Enterprise data can be more and more challenging as it plays a crucial role in decision-making and strategic planning. Nowadays industries are making such strategic decisions based on past trends, consumer habits, competitor behaviors, popular products, etc. To extract and analyze data from such complex datasets, companies are relying on data scientists.  

The scenario of these tools has been remarkable in all sectors; hence, as a data science professional, it is important to learn the top 10 tools that can give you a competitive edge. In this blog, we will discuss not only the top ten tools but also the importance of learning these tools to give you a bigger picture.

Why do You Need to Learn Data Science Tools?

As this quote goes:

“A data scientist is someone who can obtain, scrub, explore, model, and interpret data, blending hacking, statistics, and machine learning. Data scientists not only are adept at working with data but appreciate data itself as a first-class product.”

By Hillary Mason, founder, of Fast Forward Labs.

Data Science tools are widely used for data extraction, processing, analysis, and visualization to solve business-related issues. Many data science professionals often find it difficult to provide solutions for business problems due to a lack of knowledge of the right tools. Hence, understanding the tools is crucial to becoming a successful data scientist in today’s business.

Top 10 Data Science Tools

1.      Tableau

This is a popular data visualization tool used by businesses to create interactive and shareable dashboards and study past data to build an analysis. The interface is beginner friendly hence, you can use it even if you do not have much experience with data visualization. One of the major advantages of using this tool is that it supports complex visualization that integrates seamlessly with data sources such as cloud databases, big data platforms, and spreadsheets.

2.      SAS

This is also known as the Statistical Analysis System, which is often used for business intelligence, predictive analytics, and advanced analytics. SAS is usually used by businesses operating in healthcare and finance because of its diverse statistical analytical abilities. With this tool, you can perform complex statistical analysis due to the pre-built functions in machine learning, data manipulation, and data visualization.

3.      BigML

This machine learning platform simplifies predictive models with a user-friendly interface. BigML supports different machine learning tasks like clustering, regression, classification, etc. Their cloud-based GUI environment aims to reduce platform dependencies to process machine learning algorithms. You can avail of all these benefits just by creating a free account on their site; however, you can purchase their premium account as well (depending on your business needs).

4.      Knime

This is an open-source platform that can be operated with minimal programming expertise. It has an easy-to-use Graphical User Interface or GUI that is used to perform data extraction, analysis, or even transformation. It allows users to create interactive views with visual data pipelines. Knime’s extensive library includes pre-built nodes that help both technical and non-technical users conduct complex analytics. 

5.      RapidMiner

This popular data science tool is known for its secure and scalable environment to perform data preparation. You can design any data science or machine learning model with the help of RapidMiner. Overall, this tool supports the whole data science lifecycle starting from data preparation to model deployment. The drag-and-drop interface makes it easy for beginners to work on analytics without requiring much technical knowledge. Today, this tool is gaining popularity for its ability to manage large datasets effectively.

6.      Excel

This is perhaps the oldest and most important tool in data science particularly for statistical analysis. Excel can be used by professionals from the ones just starting their data science career to the CEO. Its diverse facilities let you apply formulas, various functions and key combinations to make better data representation. Here, you can create pivot tables to summarize data in a tabular format.

7.      Apache Hadoop

The framework for this tool is designed for processing and storing big datasets. With the help of this tool, you can connect with various data sources and create dashboards to visualize the reports and share them with the stakeholders. It allows a diverse range of attractive visualizations to represent your data better that can even be understood by non-technical professionals.  

8.      PowerBI

Similar to Tableau and Apache Hadoop, this tool is widely used by data science professionals to visually represent data. Offered by Microsoft Corporation, this tool allows users to generate insightful reports with the personal data analytics dashboard from the dataset. One of the advantages of this tool is its fault tolerance during unfavorable circumstances and easy integration with other data processing tools like Hadoop YARN, Hadoop MapReduce, etc.

9.      MongoDB

This is a NoSQL database that is used for managing unstructured and semi-structured data. SAP HANA is its relational database management system that simplifies the process of data storage and retrieval. One of the key features of this tool is its ability to execute both data scalability as well as advanced analytics.

10.  DataRobot

It is an automated machine learning platform designed to build and deploy predictive models. It gained popularity in modern business due to its automation capabilities in reducing time and effort with tasks like model selection, hyperparameter tuning, and feature engineering.

How to Master These Data Science Tools?

Learning these data science tools can be quite challenging if you are not clear about the concepts and frameworks. Therefore, if you are aiming to leverage the use of these tools, it is recommended to opt for accredited certifications or programs that let you work on real-time projects to gain industry exposure. Pursue data science courses from top universities or institutions like Harvard University, United States Data Science Institute, Georgetown University, etc. to gain a competitive edge.