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Monday, April 15, 2024

Top Data Science Tools to Consider Using in 2021

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The present age of Big Data is being dominated by data analytical technologies like Data Science. Machine Learning, Artificial Intelligence, and Deep Learning. As every enterprise generates data as a part of its day-to-day operations. It is the responsibility of a Data Scientist to mine, interpret, manipulate, pre-process. Extract actionable insights from this data. Present them in the form of attractive visuals. To perform these tasks, Data Scientists require to use advanced statistical tools and programming languages.

Now, let’s explore the top Data Science tools to consider using in 2021.

  • SAS

When it comes to performing statistical operations on Big Data. Most Data Scientists rely on SAS. Being closed source proprietary software SAS makes use of base SAS programming language to execute the function of statistical modeling. It is the most reliable commercial software tool use by large organizations that deal with enormous levels of Big Data. The best part about using this tool is that it supports. Data Scientists with a number of statistical libraries. Data frames for modeling and organizing their data.

The only limitation of this tool is that being a clos source proprietary. it is highly expensive and so it is mostly use by larger organizations.
  • Apache Spark

When it comes to handling Batch Processing. Stream Processing techniques Data Scientists usually prefer using Apache Spark. Being a very powerful analytics tool. It supports Data Scientists with many APIs to make accurate predictions out of Big Data using Machine Learning algorithms.

Spark supports cluster management better than Hadoop. Also has a number of APIs that are programmable in Python, Java, and R. Sparks supports swift data processing. Executes 100 times faster than MapReduce.

  • BigML

When it comes to processing Machine Learning Algorithms. Data Scientists prefer using BigML. The best part of this tool is that it comes fully interactable. Cloud-based GUI environment where Machine Learning algorithms can smooth processes. It also has standardized software that can easily integrate with cloud solutions.

  • Python Programming

When it comes to tools for Programming, most Data Scientists rely on the use of Python over any other programming language. Python is a high-level object-oriented programming language with dynamic semantics.

Python has many libraries like NumPy, Pandas. Matplotlib assists Data Scientists in performing operations on Big Data like Data Mining, Data Cleaning, Data Analysis, & Data Visualization. List of the prominent libraries in Python for Data Science include

Python also has libraries that make Machine Learning tasks easier.

NumPy: This library in Python supports the execution of mathematical tasks on large, multidimensional arrays and matrices.

Pandas: Data Scientists make use of Pandas library to easily manipulate tabular data for data cleaning and data analysis.

Matplotlib: This library in Python supports Data Scientists in visually depicting the insights from Big Data i.e. Data Visualization

Scipy: This library is used for scientific computing and it also supports Data Scientists in executing statistical tasks.


By using this library Data Scientists can easily scrape data from websites. It also helps in configuring HTTP requests.

Having a simple coding syntax. Learning Python doesn’t involve many difficulties. So this language has become the choice of preference for Data Scientists. Having many tools and packages. And being backed by an active community base among the other reasons for the popularity of Python among Data Scientists.  You can learn Python with Data Science at Kelly Technologies Data Science Training in Hyderabad program.

  • R Programming

Next to Python, R is the most preferred tool for programming by Data Scientists. When it comes to executing Data Wrangling. Data Scientists usually prefer the R language as it has many packages that support this operation.  It also supports performing statistical analysis on Big Data.

R also has a number of packages that support Data Visualization operations, for instance. Data Scientists make use of the ggplot2 package in R to create attractive visuals.  R makes Data Visualizations a lot easier. R makes it easy for Data Scientists to depict complex information in the form of simple visuals. In this way, it becomes easy for them to convey their findings to the stakeholders. Naturally, data embed in visualization can understand more clearly. This is exactly what R does.

R also has a number of packages that support the execution of Machine Learning operations. R use for various Machine Learning operations like building random forests or performing regression and classification.
  • . Tableau

When it comes to performing Data Visualization. Most Data Scientists rely on Tableau software as it is fully equipped with powerful graphics.  Tableau helps Data Scientists to create interactive visualizations. Most of the Data Scientists who are working in the Business Intelligence industry would prefer to use it. Tableau for their data analytical operations.

The best part about Tableau is its ability to interface with databases. Also, using Tableau Data Scientists can easily visualize geographical data.

  • TensorFlow

When it comes to handling Machine Learning tasks. Most Data Scientists rely on TensorFlow. This open-source tool name after Tensors which are multidimensional arrays. This tool has very high computational abilities. Back by an active user community base. This is currently the most powerful tool in the TPU platforms.

Tensor Flow is also preferred when it comes to handling advanced Machine Learning algorithms.

How to Develop Skills in these tools?

To develop hands-on deep technical expertise in handling these tools. One should work on multiple real-time projects and assignments. There are a number of Data Science training programs that let you work on live projects under the guidance of industry experts. The Data Science Course program by Kelly Technologies is the best choice in this regard. As a part of this course. Participants will get to work on five different capstone projects. More than ten assignments. Will help them in gaining much-need hands-on practical exposure.

Summary: Data science is an interdisciplinary field that makes use of scientific methods, processes, algorithms. Tools to extract actionable insights from Big Data. In this post. Let’s look at the list of the Top Data Science Tools to Consider Using in 2021


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