• Introduction
  • Top Trending Tools for Data Scientists
  • SAS
  • Apache Spark
  • BigML
  • Excel
  • Jupyter
  • Takeaway


Data Science is the science of extracting knowledge or insights from data in various forms – structured or unstructured, static or streaming, to make better business decisions.

It has gained a lot of traction over the last few years in every industry when businesses realised that they can analyse and predict trends through an in-depth analysis of data. Today, most companies are pouring big bucks into data science training, hiring employees with data analysis capabilities, and investing in tools to streamline processes.

Data Scientists need to be equipped with the right combination of skills, knowledge and most importantly the right set of tools for any kind of task– ranging from analysing unstructured text data to visualising analytics insights.
This blog is an attempt to collate the tools that are trending in Data Science today.

PG Program in Data Science


Top Trending Tools for Data Scientists:

SAS as a tool for Data Scientists is already established and accepted as the de facto tool for number crunching. SAS developers understand better than anyone else that data is stored in a variety of sources, from relational database management systems to text files and geospatial databases. This makes it easier to work with data across all industries.

Data Scientists often use SAS Enterprise Guide which is an advanced analytics tool for big data processing. SAS Enterprise Guide is very good at extracting complex business rules to generate quality reports and visualisations.
You can develop interactive reports in R language using SAS Analytics Visualisation Toolkit (SAS AVT). It offers hundreds of templates to create charts and graphs based on your business insights. You can connect R with SAS Enterprise Guide for data processing, cleaning, and importing them into the reporting tool.

SAS Visual Data Exploration (SAS ViDE) is an interactive analytics software that helps you to view live data and explore it without any programming language. It allows you to create and share dashboards with your clients, colleagues, or team members.

SAS Visual Analytics uses the R language for data processing by employing a rich collection of interactive graphical models including maps, treemaps, streaming charts, and graphs. It also allows users to publish dashboards on the web using a single click in order to improve collaboration among colleagues and even clients.
SAS cannot be compared with some of the latest tools which are open source. Moreover, some libraries in SAS that come as part of the base package can require an expensive upgrade.

Apache Spark
Apache Spark is a framework that is used for fast and large-scale data processing. Spark is not a tool that has been built from scratch. It is actually based on Hadoop’s MapReduce, with some additional modules thrown in.
Apache Spark has become one of the most popular tools for several reasons: It provides a powerful engine to handle data analytics tasks, with a more intuitive syntax than Hadoop.

Apache Spark performs faster than Hadoop, and it also uses less memory. It supports integration with several other tools used by data scientists—not just Python and R but also Scala and Java. However, this is not a tool that can be used to handle streaming data processing like Twitter feeds or data coming in from sensors.

BigML is another tool for machine learning and predictive analytics, based on Apache Hadoop. It is now owned by Apple, but Apache Spark users can download it from the BigML site.

BigML has an easy-to-use point-and-click interface that lets data scientists build their own models without having to write a single line of code (though they can if they prefer). It also has a server-based installation option and support for both Scala and Python. However, the company is still working to make BigML Python-friendly.
BigML offers an advanced cloud-based data management software that supports a wide range of different divisions at your company. The BigML has advanced analytics and decision tools that can support any department in your organisation with cloud computing capabilities.

For example, using BigML, you can carry out predictive modelling across the following areas- to predict sales, product creation, and risk forecasting. These practical Machine Learning calculations such as grouping, characterisation and time-arrangement anticipation are all utilised by BigML to supply associations in between data points.

MATLAB is used for numbers, statistics, and plotting. It is also used in applications that require a 3D graphic display of data. There are a variety of categories within MATLAB including Image Processing, Curve-Fitting, DSP and Data Processing.

MATLAB is also widely used in the scientific community as it has strong interfaces with other languages such as C++, Java, and Fortran.

Matlab is used to develop mathematical algorithms and models for research in physics, engineering, computer science, signal processing, and many other fields. It is a high-level language that can be used as an interactive environment with graphics like other interpreted languages or compiled to run on a target hardware device. The syntax of the language is close to symbolic mathematics and provides efficient, high-level commands for matrix manipulations such as multiplication, rather than using loops. MATLAB is also a numerical computing environment including visualisation.

An important tool for Data Scientists is MATLAB. The software can be used to implement neural networks with its drawing library and also handles different types of datasets.

Presumably, Excel is the most broadly used Data Analysis Tool in the world, but it is mostly used for small data sets. It is feature-rich software that includes hints for statistical analysis methods and also handles different types of datasets well.

Microsoft Excel is used as a data scientist’s tool for data preparation, representation, and complex calculation. While it was created with estimations in mind, excel comes in handy when dealing with data science matters.
Excel accompanies different formulas, tables, channels, and so forth. It can likewise be utilised to make custom formulae for you or your organisation. Data scientists frequently use Excel to clean data, because it offers an updated GUI.

Jupyter is one of the most well-known data science tools for developers and analysts. It is a framework that helps you in sharing and discussing code, technologies, and ideas on various topics like machine learning and deep learning.

Jupyter Notebook can also be used as a computational research tool such as SAS Data Science Studio or R Studio. One of the best features of Jupyter Notebook is that it allows you to run your code on the notebook itself, which means you can execute Python or R code in an interactive way.

Jupyter with its advanced and robust features is truly one of the best data science tools for beginners and experts alike.

There is an online Jupyter climate called Collaboratory, which allows you to share your code online with anyone. That means you can send a link to your code and ask for others to review it, check their own versions of the code in order to collaborate on it, or simply discuss some issues related to installing or running it.
Jupyter Notebook is open-source software that runs on several platforms, including Windows 10 (with or without internet connection), Mac OS X, and Linux. It also runs on the cloud and stores data in Google Drive.
Another famous tool for data science is called RStudio. Basically, it’s a user-friendly IDE with complete support for R language to run on your computer.

Data science needs many tools to complete a task. Many technologies have been introduced through different points of time and now it’s super hard to choose one single tool because data science is becoming more and more complex every day.

We can’t avoid the fact that we need different tools with various features according to our needs. Many data science tools can be used to execute complicated data science tasks. This means that the user does not have to write their code from scratch and can just input their commands into one program. Different versions of the same tool fulfil specific needs in different fields, such as bioinformatics or environmental analysis.
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