Data Science is the theory and practices that power the data-driven transformations. In today’s competitive world, data has become essential, and it’s a complete process of capturing, planning, and analyzing extensive sets of data. This process requires very high-performance analytics.

Data science tools are in full demand for skilled data scientists and continue to be sky-high. Businesses in all enterprises are capitalizing on the vast increase in data. This makes it a vast landscape for anyone looking for a well-paid career in an exciting and innovative field.

So, let’s deep dive into the top 20 Data Science tools you need to learn in 2020:
  1. DataRobot
  2. MLBase
  3. Azure ML Portal
  4. Tableau
  5. TensorFlow
  6. Apache Spark
  7. Excel
  8. MLFlow
  9. SAP HANA
  10. MongoDB
  11. Python
  12. R
  13. SAS
  14. STATA
  15. RapidMiner
  16. Trifacta
  17. Google Sheet
  18. NLTK
  19. Jupyter
  20. ggplot2.SparkR

1. DataRobot

 It is a global automated Machine Learning platform.

Features: 

  • It has the capabilities of Data Science, Machine Learning, Statistical Modeling, Artificial Intelligence, Augmented Analytics, Machine Learning Operations (MLOps), Time Series Modeling.

2. MLBase

It is one of the best Data Science tools and provides distributed and statistical techniques that are key to transforming big data into actionable knowledge.

Features:

  • MLbase provides functionality to end-users for a wide variety of standard machine learning tasks such as classification, regression, collaborative filtering, and more general exploratory data analysis techniques such as dimensionality reduction, feature selection, and data visualization methods.

3. Azure ML Portal

This is one of the fully trained cloud-service Data Science tools used to teach, deploy, and execute machine learning models at scale.

Features:

  • Azure ML Visual Studio Code extension makes it easy to traverse and reconstruct, develop, Train, deploy models using Python, and CLI at cloud scale.
  • It includes features that automate the model generation and tuning with ease, provide the best performance, and efficiency.

4. Tableau

 It is a Data Science visualization software with powerful graphics to make interactive visualizations.

Features:

  • It can interface with databases, spreadsheets, OLAP (Online Analytical Processing) cubes.
  • It provides the capability of visualizing the geographical data and for plotting longitudes and latitudes in maps.

5. TensorFlow

This is an ML tool, which is widely used for advanced Machine Learning algorithms like Deep Learning.

Features:

  • It is an open-source and ever-evolving toolkit which is known for its performance and high computational abilities.
  • TensorFlow can run on both CPUs and GPUs and has recently emerged on more powerful TPU platforms.

6. Apache Spark

Apache Spark is a powerful Analytics engine.

Features:

  • It handles batch processing and Stream Processing.
  • It provides plenty of APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL.
  • It has ML APIs that can help Data Scientists to make robust predictions with faster processes.

7. Excel

It is a Data Analysis tool from Microsoft used for spreadsheet calculations.

Features: 

  • Excel has the capabilities of Data processing, visualization, and complex calculations. 
  • It has the capability of connectivity with SQL SSAS cubes and dimensions.
  • It has the features of Data cleaning and transformation with the GUI environment.

8. MLFlow

It is one of the open-source Data Science software tools that manages the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. 

Features:

  • MLFlow is designed to work with any ML library, algorithm, deployment tool, or language. 
  • REST APIs and simple data formats that can be consumed from a variety of tools.
  • MLflow’s open format makes it very easy to share workflow steps models across organizations, if you wish to open source your code.

9. SAP HANA

It is an effective tool from SAP with SAP HANA Predictive Analysis Library (PAL).

Feature: It has plenty of libraries for use.

10. MongoDB

This is  another Data Analysis tool which is quite popular since it allows cross-platform document orientation.

Features:

  • It has a basic query and aggregation framework, but to do more advanced analytics.
  • It is a perfect choice to iterate ML training experiments. 
  • It provides the capabilities of the graph, streaming, and SQL APIs.

11. Python

It is a Data Science tool for scripting in Machine Learning, Data analysis, and Advanced Data Visualization and Analytics. 

Features: 

  • We can create experiments and models in Azure ML, and it has different Python libraries that we can use.

12. R

A Data Science programming script that is useful for statistical analysis, graphics representation, and reporting. 

Features:

  • We can create and use experiments and models in Azure ML with R libraries.
  • It provides an effective data handling and storage facility.

13. SAS

It is another quite common data science tool that is mainly designed for statistical operations. 

Features:

  • It has its own specific SAS programming language to perform statistical modeling with statistical libraries and tools that we can use for modeling and organizing their data.

14. STATA

This is a statistical tool used in Data Science for graphical visualizations of data.

Features:

  • It is an integrated software package that provides data science activities such as data manipulation, visualization, statistics, and automated reporting.
  • It provides faster accuracy and reliability solutions.

15. RapidMiner

It is one of the few data mining tools used for Data Science that is free of cost. It provides an integrated environment for Data Preparation, Machine Learning, Deep Learning, Text Mining, and Predictive Analytics.

Features:

  • It has the capability of taking integration Data from different sources: file, database, web, and cloud services.
  • It provides the intelligence of GUI or batch processing, load balancer.

16. Trifacta

It is a Data Science tool with data cleaning capabilities and preparation, with a modern platform for Cloud data lakes and warehouses.

Features:

  • It can get data insights faster.
  • Trifacta service Integrated with Azure and can be consumed.

17. Google Sheet

It has predefined templates, and can be used for creating interactive dashboards.

Feature:

  • It’s easy to make a web API from a Google Sheet.

18. NLTK

Natural Language Processing Toolkit is a collection of libraries and programs that comes in very handy if you use Python. 

Feature:

  • It deals with the development of statistical models that help computers understand human language.

19. Jupyter

It is open-source, and it can transform and visualize the data.

Feature:

  • It supports various data science languages like Julia, Python, and R.

20. ggplot2.SparkR

It is an advanced data visualization package for the R programming language.

    Features:

  • It is a Graphics package of R, and it uses powerful commands to create interactive visualizations.
  • It is integrated into Azure machine learning and can be used in R and Python libraries and Spark.

Conclusion

With these top Data Science tools, we can use the statistical techniques, examine and visualize insights from the data, and communicate the company’s results.The purpose of Data Scientists is to extract, preprocess, and analyze data. By this, companies can make more reliable decisions. Various companies have their requirements and use data accordingly; they can adopt relevant strategies and customize themselves for enhanced customer experience. To learn more about other Data Science tools, take a look at our Postgraduate Diploma in Data Science, an in-person classroom program with guaranteed placement and hands-on learning experience.

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