24.02.2021 - 2 monts ago
Learn About Different Tools Used in Data Science

Data Science is a very broad spectrum and all its domains need data handling in unique way which get many analysts and data scientists into confusion. If you want to be pro-active in finding the solution to these issues, then you must be quick in making decision in choosing the right tools for your business as it will have a long-term impact.

This article will help you have a clear idea while choosing the best tool as per your requirements.

Let’s start with the tools which helps in reporting and doing all types of analysis of data analytic and getting over to dashboarding. Some of the most common tools used in reporting and business intelligence (BI) are as follows:

 - Excel: In this you get wide range of options which includes Pivot table and charts, with which you can do the analysis more quickly and easily.

 - Tableau: This is one of the most popular visualization tools which is also capable of handling large amounts of data. This tool provides an easy way to calculate functions and parameters, along-with a very neat way to present it in a story interface.

- PowerBI: Microsoft offers this tool in its Business Intelligence (BI) Space, which helps in integrations of Microsoft technologies.

- QlikView: This is also a very popular tool because it’s easy to learn and is also a very intuitive tool. With this, one can integrate and merge, search, visualize and analyse all the sources of data very easily.

- Microstrategy: This BI tool also supports dashboards, key data analytics tasks like other tools and automated distributions as well.

Apart from all these tools, there is one more which you cannot exclude from this tool’s list, and that tool is

- Google Analytics: With google analytics, you can easily track all your digital efforts and what role they are playing. This will help in improvising your strategy.

Now let’s get to the part where most of the data scientists deal with. The following predictive analytics and machine learning tools will help you solve forecasting, statistical modelling, neural networks and deep learning.

- R: It is very commonly used language in data science. You can access its libraries and packages as they are easily available. R has also a very strong community which will you if you got with something.

- Python: This is also one of the most common language for data science, or you can also say that this is one the most used language for data science. It is an open-source language which makes it favourite among data scientists. It has gained a good place because of its ease and flexibility.

- Spark: After becoming open source, it has become one of the largest communities in the world of data. It holds its place in data analytics as it offers features of flexibility, computational power, speed, etc.

- Julia: This is a new and emerging language which is very similar to Python along-with some extra features.

- Jupyter Notebooks: This is an open-source web application widely used in Python for coding. It is mainly used in Python, but it also supports R, Julia etc.

Apart from all these widely used tools, there are some other tools of the same category that are recognized as industry leaders.

-          SAS

-          SPSS

-          MATLAB

Now let’s discuss about the data science tools for Big Data. But to truly understand the basic principles of big data, we will categorize the tools by 3 V’s of big data:

·         Volume

·         Variety

·         Velocity

Firstly, let’s list the tools as per the volume of the data.

Following tools are used if data range from 1GB to 10GB approx.:

- Microsoft Excel: Excel is most popular tool for handling data, but which are in small amounts. It has limitations of handling up to 16,380 columns at a time. This is not a good choice when you have big data in hand to deal with.

- Microsoft Access: This is also another tool from Microsoft in which you handle databases up to 2 Gb, but beyond that it will not be able to handle.

- SQL: It has been the primary database solution from last few decades. It is a good option and is most popular data management system but, it still has some drawbacks and become difficult to handle when database continues to grow.

 - Hadoop: If your data accounts for more than 10Gb then Hadoop is the tool for you. It is an open-source framework that manages data processing for big data. It will help you build a machine learning project from starting.

- Hive: It has a SQL-like interface built on Hadoop. It helps in query the data which has been stored in various databases.

Secondly, let’s discuss about the tools for handling Variety

In Variety, different types of data are considered. In all, data are categorized as Structured and Unstructured data.

Structured data are those with specified field names like the employee details of a company or a school database or the bank account details.

Unstructured data are those type of data which do not follow any trend or pattern. They are not stored in a structured format. For example, the customer feedbacks, image feed, video fee, emails etc.

It becomes really a difficult task while handling these types of data. Two most common databases used in managing these data are SQL and NoSQL.

SQL has been a dominant market leader from a long time. But with the emergence of NoSQL, it has gained a lot of attention and many users have started adopting NoSQL because of its ability to scale and handle dynamic data.

Thirdly, there are tools for handling velocity.

It basically means the velocity at which the data is captured. Data could be both real time and non-real time.

A lot of major businesses are based on real-time data. For example, Stock trading, CCTV surveillance, GPS etc.

Other options include the sensors which are used in cars. Many tech companies have launched the self-driven cars and there are many high-tech prototypes in cue to be launched. Now these sensors need to be in real-time and very quick to dynamically collect and process data. The data could be regarding the lane, it could be regarding the GPS location, it could be regarding the distance from other vehicles, etc. All these data need to be collected and processed at the same time.

So, for these types of data following tools are helping in managing them:

- Apache Kafka: This is an open-source tool by Apache and is quick. One good feature of this tool is that this is fault-tolerant because of which this is used in production in many organisations.

- Apache Storm: This is another tool from Apache which can used with most of the programming language. It is considered very fast and good option for high data velocity as it can process up to 1 Million tuples/second.

- Apache Flink: This tool from Apache is also used to process real-time data. Some of its advantages are fault-tolerance, high performance and memory management.

Amazon Kinesis: This tool from Amazon is a very powerful option for organizations which provides a lot of options, but it comes with a cost.

We have discussed about almost all the popular tools available in the market. But it’s always advisable to contact some data science consulting services to better understand the requirements and which tool will be best suitable for you.

Look for the best data science consulting company which would best suit in your requirements list.

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