tr>\n
Model building and insight generation<\/span><\/td>\nR, SAS, pandas, Python, Julia, Rapid Miner, SPSS, Mahout, SAP HANA, Clojure<\/span><\/td>\n<\/tr>\n
Visualization<\/span><\/td>\nGgplot2, SAP Business Objects, Tableau, Cognos, JMP, JasperSoft<\/span><\/td>\n<\/tr>\n
Model execution<\/span><\/td>\nHadoop, Java, Spark, Scala, C#, Storm<\/span><\/td>\n<\/tr>\n
Versioning<\/span><\/td>\nGit<\/span><\/td>\n<\/tr>\n
IDE<\/span><\/td>\nRStudio, Sublime<\/span><\/td>\n<\/tr>\n
Text for coding<\/span><\/td>\nJupyter Notebook, R Shiny<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

A Cluster Categorization of the Hottest Data Science Tools <\/span><\/b><\/h4>\n

As per <\/span>2014 Data Science Salary Survey<\/span>, data scientists tools fall into four clusters and that cover almost 35 tools in total.<\/span><\/span><\/p>\n

Each of the clusters depicts data scientist roles to get the best outcome with the tools and technologies used for that particular data scientist role.<\/span><\/span><\/p>\n