Photo by Russ Hendricks
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 to 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. In fact, 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.
Our Favorite Data Science Tools
1.-R
R is a programming language used for data manipulation and graphics. Originating in 1995, this is a popular tool used among data scientists and analysts. It is the open-source version of the S language 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.
2-Python
Python is another widely used language among data scientists, created by Dutch programmer Guido Van Rossum. It’s a general-purpose programming language, focusing on readability and simplicity. If you are not a programmer but are looking to learn, this is a great language to start with. It’s easier than other general-purpose languages and there are a number of tutorials available for non-programmers to learn. You can do all sorts of tasks such as sentiment analysis or time series analysis with Python, a very versatile general-purpose programming language. You can canvass open data sets and do things like sentiment analysis of Twitter accounts.
3-KNIME
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, 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-Gawk
Gawk is the open-source version of awk, a special-purpose programming language used for working on files. Awk is one of the many components of the Unix operating system. Gawk is a GNU implementation which makes it easy to make changes in text files and allows users to extract data and generate reports.
5-Weka
Weka is a machine learning software written in Java by The University of Waikato. It is used for data mining, allowing users to work with large sets of data. Some of the features of Weka include preprocessing, classification, regression, clustering, experiments, workflow, and visualization. However, it lacks advanced functionality compared to R and Python which is why it’s not as widely used in professional settings.
6-Scala
Scala is a general-purpose programming language that runs on the Java platform. It’s great for large datasets and is largely used with big data tools like Apache Spark and Apache Kafka. This functional programming style results in speed and higher productivity which has led it to slowly be adopted by an increasing number of companies as an essential part of their data science toolkit.
7-SQL
Structured Query Language or SQL is a special-purpose programming language for data stored in relational databases. SQL is used for more basic data analysis and 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 which data scientists can tap into. Among data science tools, it ranks as one of the best at filtering and selecting through databases.
RapidMiner is a predictive analytics tool with visualization and statistical modeling capabilities. The base of the software which is RapidMiner Studio is a free, open-source platform. The company also provides enterprise-level add-ons which can be bought to supplement the base platform.
Scikit-learn is a machine learning library, largely written in the Python programming language and built on the SciPy library. It was originally developed as a Google Summer of Code project where Google awarded students who were able to produce valuable open-source software. Scikit-learn offers a number of features including data classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
Related: A Beginners Guide to Neural Networks in Python & SciKit Learn
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.
Apache Mahout is an environment for building scalable machine learning algorithms. The algorithms are written on top of Hadoop. Mahout implements three major machine learning tasks: collaborative filtering, clustering, and categorization.
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12-Apache Spark
Apache Spark is a cluster-computing framework for data analysis. It has been deployed in large organizations for its big data capabilities combined with speed and ease of use. It was originally developed at the University of California as Spark and later, the source code was donated to the Apache Foundation so that it could be free forever. It’s often preferred to other big data tools due to its speed.
13-SciPi
SciPi or Scientific Python is a computing ecosystem based on the Python programming language. It offers a number of core components including NumPy for numerical computation, Matplotlib for plotting and the SciPy library which is a collection of algorithms and functions.
14-Orange
Orange is one tool among data science tools that promise to make data science fun and interactive. Compared to many of the tools discussed here, this one is simple and keeps things interesting for data scientists. It allows users to analyze and visualize data without the need to code. It offers machine learning options for beginners.
15-Axiis
Axiis is a lesser-known data visualization framework among data science tools. It allows users to build charts and explore data using pre-built components in an expressive and concise form.
16-Impala
Impala is the massive parallel processing (MPP) database for Apache Hadoop. It’s used by data scientists and analysts allowing them to perform SQL queries for data stored in Apache Hadoop clusters.
17-Apache Drill
Apache Drill is the open-source version of Google’s Dremel for interactive queries of large databases. It’s powerful, flexible, and agile, supporting data stored in different formats in files or NoSQL databases and is one of the most versatile data science tools.
18-Data Melt
Data Melt is a mathematical software which will make your life easier with its advanced mathematical computations, statistical analysis, and data mining capabilities. This software can be supplemented with programming languages for added customizability and even includes an extensive library of tutorials.
19-Julia
Julia is a dynamic programming language for technical computing. It’s not widely used but is gaining popularity among data science tools because of its agility, design, and performance.
20-D3
D3 is a javascript library for building interactive data visualizations 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.
21-Apache Storm
Apache Storm is a computational platform for real-time analytics. It’s often compared to Apache Spark and is known as a better streaming engine than Spark. It’s written in the Clojure programming language and is known to be a simple, easy-to-use tool.
22-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.
23-TensorFlow
TensorFlow is the product of Google’s Brain Team coming together for the purpose of advancing machine learning .and is very popular among data scientists and machine learning engineers. It’s a software library for numerical computation and 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 helps make deep learning accessible for thousands of companies.
24-Keras
Keras is a deep learning library written in Python. It runs on TensorFlow allowing for fast experimentation. Keras was developed to make deep learning models easier and helping users treat their data intelligently in an efficient manner.
We hope you’ve got some new data science tools for your data science toolkit in this article! Comment below if you can think of any more.
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