5 Easy Methods to Visualize Data in Python Better

5 Easy Methods to Visualize Data in Python Better

Apart from gathering and analysing the data, a data scientist has to present the result of the analysis in a form that is understandable and discernible. Visualizing the data with clear representations and in a neat and concise presentation is essential to drive home the conclusion of the analysis.

As much as the analysis is important, taking time to present the final takeaway points to the appropriate persons is also equally important. A data scientist may need to explain the result of the analysis with the proof to some non-technical bosses or clients and the best way to help them understand is with data visualizations.

As Python is one of the preferred languages for machine learning algorithms, there is a lot of analysis that needs to be represented and delivered in an understandable form. Python has made this easier by having its own libraries, especially for data visualization – matplotlib and seaborn. 

These libraries offer 2D and 3D visualization graphics to create quality representations of data sets with complete options of customization like themes, colours, filters, palettes and various other tools to visualize functions and other complex mathematical computations in its simplest forms.

There are generally five types of data visualization techniques and all ...


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How Business Data Supply Services Have Changed Post GDPR

How Business Data Supply Services Have Changed Post GDPR

The morning of 25th May 2018 brought a new dawn in the digital era when the General Data Protection Regulation, popularly termed as GDPR, came into practice. As a strict regulation for private companies that sell or store personal information, the announcement came as an assurance to the citizens of EU and EEA who were apprehensive about the security of their sensitive data. 

It stands to reason that GDPR places seemingly simplistic details like name, photo, email address, bank details, social networking updates, location details, medical information, and computer IP address into its list of personal data. Each of these pieces of information about an individual can pose serious threats related to Identity theft and fraud. However, it may be questioned as to why private companies are the targeted audience of the regulation. 

To answer that query, the regulation justifies that personal data of any person is equally valuable in the public, private, or workspace. And while its protection is thoroughly observed in public spheres, the vulnerability of digital systems in the private sector is its huge setback. Here is a list of changes that a company needs to incorporate to become a GDPR compliant firm:

1. Thorough Filtration of Company’s Data

Since GDPR ...


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5 Easy Breezy Ways to Master Python!

5 Easy Breezy Ways to Master Python!

Python is touted as one of the fastest-growing major programming languages in the world at the moment. It is quickly becoming the most visited tag on Stack Overflow as well. One of the major reasons of its exponential growth is that Python is an incredibly versatile language. It can be used to develop websites, machine learning algorithms, as well as autonomous drones. A large number of programmers around the globe use Python as it empowers them to create almost anything. However, Python is not as easy as it seems to be. You really need to put in a lot of effort to master the language. 

Factors contributing to Python’s growth

One of the main reasons why Python is growing at a great pace is that it can be utilized for a large array of purposes, starting from web development to data science to DevOps. Therefore, it has become very important and useful to understand the concepts of Python development. 

Programmers who were earlier using programming languages, like C, and never believed that scripting could be as powerful, unless they were introduced to Python. The language plays a major role in lowering down the workload through tasks automation. Plus, it is pretty reliable and ...


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How-to Guide to Handling Missing Data in AI/ML Datasets

How-to Guide to Handling Missing Data in AI/ML Datasets

Artificial Intelligence and Machine Learning are the noble pursuits that depend largely on the data they are fed. With this data, systems figure out the future path and learn to handle complex scenarios. All of the applications of Machine Learning and Artificial Intelligence makes sense only when the supplied data is complete and rich.

But, in the real world, the data is not perfect, just like everything else. But, there are steps to fix the data when it is incomplete, incoherent, and unsuitable. Today, we discuss the methods to treat missing data when a comprehensive data is required for ML and AI applications.

Whether to ignore the missing values or to treat them effectively, depends on some factors to be considered such as the percentage of the missing values in the dataset, the variables these values affect, and whether the missing values belong to a dependent or an independent variable, etc. 

The performance of your predictive analytics depends on the accuracy and the integrity and the completeness of the data. Therefore, it becomes necessary to treat missing data when the need arises.

Treatment by Deletion

The best avoidable method to get over the missing data is to delete the record. This can be done either ...


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How to Monitor your Network Using Networking Analytics

How to Monitor your Network Using Networking Analytics

The fundamental job of a network administrator is to monitor the network for traffic usage, performance, availability, and faults. This continuous practice helps network administrators to respond quickly to the issues in their network, and ensure an optimized utilization of the network.

An effective network monitoring tool is either a software-based solution or a software and hardware integrated solution that monitors and analyses the network from end to end. The tool studies the network for various metrics such as latency, bandwidth, CPU usage, and responsiveness. The solution is also responsible for issuing alerts and warnings when the performance of the network exceeds thresholds that is when the network exhibits over-usage or downtime.

Employing Network Analytics can prove to be of utmost benefit for any organization as it empowers the management to:


Discover in-depth analysis of the network that is not otherwise possible.
Improve business performance by avoiding costly mistakes, maximizing revenue, and optimizing capital investments.
Lessen the risks of failure by making informed decisions based on the prediction of network usage and its implications.


6 valuable features of a network monitoring and analytics tool

Any network monitoring and analytics tool should have the following features inherently-

Interactive speed

All networking monitoring and analytics activities need to happen in real-time, ...


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5 Streaming Analytics Platforms For All Real-time Applications

5 Streaming Analytics Platforms For All Real-time Applications

Through Streaming analytics, real-time information can be gathered and analyzed from and on the cloud. The information is captured by devices and sensors that are connected to the Internet, as part of the Internet of Things disruption.

Streaming analytics solutions allow organizations to build real-time solutions using IoT and extract information from them later using Big Data, or to churn them first-hand using real-time processing. The power of streaming analytics is such that it allows for the streaming of millions of events in a second and thus allows enterprises to build mission-critical applications that require the performance to be quick and efficient.

Real-time streaming analytics can, for example, present to you the statistics if your latest online ad campaign is working as expected, or if it needs some tweaking to work better. Such applications want to always stay upgraded for performance benefits.

Here are the top platforms being used all over the world for Streaming analytics solutions:

Apache Flink

Flink is an open-source platform that handles distributed stream and batch data processing. At its core is a streaming data engine that provides for data distribution, fault tolerance, and communication, for undertaking distributed computations over the data streams. In the last year, the Apache Flink community ...


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Big Things to Expect from the Big Data Market: 2018

Big Things to Expect from the Big Data Market: 2018

Technology is evolving every single day, and we need to be prepared for them. However, saying is easier than done. Big Data solutions have become the latest trend in the tech world, thanks to its ability to help us get access to and understand data along with the analytical part. The year 2018 being only a few weeks away, and the way technology is advancing every day, we can start gearing up for some big-time evolution ahead, regarding performance, security and speed. Therefore, here are some of the fantastic features you can look forward to in the year ahead. Happy exploring!

Automated cognitive technologies

The earlier of Big Data only consisted of capturing, moving and storing data on computers. Now, the times have changed into tasks that would use automated face recognition to perform in the near future. You can also expect higher cognitive technology in reasoning and planning in the future, which will improve comprehension of data among others on computer systems.

Growth boost

Expect a breakthrough shoot in growth in Big Data analytics, which is already a growing market, in the year 2018 and beyond. Experts are currently hoping for surpassing $203 billion by the year 2020. The demand for data emphasis ...


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Top Four Competencies a Data Scientist Should Have

Top Four Competencies a Data Scientist Should Have

Jobs in the niche of data science is quite lucrative in terms of fat-pay package and adequate job exposure. But the role of a data scientist may slightly vary from one company to other. For example, in the Indian job market, almost every Java software development company that deals with data science and data management, recruits data scientists but the role may vary depending on the nature of data the company deals with. This year unveils enormous job prospects for data scientist provided they acquire these four competencies.

Proficiency in quantitative analysis

Quantitative analysis is one of the most pivotal skills of a data scientist which helps him in acquiring knowledge about a set of data for three major reasons. These are   


Experimental design and concerning analysis: This mode of analysis works for consumer market-related data, where a data scientist can help a lot.
Machine learning:  This is one of the most intricate roles a data scientist plays. A data scientist helps in creating prototypes for testing assumptions, selection and creation of needed features, as well as he helps in recognizing the areas of strength and prospect in obtainable machine learning systems.
Simulation of complex economic or growth system: In this area, a data scientist checks ...


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How Big Data and Analytics Can Help Business Workflow

How Big Data and Analytics Can Help Business Workflow

Major organizations use Big Data and Data Analytics to improve their business and make informed strategic and operational decisions. But how do they do it? It is said that one can derive meaningful insights from data and convert them into actionable knowledge – but it is easier said than done. As you know that data is invaluable, and it is the foundation of every successful venture in the world. That makes it imperative for you to learn how to use it effectively for seeing a growth in your businesses.

Out of the various ways to analyze your data, there are some simple and effective tools that can help get more out of it. Let’s focus on six  ways to analyze data more efficiently:

1. Find a Database Engine

Database engine does sound like a lengthy process to implement, but it is simple to utilize and use. Every company has a lot of unused data, which could be used to serve the customers. It can manage your entire data and content, along with the archived data for future usage. It ensures the security of your data and keeps them all in one place for easier review. You can easily work on areas that need ...


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