Tools Commonly Used by Data Scientists

Expertise in Tools Commonly Used by Data Scientists is still in hot-demand and the demand won’t go away anytime soon, the fact again is that Data Science has been termed the 21st century hottest job. A data scientist needs to analyze vast amounts of data and make use of sophisticated tools to make the transition of data to insights possible. The scope of the job of a data scientist includes identifying large data sources and quality, analyzing the data, working on training models with the data to produce improved technologies and processes.

Example of what data science tools can do include:

  • Building predictive models of customer behaviour in banks, shopping mall, cinemas etc.
  • Building predictive technology that recommends or advice the next line of actions or interests to website visitors based on their past activities.
  • Building predictive technology that detects or predict the likelihood of occurrence of a particular disease.
  • Running experiments on data to test product changes, clinical findings, security systems etc.
  • Building Big data-driven technology like self-driving cars, delivery robots, precision drowns and self-flying aeroplanes and farm technologies etc.
  • Building data products that enable new product or service features.
  • Post-marketing surveillance.

Some Tools Commonly Used by Data Scientists is as follows based on specific functions:

  1. Data Wrangling: R, SQL, Hive and Spark
  2. Machine Learning: Majorly R and Scikit-Learn in Python Programming
  3. Data Visualization: ggplot2 for R, Matplotlib in Python, rCharts, D3.js, Power BI and Tableau
  4. Data Management and Analysis: tidy in R, NumPy, Scipy and Panda for Python, STATA syntax, SAS, SPSS GUI, and Microsoft Excel
  5. Core Programming: JAVA, R and Python