The Data Science Toolkit: 20+ free data science tools

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Published on July 15th, 2022

Tools are an essential element in the field of data science. The open source community has been contributing to the data science toolkit for years, which has led to a lot of progress in the field. There has been a debate in the data science community about using open source technology to overcome the proprietary software offered by players like IBM and Microsoft. So, many large enterprises have started contributing to open source solutions to stay at the forefront of users’ minds, and open source tools are increasingly dominating the data science toolkit.

With a wide variety of open source tools available, from data mining platforms to programming languages, we’ve combined various technologies that data scientists could add to their data science toolkit.

Table of Content

Here are 20+ free data science tools that are in demand

1. Python

Python is a widely used language in data science, and it’s one of the high-on-demand data science tools for beginners, created by Dutch developer Guido Van Rossum. It is a widely helpful programming language focused primarily on clarity and simplicity. If one is not a developer but rather hoping to learn, it’s an incredible language to start with. It’s simpler than other widely applicable dialects, and there are various tutorials that even non-software engineers can learn. With Python, a flexible and widely helpful programming language, you can perform multiple tasks such as time series analysis or sentiment analysis. You can spread open data collections and do things like sentiment analysis of Twitter accounts.

2. NumPy

NumPy is a general-purpose array processing package and it is a very popular data science tool for beginners. It provides high-performance multidimensional array objects and tools for manipulating those arrays. This tool works with data as an N-dimensional array object. It provides tools for manipulating arrays and performing standard linear algebraic calculations such as array manipulation, basic statistics, and dot product operations.

3. Pandas

The Pandas library simplifies data manipulation and analysis in Python. Pandas works with two primary data structures. They are Series, a one-dimensional labeled array, and DataFrame, a two-dimensional labeled data structure. The Pandas package has many tools for reading data from various sources, including CSV files and relational databases.

Once the data is exposed as one of these data structures, pandas have a wide range of specific functions for cleaning, transforming, and analyzing the data. These include built-in missing data tools, simple plotting functions, and Excel-like pivot tables.

4. SciPy

SciPy is another basic scientific computing python library. Scipy effectively builds on the math functions available in NumPy. Where NumPy provides high-speed manipulation of arrays, SciPy works with those arrays and allows the application of advanced mathematical and scientific calculations.

5. Scikit-learn

Scikit-learn is an AI library, largely written in the Python programming language and based on the SciPy library. It was originally developed as the Google Summer of Code project, where Google provided internships to students who had created significant open source software. Scikit-learn offers various strengths, including data clustering, regression, clustering, dimensionality reduction, model determination, and preprocessing.

6. Keras

Keras is a python API that aims to provide a simple interface for working with neural networks. Popular deep learning libraries like Tensorflow are notorious for not being very user-friendly. Keras sits on top of these frameworks and provides a friendly way to interact with them.

Keras supports convolutional and recurrent networks, sponsors multi-backends, and runs on both CPU and GPU.

7. TensorFlow

TensorFlow is a product of the Google Brain Team, which has come together to develop machine learning and is in too much demand among data scientists and machine learning engineers. It’s a software library for numerical computing and built for everyone from beginners to an expert. It allows ones to access the power of deep learning without having to understand some of the complicated principles behind it and is among the data science tools that help make deep learning accessible to thousands of companies.

8. Matplotlib

Matplotlib is the most popular data science tool for beginners used for plotting. Many other popular plotting libraries depend on the matplotlib API, including the pandas plotting function and Seaborn.

Matplolib is a rich plotting library that includes functions for creating various graphs and visualizations. Additionally, it has features for creating animated and interactive charts.

9. Jupyter Notebooks

Jupyter notebooks are a very popular data science tool in 2022 and are in great demand for data science beginners. These provide an interactive Python programming interface. The advantage of writing Python in a notebook environment is that it lets you quickly render visualizations, datasets, and data summaries directly in the program.

10. Gawk

Gawk is an open source rendition of awk, which is the specific reason for a programming language used for working with documents. Awk has one of the huge components of the Unix framework. Gawk is a GNU utility that makes it easy to make changes to textual records and allows clients to extract information and generate reports.

11. Weka

Weka is AI programming written in Java at the University of Waikato. It is used for data mining and allows clients to work with huge arrays of data. The Weka highlights section includes preprocessing, ordering, regression, clustering, trials, workflow, and visualization. However, it needs advanced utility in contrast to R and Python, which is why it is not as widely used in experienced settings.

12.Scala

Scala runs on the Java platform. It is exceptional for huge datasets and is generally used with big data engines like Apache Spark and Apache Kafka. This useful programming style brings speed and greater efficiency, which has led to it being adopted by a growing number of organizations as a core part of their data science toolkit.

13. SQL

Structured Query Language (SQL) is a specific programming language reason for information stored in relational datasets, and it is high on demand data science tool in 2022. SQL is used for more fundamental data analysis and can perform operations such as arranging and manipulating data or retrieving data from a dataset. Since SQL has been used by associations for quite a long time, data scientists can take advantage of the already existing huge body of SQL. Among information science tools, it ranks as truly excellent at sifting and selecting datasets.

14. RapidMiner

RapidMiner is an insightful investigation tool with visualization and statistical demonstration capacities. The basis of the product that is RapidMiner Studio is a free and open-source platform. The organization also provides additional enterprise-level items that can be purchased to enhance the base platform.

15. Apache Hadoop

The Apache Hadoop programming library is a system written in Java for preparing huge and complex datasets. The core modules of the Apache Hadoop framework include Hadoop Common, Hadoop MapReduce, Hadoop Yarn, and Hadoop Distributed File System (HDFS).

16. Apache Spark

Apache Spark is a group of figural frameworks for data analysis. It has been shipped in huge associations for its enormous information capabilities associated with effortless utilization. It was originally developed as Spark at the University of California, and later the source code was bought by the Apache Foundation so it could be free forever. It is often favored over other big data tools because of its speed.

17. Orange

Orange is one of the in-demand tools among data science tools. It is a powerful tool to perform data analysis and visualization, see data flow, and become more productive. It allows clients to dissect and visualize data without coding. This makes machine learning a good choice for beginners.

18. Axis

Axiis is a lesser-known data visualization system among data science tools. It allows clients to create graphs and explore data using pre-built parts expressively and concisely.

19. Impala

Impala is an MPP-SQL (Massive Parallel Processing) query engine for processing large amounts of data stored in Hadoop clusters. This is open-source software written in C ++ and Java. It offers high performance and low latency compared to other Hadoop SQL engines.

In other words, Impala is the most powerful SQL engine (with an RDBMS-like experience) and provides the fastest way to access the data stored in the Hadoop distributed file system.

20. Apache Drill

Apache Drill is an open-source variant of Google’s Dremel for intelligently querying huge data sets. It is incredible, adaptable, and nimble, supports information stored in various configurations in documents or NoSQL datasets, and is perhaps the most flexible tool for data science.

21. Data Melt

Data Melt is numerical programming that makes your life easier with high-level numerical calculations, data mining, and statistical analysis capabilities. This product can be extended using programming dialects.

22. Julia

Julia is a powerful programming language designed to give users the speed of C/C++ while remaining as easy to use as Python. It is not widely used, but due to its performance and design, it will be gaining popularity among data science tools in 2022.

23. D3

D3 is used to create data visualizations inside your program, and it is a JavaScript library. It enables data scientists to create rich representations with a significant degree of adaptability. It’s a great addition to your data science toolkit if you want to communicate your data insights incrementally.

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