SigmaWay Blog

SigmaWay Blog tries to aggregate original and third party content for the site users. It caters to articles on Process Improvement, Lean Six Sigma, Analytics, Market Intelligence, Training ,IT Services and industries which SigmaWay caters to

How technology helps in relocating your home

Packing and moving your living space is quite a headache. Especially in India, most of the people try to find someone providing the cheap shifting services and hence get trapped as they do not provide safety to your good, thus resulting in an expensive net cost. Thankfully, technology is again helping humans to get a fair deal in terms of safety of good and proper quotation of charges. Some of the reallocating companies have started to use AI based softwares that can quote exact amount to be charged from customers based of the quantity and nature of goods, destination, path to destination, floor of new location, whether lift is present or not and many more relevant factors. These softwares also helps to find the price for the goods so that they can have an insurance of that amount. Customers can also use the GPS technology to track their goods while they are being shifted to a place too far.



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Applications of Python

Python Programming language is already quite famous for its power and simplicity. It has become an all-in-one programming language because of availability of so many libraries and frameworks which makes python extensible to many areas.

The top 6 application areas of Python are as follows :-

1. Business

2. Scientific / Numeric

3. Education

4. Gaming

5. Web and internet

6. GUI applications.



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Steps for better utilization of data

It’s really fascinating to see how we are generating an incredible amount of data every minute. Plenty of blogs and articles have been written stating that the better utilization of this data may result in more profit generation for the organization. So here are some tips to begin with the path of better utilization.

Currently what most of the organizations see is they are Creating data, and they want to generate some additional revenue from it, but they miss how to link these two aspects. 

To begin with, they must consider to use the Big Data Engines like Hadoop, Google Bigdata, Horton Works, MongoDB or anything similar. They help to find the patterns and correlations between different types of data.

Next is Data Warehousing. Data warehousing is not just maintaining a database. It has many advantages over the traditional databases. 

And another important step is Data Visualization. From the above two steps we have the data properly managed and organized. In this step we visualize the data in a productive manner.



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Data security in Healthcare

The world is becoming more digital. Health industry has also turned mostly digital and maintains every record and every detail in digital format. This has many pros but it also bring some threats like in terms of data security. Health industry keeps a lot of information about a person like their name, date of birth, address, card numbers, contact numbers, insurance details and many more. 

There are the following 3 reasons why these data needs to be protected :- 

•  To avoid identity theft.

•  For the patient’s safety.

•  For the reputation of the hospital and healthcare providers.


To ensure data security, just installing the antivirus is not enough. There must be other features like data encryption, limited access and modification of the data and limited transmission and sharing of data. 



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How to Manage Big-Data

For any organization, it is necessary to keep a track of the data created, data pulled, the source from where it is pulled, the format of the data they have been using till now, who can access this data and many more. There are many strategies for managing the data but there’s nothing as a perfect strategy. It varies from organization to organization and types of data. Another important factor is size of organization and its budget. The following are the three factors that can help in managing the data in a better way i.e. as per the GDPR rules.

1. Centralization of data

2. Automating data management 

3. Measuring Success.



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Programming Languages prevalent for Data Science

Tons of data is generated everyday in the industry. And making sense of this pile of data has become an important task for many businesses. To achieve this, they are turning into Big Data analytics and Data Science. Data Scientists have knowledge about various algorithms suitable for various types of data and these statistical algorithms are implemented in several programming language. Selection of the programming language depends on many factors. So here is the list of top 6 programming languages that are used by most of the data scientists and analysts.

1.  Python

2. R Programming

3. Matlab

4. Java

5. Julia

6. Scala

Read detailed review at


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The cloud is really Growing up

Just 5 years back, people were debating over whether to accept cloud over in-premise systems, its cost, the billing systems of cloud etc. The topic “Should we use cloud” from that time has turned into “What else should be do on cloud”. Cloud has really grown and have got its roots really deep in the industry. Most of the industry is planning to find new ways to do the business which has cloud as a major part in their systems. 



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Importance of Proper data analysis and usage

Data is like an untapped stream of gold. If used properly, it can help any business to start climbing of the stairs of success real fast. History has plenty of examples like Hershey’s, Caterpillar, Vermont Electric Power Company and many more. These companies began to store, segregate and analyse their data. The data includes their operational activities, sales, purchases, monitoring employees, data about competitors and many more. Proper usage of this data can bring a digital transformation in the organisation. Data analysis often results in cut in operational cost. But its benefits doesn’t just limit till the cost cut. Data analysis can also show a path for revenue generation.



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Most prevalent languages for Machine Learning and data science

Careers in machine learning, Data science, artificial intelligence, deep learning and many more are considered as one of the best choices to pursue. Now these technologies and the related jobs are considered one of the hottest and best jobs today. So, here are the list of top 5 languages prevalent in market for data science, machine learning etc.

1. Python

2. R

3. Java

4. Scala

5. C



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Don’t fall behind in terms of advancement.

Over past few decades, there has been a rapid advancement in the industry. It has become important that the strategists verify that their end users are getting the latest and quality material. Falling too behind of the advancements may result loses for the organisation. There are several symptoms that may help you to understand it’s the time to make necessary changes in your IT infrastructure to meet the requirements of the end users.

5 such symptoms are as follows :-  

1. Increasing number of IT help desk calls.

2. Hardware/software failures and outages on the rise.

3. Rise in the use of shadow IT.

4. Nobody wants to work for you.

5. New tech initiative is scrapped due to shortcomings in the infrastructure.

Read in Detail at


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Most common programming languages among Github Users

Github is a very common and well known platform for learning, sharing and developing programs. It’s very helpful especially for team projects. With time Github has turned to be a platform for interactive programming learning. The most common and most chosen programming languages among the Github users are :-

• javascript

• Python

• Java

• Ruby


• C++


• C#

• GO

• C

Read Detailed review at


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Initial steps into AI for your enterprise

May be your organisation has just started to step into AI, but don’t worry its not too late. It takes plenty of time to embed a good helpful AI into the organisation and there’s no scope of mistake. Giants like Google, Amazon, Microsoft have implemented AI at early stages because they have plenty of data and plenty of resources. For any organisation it is important to find the best suitable aspect for AI implementation which will provide benefit at most. 



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New job trends in cyber security in IT sector

Cyber security is of utmost important to any organization and of late there has been a tremendous rise in cyber security jobs. This high demand comes from expansion in the interconnectedness of gadgets and computing systems, thus increasing the potential points of intrusion. So, here is a list of some trends in cybersecurity that will affect jobs in the IT sector. They are: Artificial Intelligence, Internet of Things Openings Outbound and Opening Inbounds, Cloud Services and Mobile Devices and Third-Party Apps. to know more, go to:


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Data Loss – A Threat to Company!

Data is the most valuable asset for any company and any person dealing with this data needs to be cautious. Modern businesses rely on data. They store, process and access data for information gathering and use it for decision making. According to reports of 2017, a single mistake in handling this data can result into loss of nearly $3.6 million. 

However, data can be loss due to various. Few of them are:

1. Human Error

2. Hardware Failure

3. Theft

4. Online Crime

5. Natural Disaster

The best way to deal with this is to take prevention and keep an up-to-date recovery plan and a 3-2-1 backup strategy, i.e. there should be three copies of data, kept in two different mediums, and at least one of the backups should be off site.

Read about it at:


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Where are we? – Advancement in AI

The pace at which technology is moving is unmatchable. Every day some advancement in technology comes into lime light. Among the various fields in technology, one of the major and trending technology is AI. It is growing each day and proving to be a solution in many applications. Below are the 10 areas where Artificial Intelligence can be noticed:

  1. Robots predicting the future
  2. Robot Soldier
  3. Survival Robots
  4. Police using AI algorithms to predict crimes
  5. AI-based medical treatment
  6. Autonomous drones and weapons
  7. Supercomputers with imaginations
  8. AI communicating with AI
  9. AI hackers
  10. AI in court

Every thing in this world have a positive side and a negative side. Similarly, a few cases have been there where Artificial Intelligence have gone wrong. However, chances of improvements are always there. 

To know more about these 10 areas and other uses visit:


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Bringing Artificial Intelligence in Business

Now a days, companies are investing a huge amount of money in Artificial Intelligence, Automation, Robotics etc in order to be up to date with technology. However, still there are few limitations that each of them faces. Even after adopting various technologies, the reports stated that the company still struggles in defining goals. Below are the five steps that companies must follow in order to improve and to meet their expectation for automation technology:

  1. Recognize that the use of intelligent automation is transformative, and built on the use of new machines and data sources
  2. Formulate a comprehensive approach to automating the service delivery model
  3. Measure value vs. risk
  4. Consider the “operating model” in all forms
  5. Disrupt from within

Bringing innovation in always good but it is also suggested to look for other alternatives for investment as not all technologies proves to be powerful for a company.

Read more about it at


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Rules to Follow in Data Analytics

Analytics is one of the major jobs performed in companies these days. Daily operations are carried out involving data that presents us with results which helps an organization to carry out further processes and helps in decision making. Effective business intelligence is the product of data processed. This data is raw and can be either structured or unstructured. 

Firstly, one needs to manage data before processing it. Rules are to be set for the analytics process which can offer better insight and an easy processing. Below are the five rules that can help in managing your data more effectively:

  1. Establish Clear Analytics Goals Before Getting Started
  2. Simplify and Centralize Your Data Streams
  3. Scrub Your Data Before Warehousing
  4. Establish Clear Data Governance Protocols
  5. Create Dynamic Data Structures

The field of data analytics is always evolving and thus it is important to create a proper structure that can help in future. By establishing them we can enhance the quality of data processing.

Read more about it at:


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Learning Neural Network and Deep Learning

Artificial Intelligence is a combination of various sub topics. Whether it be Machine Learning, Deep Learning, Neural Network etc, each one of them finds shelter under artificial intelligence. Below are the basics of two important topics – Neural Network and Deep Learning.

Neural Networks: Neural network is modelled in the same way as human brain. It compromises various algorithms and aims to find relationship in data provided by us through processes that mimics like human brains. It is one of the trending technologies and is finding its applications in various fields like trading, medicine, pattern recognition etc.

Deep Learning: Deep Learning is basically a network composed of several layers. It is also known as ‘stacked neural networks’. It is a subfield of machine learning and is inspired by the structure and function of brain. It can be widely used in classification, clustering, predictions etc.

We are walking towards the era where technology will dominate and the main technology will be artificial intelligence. Hence it is important to get an insight of what we will be with in future. For a beginner, these topics might be confusing but they form the crust of artificial intelligence.

Read more about them at:


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Reasoning for Slow Pace of Digital Transformation

Digitalizing is becoming the need of hour in every business. Every organization is trying to in cooperate technology as per there needs. Companies believe that cloud storage, analytics, mobile and social advancement are all the tools they require for digital transformation. However, this is not enough. Digital Transformation is still lagging behind even after great efforts by organizations. One reason behind it is the fact that one could not match the speed at which technology is growing. 

Following are few challenges that organizations faces while trying to keep digital transformation up to date:

  1. Lack of vision and leadership
  2. IT and business don’t see eye to eye
  3. Little to no engagement
  4. Transforming ops is hard
  5. Governance is lagging
  6. Critical functions are being shortchanged
  7. Shying away from the cutting edge
  8. Metrics misalignment
  9. Failure to change culture
  10. Not failing enough

Read more about them at:


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Finding Data!

Data is very important for various technologies. Whether it be Artificial Intelligence or Machine Learning, Data analysis or Research work, Data is mandatory to implement them. However, the task of finding right data is very tedious and time consuming. One needs to find data that is most appropriate in terms of information available, size and other factors. 

Every day a huge amount is data is generated on internet. To our help there are few open data sources that are free to use. This data can be in raw form which might need further processing. But to start with the process and to get a data set, one could visit below mentioned sites that provides data for free: 

  1. Kaggle
  2. UCI machine learning repository

Know more about them at :


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