Data science has become a buzzword in the rapidly growing world of technology. Almost every organization and business is coming to recognize its benefits and adopt its methodologies. But with the rise in the popularity of technology, comes the rise in demand for professionals.
Data scientists are one of the most sought-after professionals in today’s job market. If you are someone who aspires to become a data science professional, as a first step make sure to enroll in some Data Science course offered by prestigious institutes where you will end up having immense knowledge on all concepts, also you become much familiar with all tools mentioned below.
Alt-text: 15 Most Popular Data Science Tools one should know in 2022
To become an expert in any field, it is vital to understand its methodologies and know how to use its corresponding tools.
In this blog, we’ll look into the widely used data science tools that one should be aware of to be a successful data scientist.
Points to be Covered
- Who is a Data Scientist?
- Popular Data Science Tools
Who is a Data Scientist?
A data scientist is a senior most role in a project team. Data scientists are responsible for handling large amounts of structured as well as unstructured data and then creating data-driven operational models based on it.
Another task of a data scientist is connecting stakeholders and customers.
In data science, modern tools and methods are used to analyze enormous amounts of data to uncover hidden patterns, acquire crucial information, and make business decisions.
A qualified data scientist should be able to process, model, and analyze data before interpreting the results to create practical plans for organizations.
Now that we know who a data scientist is, it’s time to look into the tools required by them to complete their tasks.
Popular Data Science Tools
Here we’ll use some of the most popular and widely used tools that Data Scientists should know in 2022.
1. Apache Hadoop
- Apache Hadoop is open-source software, written in Java language.
- If a user cannot decide what to do with some data, Hadoop provides them with a low-cost feature wherein they can store the said data, even if it is in an unstructured form.
- Apache Hadoop supports parallel data processing.
Matplotlib was introduced in 2003 by John D. Hunter.
The feature named Pyplot in Matplotlib makes it easy for developers to do plotting as it provides them more control over line and font styles.
Although its main focus is creating 2D visualizations, it also consists of a 3D toolkit as an add-on feature.
- Data from database files, online databases, SAS tables, and Microsoft Excel tables can all be easily accessed using this program.
- Developers make use of its statistical libraries to deduce data-driven business insights and models from the data.
- SAS offers advanced features and visualization techniques to users so that they can build effective reports and dashboards.
4. Apache Spark
- It is open source and can manage large amounts of data.
- It is known to have much faster cluster computing as compared to other tools in the market.
- It is compatible with Cassandra, S3, HBase, and HDFS.
5. Google Analytics
- Google Analytics is a powerful tool as it delivers a thorough analysis of the performance of a business website or app for data-driven insights.
- It is a service offered by Google to track website traffic and was introduced in November 2005.
- It is extremely easy to use and anyone can make use of its features, even with no prior technical knowledge.
- KNIME is an open-source data science tool that is used for quickly extracting and transforming data.
- You can deploy data science workflows as analytical applications and services using the commercial platform known as the KNIME Server.
- Tensorflow supports visualization features.
- Developers use Tensorflow with Python to gain meaningful business insights from the extracted data.
- TensorFlow assists in building models for a variety of tasks, such as partial differential equation simulations, image recognition, handwriting analysis, and natural language processing.
Tableau was introduced in 2003 to perform complex data analysis and solve visualization problems.
It can be used to create charts, graphs, and other interactive visual models. In addition, Tableau’s drag-and-drop feature makes it very easy to use.
Rapidminer is another great data science tool that is used in prediction modeling.
It helps developers to conduct data preparation, analysis, and statistical modeling. It can also be used to build custom workflows.
DataRobot is a great tool to increase team productivity by integrating artificial intelligence and machine learning algorithms.
DataRobot supports parallel processing.
It is an easy-to-use tool as it has a great interface and allows drag and drop functionalities.
11. Sci Kit Learn
- Scikit Learn is an open-source machine-learning library.
- It supports a wide range of supervised and unsupervised machine learning algorithms.
- It also helps in the Normalization and feature extraction of data.
- NLTK or Natural Language Toolkit is another wonderful tool used by data scientists. It is a Python-based toolkit that can be used for a lot of tasks such as tokenization, classification, visualizing words, and parsing.
- NLTK’s most important feature is that it supports the largest number of languages as compared to other Python libraries.
13. Microsoft Power BI
- Microsoft Power BI is considered one of the most powerful data science tools used today.
- It is an interactive data visualization tool that offers amazing features to users for creating visually appealing dashboards and reports.
- Matlab is a closed-source data science tool that helps in signal processing, analyzing algorithmic performance, and performing statistical modeling.
- It supports high-level language and offers an interactive environment for developers.
- Weka is another open-source software used by data scientists mainly for performing data mining.
- It is a machine-learning tool used for data preprocessing, classification, and visualization of data.
We hope that this blog has provided you with a clear understanding of all the widely used data science tools that experts use to cut down on work delays.