Top data science tools employers expect you to know

Data Science is always considered to have a bright future if chosen as a career due to its numerous applications around the world. It is linked with numerous other technologies such as Artificial Intelligence, Machine Learning, and the Internet of Things. Thus, referred to as multi-disciplinary technology.

We show a clear path in the direction of how to become a Data Scientist to our students.  These are the people behind the scenes of any business development analysis, environmental statistics and even in the healthcare sector. The demand for Data Scientists is keeping on rising due to the necessity in the analysis of data on the whole.

As their work includes numerous technologies, the number of tools deployed in the process is also numerous. They acquire the required data, connect the dots and figure out the statistics for future predictions. 

Some of the top-rated Data Science tools are mentioned below for your reference

SAS programming language

SAS is one of the tools used in data science to perform statistical operations. It’s usage can be witnessed by professionals for the implementation of commercial software. It is among the few closed source proprietary software out there in the market.

Besides due to the unavailability of a considerate number of libraries in the base pack, up-gradation to the next level leads puts a stress on the financial side of small and medium level companies.

Thus, it is mostly preferred by the corporates as a Data Science tool. However, for a data scientist, it is considered over other tools due to its deployment in modeling and organization of data with the help of various tools and statistical libraries available. It’s wide usage is mainly due to the appealable GUI along with the necessary technical support.

The integration of database access with the help of SQL is highly productive. It is quite difficult to build-up complicated graphical plots but its drag-and-drop interface is convenient in the creation of excelling statistical models.  

Apache Spark

Designed for the processing of batch along with stream processing is among the frequently used tool in Data Science. The presence of APIs aids the Data Scientist in getting repeated access to the data for the accomplishment of tasks in machine learning. 

It is considered to be an enhancement over Hadoop with great speed and preferred by students at the Data Science beginner level. The existence of APIs aids in making better predictions with the usage of data. Its greatest advantage is the usage of multiple programming languages such as R, Java, Python, and Scala along with the numerous libraries.  

BigML

Another well-known Data Science tool in the market is BigML. It’s usage is evident during the processing of Machine Learning algorithms as a Data Scientist is capable of getting a cloud-based GUI that is completely interactable in nature.

The companies can make use of Machine Learning algorithms such as classification, clustering and time-series forecasting due to the presence of standardized software.

The web-interface provided by BigML is another advantage that can be created either free-of-cost or a premium account as per the requirement with the help of Rest APIs. Exporting of Visual charts is possible with the help of BigML over your smartphone or IoT devices.

The availability of a wide range of automation techniques is useful for both workflow automation of the reusable scripts and tuning of the hyper-parameter models.    

D3.js

JavaScript is deployed for the scripting of language in the client-side. It is one of the JS libraries that grants the user permission to make interactive visualization on web-browser along with animated transitions.  It can be also used in creating illustrations and transitory visualization with the help of CSS for the implementation of customized graphs over web-pages. These illustrations can be learned even through our Data Science online course at your own pace. 

MATLAB

MATLAB is among the well-known closed-source software deployed for numerical computation, which aids in algorithm implementation along with statistical modeling and matrix functions.  Stimulating neural networks is a bit tough in Data Science but MATLAB has made this task to be executed with ease.

MATLAB graphics library is capable of reducing the Data Scientist's complexity of work in creating visualization, signal and image processing. Thus, its use is vital for a Data Scientist.  Moreover, designed with simple integration for the various enterprise applications has made its demand further increase in the market. This very tool can be learned well with the expert’s guidance through our Data Science offline course.

Excel

Excel has always been a great tool for Data Analysis developed by Microsoft that is deployed for all the spreadsheet calculations along with visualizing, processing, ad calculations of complex data.  This is one of its kind and is the most trusted Data Science tool by many professionals due to its easy-to-access and complex-free design.

The developer can customize his formulae and functions with the deployment of tables, slicers, and filters available in this tool. Even though it's not into huge data calculation its preference for spreadsheets and data visualizations will never diminish.

If the developer gets a chance of connecting Excel and SQL, tasks like manipulation and analyzation become more simple, which is the prime reason for Excel to be still counted as a Data Science tool.  

Ggplot2

When it comes to Data Science, there is indeed the necessity of advanced data visualization package when considering the R programming language.

As this tool came into existence as an alternative for native graphics package of R.  If the developer is in search of a tool for the creation of personalized visualizations for the enhancement of storytelling, then this tool is an excellent choice. Moreover, it does something more by allowing the user to develop different styles of maps and hexbins, cartograms, and choropleths. 

Tableau

Tableau is nothing but software related to Data Visualization that comprises of graphics for the creation of interactive visualization. The prime focus of this tool is to target the audience belonging to industries into business intelligence.

The best part of using Tableau is the competence of interfacing with spreadsheets, OLAP (Online Analytical Processing) and databases. Besides, its capability to foresee any geographical data for mapping out longitudes and latitudes. 

Jupyter

Even this is among the open-source tool that is based upon Python to aid the developers in developing the open-source software along with experience interactive computing.

It’s one of the features is supporting diversified languages such as Python, R, and Julia. It’s application can be witnessed in writing live code, presentation, and visualization. Moreover, the feature of the online platform of Jupyter, known as Collaboratory that is executed over the cloud along with the storage of data in the Google drive.  

Matplotlib

It was primarily developed for visualization and plotting library. And now, it is considered to be a well-known tool for the generation of graphs along with the data analyzation. It’s usage is mainly observed in the plotting of various complex graphs along with the generation of bar plots and scatterplots.

There are many essential modules and the widely used among them is pyplot, which is open-source back-up to the MATLAB’s graphic modules.

NLTK

NLTK refers to Natural Language Toolkit, which is nothing but the collection of libraries present in Python language. Natural Language Processing handles with statistical models that are part of Machine Learning to aid the computers to understand human language.

It’s usage can be observed in numerous techniques namely stemming, machine learning, tokenization, parsing, and tagging. There are around 100 corpora that are a compilation of data to build models related to machine learning.

This language is necessary to establish communication between computers and humans, as there is no possibility of the computer to understand our requirements. However, it is not the same after the tremendous evolution in technology.

Some of the most familiar examples are Siri, Alexa that is developed to minimize the gap between human and computer. Thus, prevailing as the best Data Science tool in the market. 

Scikit-learn

It is mainly deployed for the operation of Machine learning algorithms due to its simple and easy-to-implement features that are necessary for data analysis in Data Science. This tool acts as backing up characteristic namely preprocessing of data, reduction in dimension, clustering, classification. Hence, its importance as a Data Science tool has not been diminishing.

There are numerous libraries associated with this tool such as Matplotlib, SciPy, Numpy and many more.

TensorFlow

It is one of the standard tools implemented in Machine Learning. Its name has been kept after Tensors that have arrays with multidimensional. This tool is well known for the high computational capacity along with performance.

Its unprecedented edge to processing and capability of being executed over both CPU and GPU has led to its wide utility. Moreover, the enormous processing capacity of this tool has led to its deployment in many applications for instance drug discovery, speech recognition, image generation, image classification, and language recognition.  

Weka

Waikato Environment for Knowledge Analysis popularly known as Weka is just software that operates over machine learning technology is written over Java. Its usage can be witnessed in data mining by the accumulation of various machine learning algorithms.

This tool comprises of numerous tools associated with machine learning such as clustering, preparation of data, regression, classification, visualization, and clustering.  

Weka is one among the GUI software, which makes the process of implementation of various algorithms related to machine learning very easy. There is no need for a developer to write any coding to understand the functioning of Machine learning. It will be beneficial for a Data Scientist who has just entered the industry.

RapidMiner

The deployment of tools in any technology is done for the ease of completing the tasks. RapidMiner is no different by increasing the data science team’s productivity on the whole with a quick platform, which unites machine learning with various others namely data preparation and model deployment.

RapidMiner Studio is considered as the Visual Workflow Designer for the entire Data Science team. There are around 1500 functions in this that aids the users in the automation of the predefined connections, repeatable workflows, and built-in templates. The Data Scientist can get the needed visibility with the help of RapidMiner. It is used in various industries along with various types of solutions.   

Conclusion It is indeed a known factor that as a Data Scientist the person needs to be well versed with numerous tools. As they need to deal with a humongous amount of data the usage of various tools are very necessary for the creation of aesthetic and interactive visualizations with powerful predictive models.