Top 10 Data Science tools

Data Science tools:

Tools are an important element of the data science field. The open-source community has been contributing to the data science toolkit for years which has led to major advancements in the field. There has been debate in the data science community about the use of open-source technology surpassing proprietary software offered by players such as IBM and Microsoft. Many of the big enterprises have started to contribute to open-source solutions so they can stay top of mind for users and the data science toolkit has increasingly become one dominated by open-source tools.

Since there are a wide variety of open source tools available from data-mining platforms to programming languages, we put together a mix of technology that data scientists could add to their data science toolkit.

1. R:

R is built by data scientists, It is a programming language used for data manipulation and graphics. Originating in 1995, this is a popular tool among data scientists and analysts. It is an open version of the S language that is widely used for research in statistics. According to data scientists, R is one of the easier languages to learn as there are numerous packages and guides available for users.

R has steep learning and is generally built for stand-alone systems. Although there are several packages to speed up the process. The major power of R is its user community which offers extensive support and has developed the package base CRAN. A few great packages for you to start exploring in R would be –

1. ggplot2/ggvis – Data Visualization

2. dplyr – Data Munging and Wrangling

3. data.table – Data Wrangling

4. Caret – Machine Learning Workbench

5. reshape2 – Data Shaping

2. Python:

Python is another widely used language among data scientists, created by Dutch programmer Guido Van Rossem. It’s a general-purpose programming language, focusing on readability and simplicity. If you’re not a programmer but are looking to learn. Python is easier than other general-purpose languages. You can do all sorts of tasks such as sentiment analysis or time series analysis with Python. It is a very versatile programming language, you can canvass open data sets and do things like sentiment analysis of Twitter accounts.


KNIME is a software company with headquarters in major tech hubs around the world. The company offers an open-source analytics platform written in Java. It is widely used for data reporting, mining and predictive analysis. This base platform can be advanced with a suite of commercial extensions offered by the company, including collaboration, productivity and performance extensions.

4. SQL:

Structured Query Language (SQL) is a special-purpose programming language for data stored in relational databases. It is used for more basic data analysis. It can perform tasks such as organizing and manipulating data or retrieving data from a database. Since SQL has been used by organizations for decades, there is a large SQL ecosystem in existence already that data scientists can tap into. Among data science tools, it ranks as one of the best at filtering and selecting through databases.

5. Apache Hadoop:

Apache Hadoop software library is a framework, written in Java for processing large and complex datasets. The base modules for the Apache Hadoop framework include Hadoop Common, Hadoop Distributed File System (HDFS), Hadoop Yarn and Hadoop MapReduce.

6. Impala:

Impala is an open-source massively parallel processing (MPP) database for Apache Hadoop. It is used by data scientists and analysis allowing them to perform SQL queries for data stored in Apache Hadoop clusters.

7. MongoDB:

MongoDB is a NoSQL database known for its scalability and high performance. It provides a powerful alternative to traditional databases and makes the integration of data in specific applications easier. It can be an integral part of the data science toolkit if you’re looking to build large-scale web apps.

8. D3:

D3 is a javascript library for building interactive data visualization within your browser. It allows data scientists to create rich visualizations with a high level of customizability. It’s a great addition to your data science toolkit if you’re looking to dynamically express your data insights.

9. Tensor Flow:

Tensor Flow is the product of Google’s Brain Team coming together to advance machine learning. It’s a software library for numerical computation and is built for everyone from students and researchers to hackers and innovators. It allows programmers to access the power of deep learning without needing to understand some of the complicated principles behind it and ranks as one of the data science tools that help make deep learning accessible for thousands of companies.

10. RStudio:

RStudio integrates with R as an IDE to provide further functionality. It combines a source code editor, build automation tools and a debugger.