Top 15 Data Science Tools which every Data Scientist should know

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Tools help make our tasks easier, more precise, and efficient. The more complex the task at hand, the more intricately built the devices should be. Take, for example the simplicity of a knife that cuts fruits than that of a metal cutting machine. And when it comes to the complex roles of a data scientist, having the right tools saves the data scientist from delving into tricky coding languages.

Data science tools help organise, store and process data as well as implement AI and Machine Learning tasks in an easier way. The tech tools come with predefined algorithms and functions designed to build customised machine learning models without the knowledge of programming.

Having these tools at disposal cuts your task as a data scientist and allows you to devise techniques to improve machine learning models and make better inferences.

So as an aspiring data scientist, which tools do you think you should try to get hands-on knowledge of? It depends on your field too, but you'd get a good idea with this list of the top 15 tech tools that come in handy in tasks of a data scientist in 2020.

1. Rapid Miner

Rapid Miner covers all aspects of prediction modelling. Its functionalities include data building, preparation, model building, variation, cloud depository, and deployment. Its GUI is user-friendly and helps connect the blocks quickly.

The product comes with a 30-day trial, although the server is a bit costlier than others of the forte.

2. Talend

Talend is an open-source tool that offers software solutions for data integration, preparation, and integration of the application. The means of Talend are quite affordable since it is open source. The tool is quite helpful to deploy tasks and maintain the database automatically.

3. Data Robot

Data Robot is a popular tool for automated machine learning. Data scientists, as well as other IT professionals. The platform's easy deployment is one of its USPs, along with parallel processing.

4. Amazon Redshift

Amazon Redshift is a data warehousing tool which is a part of the Amazon Web Services. The petabyte-scale data warehouse service helps its users to analyse large gigabytes of data and extract valuable insights from it. You can also use it for massive database migrations, which is a common issue you experience in data science.

5. Qubole

Qubole claims itself to be the first autonomous data platform. It is an open source and facilitates machine learning, data exploration, and streaming analytics automatically. Qubole provides user-friendly end-user tools like SQL query tools, dashboards, and more.

6. SAS

SAS stands for statistical analysis software and effectively automates user tasks along with running SQL queries using macros. SAS includes interactive dashboards and powerful visualisation that helps in data mining and predictive modelling. The only drawback with the service is that it is quite expensive, and hence data science gets access to it only in large corporates.

7. BigML

If you aren't an expert in coding and programming as a data scientist, you'd need a cloud-based GUI environment that helps process machine learning algorithms to effect in various sections of the business. It contains features like risk analysis, product innovation, and sales forecast in a single software.

Its wide variety of algorithms like clustering and time series forecasting make it a very interactive platform. BigML's web interface is user-friendly, and it also has a free account option for smaller data analysis needs. You can also export your analysis charts to mobiles and other devices and get access to interactive visualisations using it.

8. MATLAB

MATLAB is a versatile tool for data scientists providing a multi-paradigm computing environment to process mathematical information and has extensive use in scientific disciplines. The closed source platform helps in statistical modelling and algorithmic implementation of data.

MATLAB also facilitates image as well as signal processing along with data cleaning. MATLAB is also easy to integrate with enterprise tools, and its only drawback is its cost since it’s a closed source software.

9. Excel

One of the most basic tools of Data Science, Excel, should be on the fingertips of every data scientist. Microsoft Excel helps organise data in the form of a spreadsheet and can-do complex calculations at a finger.

Apart from the present features and functions, you can also add custom functions to it. If you connect Excel to SQL, you can also analyse data with it. Excel is not ideal for big data processing and calculation, but it's a straightforward tool for data organisation and calculation.

10. Tableau

Tableau is a data visualisation software that helps manual data analysis and decision making through clear and precise visuals. The platform converts raw data into understandable formats.

It has extensive use in Business Intelligence and helps devise strategies by making quick inferences and finding patterns easier through clear visuals. Tableau can interface with OLAP cubes, databases, spreadsheets, and more. It also comes with its analytics tool to observe patterns and trends for business inferences quickly.

11. Natural Language Toolkit

With the rise in Natural Language Processing, it has become possible for machines to understand human language better. Alexa, Google Now, Siri, and more are prime examples of it.

NLP deals with understanding human language through the development of statistical models. Natural Language Toolkit or NLTK is a collection of libraries that employ language processing methods like tagging, machine learning, stemming, parsing, and tokenisation. It further includes various apps like Text to Speech, Word Segmentation, and so on, helping the AI do its job well.

12. Azure HDInsight

Data storage is the first and primary responsibility of a data scientist. The free, open-source software Apache Hadoop offers a framework that can store and manage massive bytes of data without breaking a sweat.

It provides high-level computation by distributing data over a group of 1000s of computers quickly. It also offers other data processing modules like YARN and Hadoop MapReduce for integrated functionality.

13. Azure HDInsight

Azure is a popular cloud platform from Microsoft that provides a complete software solution for data processing, storage, and analysis. Azure HDInsight also integrates with Apache Hadoop for quick and smooth handling of data.

It also offers Microsoft R server that helps develop models for powerful machine learning and statistical analysis. Even enterprises like Jet and Adobe use Microsoft Azure HDInsight for the management and processing of massive amounts of data.

14. Alteryx

Alteryx is an enterprise-level platform for professional discovery, preparation, and analysis of big data. The platform allows you to discover data and deliver it to various parts of the organisation.

You can manage workflows, users, and data assets using the centrally controlled UI. You can also embed Python as well as R processes using the software tool. The full-fledged tool is quite expensive for a startup, but big enterprises are benefitting for the software that has it all.

15. Informatica Power Center

With a revenue of over $1.05bn, Informatica is a highly recognised data integration tool particularly useful for data scientists working behind business models.

It is based on the Extract Transformation Load (ETL) architecture. It offers a complete framework for data extraction, transformation, processing and deployment to the warehouse. Data Scientists can personalise the way Informatica processes the data according to the businesses.

It offers crucial support for adaptive load balancing, dynamic partitioning, pushdown optimisation, grid computing and more.

Conclusion

These are the most trending Data Science tools in 2020 tech tools for a data scientist. Of course, the list is not exhaustive as engineers and data scientists are always looking for upgrades and enhancement in the software to store, process and interpret data more accurately.