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The Data Scientist’s Toolset

  • Post published:February 16, 2021

Are you an aspiring Data Scientist? Do you know the most commonly used data science tools? Data science is a field that is witnessing an exponential demand in the modern world. Many processes that are interlinked with how businesses work in today’s world are directly related to data science.

Essential Data Science Tools

Its growing importance and how it’s relevant in terms of improving the efficiency of businesses makes it one of the highest paying jobs in the world. The act of gathering important data, studying and deriving strategies, and reshaping the data into meaningful data is called data science. The data scientist’s toolbox refers to tools that are to be used to gather, study and reshape data. Let’s take a look at some of the essential data science tools:

1. Tableau

Tableau is an essential data science tool that is used for data visualization. As you can probably guess, data visualization is of the essence when it comes to the field of data science. Tableau allows a concise and clear representation of complex data without wasting your time. Tableau uses a wide range of analytical processes such as cubes, spreadsheets, cloud databases, and relational databases. It has the following set of features:

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  • It provides a comprehensive end-to-end analytics
  • Data calculations (Advanced Level)
  • Content Discoveries
  • No security risks
  • A responsive UI that works well with all kinds of screen sizes
  • Easy customization of datasets

2. TensorFlow

TensorFlow is a name that’s quite famous in the world of machine learning and data science. It provides a holistic ecosystem that consists of multiple tools, resources and libraries that can help you in building and training models. TensorFlow uses minimum resources to provide major functions in a wide range of areas. Here are some features of TensorFlow:

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  • Creation of statistical models
  • Data Visualization aspects
  • Works well with complex mathematical expressions
  • Support for deep neural networks and machine learning concepts
  • High scalability of computation

3. NumPy

Another important player in data science tools is a library known as NumPy. NumPy works in coherence with Python and is a powerful tool for mathematical computations. NumPy is one of the most basic foundations you need for computations and most other libraries require it be a prerequisite. The library treats data in the form of an N-Dimensional array object and performs array operations and statistics.

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Here are some of the features of NumPy:

  • Uses less memory to store data
  • Creation of n-dimension arrays
  • A wide range of mathematical operations
  • Performing array operations and manipulation

4. BigML

An integral part of data science is to use certain tools to make data-driven decisions. BigML is one tool that is essential in terms of accomplishing data-driven decisions. BigML is relevant to Machine Learning and allows building datasets and models with ease. BigML is an influential tool as it has the ability to export models in JSOP PML & PMML which allows a seamless transition from one platform to another.

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Here are some of the features of BigML:

  • Time Series Forecasting
  • Ability to detect anomalies
  • Regression Analysis and Time Series Forecasting
  • Option of Cluster Analysis


Data science tools must include a reliable software that offers multiple mathematical functions. In essence it is a multi-paradigm numerical computing environment that enables processing of complex mathematical information. It isn’t an open-source software however its efficacy is remarkable in terms of providing mathematical input. Some of the common mathematical calculations the software can make include:

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  • Matrix Functions
  • Algorithmic Implementation
  • Statistical Modeling of Data

When it comes to data science, MATLAB is used as one of the major data science tools in terms of simulating neural networks and fuzzy logic.

In a Nutshell

The data science toolbox consists of a holistic set of tools that must be at your disposal at all times. The fact is data science is a complex field but it’s growing demand and intense competition makes it hard to enter in the market successfully.