Data scientist remains one of the most popular jobs of the last decade. And with the large amounts of data we create every day, there’s no surprise that there are several tools people use to make sense of it.
Data science teams have plenty of tools and platforms at their disposal to help achieve their analytics goals. In order to aid you in the analytics process, it’s important to choose the right tools for the job. Here is an overview of 20 top data science tools, categorized by their dominant functions, with detailed information on their features and real-world applications in the field.
Programming Languages
Here are some programming languages.
Python
This is a popular programming language created in 1991 by Guido van Rossum. It is used for system scripting, software development, mathematics, and web page development.
Python is a general-purpose language whose design philosophy focuses on readability. The main idea of this high-level and interactive language is that it is designed to be easy to use and fun.
R
R is a programming language used for graphical representation and statistical analysis. Robert Gentleman and Ross Ihaka created this software environment.
Bioinformatics, Data Miners, and Statisticians use R to develop statistical language. One thing about R is that it’s an interpreted language that can be accessed through a command line.
SQL
SQL, or Structured Query Language, is a programming language that processes and stores information in a relational database. This relational database stores the information by representing the data in a tabular form with columns and rows representing different data sets.
SQL was first invented in the 1970s and is widely used because it integrates well with other languages.
Data Analysis and Visualization Tools
Here are some tools for data analysis and data visualization.
Excel
Excel is a spreadsheet program obtained from Microsoft and is part of the Office product group. Excel enables users to organize, calculate, and format data in a spreadsheet.
Excel is important because it enables data analysts to make information more palatable and understandable as data is added. To do this, Excel uses a large collection of cells divided into rows and columns to manage data. You can also automate Excel with Python.
Tableau
Tableau is a data visualization software focused on business intelligence used to analyze large data volumes. It was created in 2003, and since then, Tableau has helped users create charts, maps, stories, graphs, and dashboards to analyze and visualize data so people can make business decisions.
With Tableau, you don’t need any prior programming language, just an understanding of the program.
Power BI
Power BI is an interactive software that gives nontechnical users the tools for analyzing, sharing, and visualizing data in a business intelligence platform. Power BI offers deep integration with several Microsoft products making it a flexible tool.
With this software, users can find insights in data, connect data sets, and clean data to turn it into an understandable data model.
Kibana
Kibana is an open-source and free visual interface tool that allows you to visualize, explore and manage Elasticsearch data.
Kibana has a major integration with Elastic Stack and Elasticsearch, which makes it excellent for security analysis, application performance monitoring, and geospatial data analysis.
Pandas
Pandas is an easy-to-use data manipulation and analysis tool written for Python. This tool is open-source and provides labeled data structures. Panda is built on top of another Python package, Numpy, and is used for machine learning and data science tasks.
For the most part, Pandas helps you do many other tasks such as data fill, merges and joins, statistical analysis, loading and saving data, and data visualization.
NumPy
In 2005, NumPy, which stands for Numerical Python, was crafted by Travis Oliphant. It is a basic package used for analysis in Python. It provides various derived objects and multidimensional arrays as well as several routine assortments used for fast operations such as shape manipulation and mathematical logic.
You would find most people using NumPy when manipulating an array. You can also use NumPy in linear algebra, matrices, and fourier transform.
Cloud Computing Platforms
Here are some cloud computing platforms to look out for.
Amazon Web Services (AWS)
Amazon Web Services (AWS) is a cloud computing platform that provides a range of services, such as platform-as-a-service (PaaS), infrastructure-as-a-service (IaaS), and software-as-a-service (SaaS) to organizations. These services include storage for databases, computing power, and content delivery.
AWS was first launched in 2002 to handle its online retail operations for Amazon. In 2006, AWS introduced its IaaS services and was one of the first companies to offer a pay-as-you-go cloud computing model that enables users to scale their computing, storage, and throughput services based on their needs. Since then, they’ve continued to evolve, providing more than 170 AWS services.
Google Cloud Platform (GCP)
Google Cloud Platform(GCP) is a cloud computing platform and public cloud vendor like AWS. Google’s data centers offered by GCP give customers free or pay-per-use access to computer resources worldwide.
GCP’s computing services cover everything from data management to AI and machine learning tools to web and video delivery over the internet. Google Cloud should not be confused with Google Cloud Platform. Google Cloud refers to the various internet-based services that assist organizations in going digital, while Google Cloud Platform is a subset of Google Cloud which give public cloud infrastructure for hosting web-based applications.
Big Data Processing Tools
Here are some popular tools for processing big data.
Apache Hadoop
The Apache Hadoop is a collection of software utilities providing a framework for processing vast data sets across computer clusters. It does this by utilizing straightforward programming models. Essentially, this software uses several computers on a network to solve problems.
Apache Hadoop is engineered to scale seamlessly from individual servers to thousands of machines, with each one offering localized storage and computation.
Apache Spark
Apache Spark processes vast data sets with speed and efficiency. It does this by distributing processing tasks across multiple computers alone or in conjunction with other distributed computing tools.
These capabilities are essential in big data and machine learning, where a lot of computing power is necessary to analyze massive data stores. Apache Spark additionally simplifies the programming workload for developers. Since 2009, when Apache Spark was created, it has offered a user-friendly API that abstracts much of the complexity of distributed computing and big data processing.
Apache Kafka
Apache Kafka platform is distributed and is designed specifically for building streaming data pipelines and applications that can adapt to real-time data streams. This means that as billions of data sources continuously generate streams of data records, including streams of events, Kafka provides the infrastructure to respond to these events as they are happening.
It was originally developed by LinkedIn in 2011 for their use. Kafka was then open-sourced and donated to the Apache Software Foundation. Today, Kafka is the most widely-used streaming platform and can process trillions of records per day without any noticeable performance degradation as data volumes increase
Elasticsearch
Elasticsearch is an engine for search and analytics that relies on Apache Lucene. Elasticsearch is widely used for business analytics, full-text search, log analytics, operational intelligence, and security intelligence.
Machine Learning Tools
Here are some tools in the machine-learning space.
TensorFlow
TensorFlow is an open-source library created by the Google Brain team, which was released to the public in 2015. TensorFlow is used for numerical computation and large-scale machine learning and contains a wide range of machine learning and deep learning models and algorithms.
TensorFlow is used to simplify the process of building machine learning applications by providing a front-end API in Python or JavaScript while using C++ to execute the applications. TensorFlow can train and run deep neural networks for various purposes, such as natural language processing and image recognition.
PyTorch
PyTorch is a Deep Learning tensor library optimized for applications using CPUs and GPUs and CPUs. PyTorch is also used for applications like natural language processing and computer vision.
Since its inception by the Facebook AI Research (FAIR) team in 2017, PyTorch has become a popular and efficient framework for creating Deep Learning models. It is an open-source library based on Torch and designed to provide greater flexibility and increased speed for implementing deep neural networks.
Scikit-learn
Scikit-learn is an extensive Python library for machine learning projects. It offers a range of statistical, mathematical, and general-purpose algorithms that serve as the foundation for numerous machine-learning technologies.
This free tool plays a crucial role in developing various algorithms for machine learning and related technologies.
Development and Collaboration Tools
Here are some of the best development and collaboration tools.
Jupyter Notebook
JupyterLab is an interactive development and collaborative space for code, notebooks, and data accessed through the web. Its interface is adaptable, enabling users to arrange and configure workflows in scientific computing, data science, machine learning, and computational journalism. The design is modular, which allows extensions to be incorporated to expand and enhance functionality.
Git
Git is a distributed version control system that helps developers track their progress on a coding project, collaborate and learn. As a developer, it’s important to know every version of your code, who made any changes, and when those changes happened.
Ready to Learn More?
There’s never been a better time to learn about data analysis than now.
Data analysis transforms how we approach complex problems and uncover hidden insights. The rapid advancements in technology and machine learning enable us to delve deeper into the vast amounts of data surrounding us, providing a wealth of knowledge at our fingertips.
With the power of data analysis, you can uncover new trends, identify patterns, and make informed decisions that were previously out of reach. This makes it an exciting and constantly evolving field to be a part of.
Learn more about data analysis now.
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