Top 5 tools Used in Data Science

Tools used in Data Science

A famous quote by Atul Butte from Stanford University ;

Hiding within those mounds of data is the knowledge that could change the life of a patient, or change the world.

It gives us the domains that Data Science is touching these days and bringing some of the best transformations in today’s world. Almost all the existing domains in today’s world like Healthcare, Education, Finance etc are being powered at some or the other extent by Data Science. Hence, the demands of Data Scientists is increasing very rapidly. Certain job surveys stated that Data Science is one of the most sought-after jobs in today’s world. And the companies are trying to mold their products according to the demand of the users by analyzing data.

But as the career of being a data scientist is gaining momentum; so is the confusion amongst the masses. As they are not able to fix the perfect tool for working in the domain of Data Science. But, this post will help you to get above this confusion and start making a career in the field of Data science.

To get started, let’s list some of the most famous Data Science Tools :

  1. Python
  2. R
  3. SQL
  4. Hadoop
  5. MATLAB

The above-mentioned tools are some of the best tools to start a career in Data Science (Note: This list is not the end of the cool Data Science tools available in the market. this list was compiled by me as per my experience reading various surveys, articles etc.). As the tools have been mentioned above, let’s dive deep into some of the most popular Data Science tools that a large section of people in Data like Data Scientists, Data Engineers, Data Architect etc use on a daily basis.

Data Science Tool #1

Python : Python is a general-purpose programming language that is gaining popularity for doing data science due to the large options of libraries like Numpy, Pandas etc. These libraries help in deriving useful insights from tons of data. Companies worldwide are using Python to harvest insights from their data and get a competitive edge. So, learning Python with a good knowledge of libraries like Numpy, Pandas along with data visualization tools like Matplotlib etc can be a good choice to start your career in Data Science. Explore the future of Python here.

Data Science Tool #2

R : R is a highly in use & powerful language for data analysis and statistical computing. After its development, endless efforts by a lot of users worldwide have made R to grow continuously. R is gaining popularity because of the inclusion of powerful packages such as dplyr, tidyr, readr, data.table, SparkR, ggplot2 thus making data manipulation, visualization and computation much faster. A lot of organizations are using R along with Python to extract some cool insights from the data.

Data Science Tool #3

SQL : SQL is a language highly in use for Data Science, but limits only till filtering based on some columns, grouping by some category etc. Though SQL is quite good for writing complex queries or joins, but it can’t handle any serious data wrangling/cleaning/modeling tasks which frequently comprise any data science project.

Data Science Tool #4

Hadoop : Hadoop is an open-source software framework that provides a perfect platform for processing of large data sets across clusters of computers using simple programming models. It helps in scaling up from single servers to thousands of machines. The storage of Hadoop unencumbers the schema-related constraints commonly found in SQL-based systems. Organizations are using Hadoop to stage large amounts of raw, sometimes unstructured data for loading into enterprise data warehouses. Many of Hadoop’s largest adopters use it for the real-time data analysis that enables web-based recommendation systems.

Data Science Tool #5 

Tableau : Tableau is one of the most popular Data Visualization tools used by people. It enables you to create insightful and impactful visualizations in an interactive and colorful way. The power of Tableau is that you can make descriptive/insightful infographics with gestures as simple as drag and drop. And it doesn’t even require a single ounce of coding. You can also carry out basic chart formation, calculations, parameters etc. with Tableau.

So, these were some of the most in-demand tools. Though, the list and a short description of a few tools above can help you to start in a very beginner friendly way. But it’s important to know the concepts like Probability, Statistics, Calculus. You can start with any of the tools as all of them are in huge demand these days. But several surveys and people suggest starting with Python for diving deep into Data Science.

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