Data science is a buzzing word nowadays. Some say that data science is such a hot career right now. But, do you know the reason behind it? Data science is a rising career in today’s digital world because most of the large organizations are running through data. As technology and development have changed the scenario of the market, we have seen that the scale of data available to human society is growing fast.
Data science has made it possible to keep an eye on space stations also. For example, the Urthecast company has recently installed the first civilian high-resolution cameras directly on the international space station to take pictures of the earth that can capture 2.5 TB (terabytes) of data a day which is quite a large amount. Data science is becoming crucial for every field and sector such as supermarkets, industries, gaming and sports, hospitals, banks, space stations, etc. But the term data science is widely used in the IT industry and in other related industries. As a result, there is a high demand for data science experts who can manage and solve Big Data issues. In fact, many edtech firms are offering Data Science Bootcamp for candidates who want to become skilled data science professionals.
This article will throw light on some of the important data science tools that can help you go ahead in your career graph.
What Is Data Science?
Before exploring the data Science tools, let us know more about data science in a few lines. Data science is considered one of the most promising and in-demand career paths for skilled professionals. Data science helps to uncover useful intelligence from a large amount of data, so the data scientists must master the full spectrum of the data science life cycle and must also possess a high level of flexibility and understanding of maximizing returns at each level of the process.
The commonly known facts of data scientists are that it is a technical process that encompasses scientific processes, statistics, and maths, advanced analytics, AI, and specialized programming. It is also better known as a multidisciplinary approach that can extract actionable insights from the large volumes of data collected and formed by several organizations. This process includes several steps such as preparing data for analysis and processing, analyzing and managing, identifying meaningful insights, and presenting the results to reveal patterns and help stakeholders to draw informed conclusions.
So data scientists require a deep knowledge of computer science and pure science skills that go beyond those of typical data science analytics skills. Data Scientists use several advanced tools to perform their tasks and responsibilities. Let us have a look at some of the important tools they use in their day-to-day operations.
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Popular Data Science Tools
We have witnessed rapid growth in the data science field that has driven a growth in data science tools development. Data Science tools can be divided into two categories:
- Self-service tools with technical expertise (understanding of statistics and computer science, programming skills, etc.)
- Tools for business users (automate commonly used analysis)
Techies use many types of data science tools such as Python, R, SQL, etc. But to become a successful data scientist, Python should be the first tool to master. Let us know about some of the important tools used in data science.
- SAS
SAS stands for Statistical Analytics System and it is one of the oldest data science tools that help to perform granular analysis of textual data. This tool can also generate insightful reports, so several data scientists prefer the visually appealing reports created by SAS. It is widely used for multiple data science activities such as retrieving data from various sources, data mining, econometrics, time series analysis, business intelligence, etc.
- Python
Python is one of the topmost tools which is widely used by many scientists for web scraping and other activities. It offers libraries like Matplotlib, SciPy, NumPy, Keras, Pandas, etc., which are specially designed explicitly for data science. Python helps data scientists to perform several statistical, mathematical, and scientific calculations.
- Apache Hadoop
It is open-source programming software that is widely used for the parallel processing of data. Apache Hadoop consists of a distributed file system responsible for dividing the data into chunks and distributing it to various nodes. It involves many kinds of components like Hadoop MapReduce, Hadoop Common, and Hadoop YARN.
- Tableau
This tool is used in data visualization that is helpful in the decision-making and data analysis process. It helps you represent data visually in less time and uncover hidden trends and patterns easily.
- PyTorch
It is an open-source tool, built in Python that is starred by 45K in Github. Many data scientists rely on PyTorch, which includes libraries for most machine learning approaches.
- RapidMiner
This tool helps data scientists and teams to be more productive through a lightning-fast platform that unifies data prep, model deployment, and machine learning. RapidMiner is a widely used tool worldwide that helps to reduce cost, drive revenue, and avoid risk. It is a platform with code-optional, guided analytics, and more than 1500 functions. Its studio is considered as the Visual Workflow Designer for Data Science Teams. Rapidminer is built on three major components that allow users to automate predefined connections, repeatable workflows, and built-in templates. Rapidminer platform makes it possible to get visibility into data science teamwork and governance.
- DataRobot
It offers a machine learning platform for data scientists of all skill levels to build and deploy accurate predictive models. The DataRobot platform uses parallel processing to evaluate and train thousands of models in Python, R, Spark MLlib, H2O, and other open-source libraries. It addresses the critical shortage of data scientists by changing the speed and economics of predictive analytics. DataRobot is specially built with the deep knowledge and experience of some of the world’s top data scientists.
Tensorflow, Alteryx, Qubole, Paxata, Trifacta, Lumen data, Apache Spark, SQL, BigML, D3.js, MATLAB, Excel, ggplot2, Tableau, Jupyter, Matplotlib, NLTK, Scikit-Learn, Weka, are a few more tools commonly used in data science to increase the productivity of data scientists and make the processes easy.
