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Month: August 2018

Keeping Agile with Change Management

Posted on by Imarticus Learning

Many software start-ups and IT companies are now resorting to ‘Agile’ and change management. Agile methodology mainly refers to dissecting a project into short-term cycles which are commonly known as “sprints.” The assumption behind agile methods is that the situation keeps changing throughout the project.
Agilists focus on the reality of the business and try to deliver the change projects within stipulated time by performing the project in short sprints rather than adopting a single change management process.

The Agile development process

The classic Agile development process case study has been detailed in the Scrum 1995. The critical observations of the Agile framework can be considered as:

  1. The disintegration of the change projects into short-term iterative cycles
  2. Less time and space to achieve the objectives as now you are focusing on completing one milestone after the other
  3. Changes and developments in the current plans
  4. Organizational resistance to change management
  5. Managing the impact of agile on the existing project managers, and IT team

The short tern iterative cycles are repeated every 2-3 months depending upon the size and requirements of the project. The steps involved in the iterative includes planning, requirement analysis, designing, building, and testing. These processes keep changing with every iterative due to the underlying assumption of the dynamic situation in an agile environment.

Agile Manifesto

Due to the flexibility and adaptability of agile methodologies, an Agile Manifesto was published in 2011.
The principles of the Agile Manifesto are:

  1. An interactive environment: Individual interactions at an organisational level is given prime importance. It involves coordination, teamwork, and motivational sessions.
  2. A pilot version of the project: Before the release of final version a demo pilot version of the project is adapted to understand best the requirements of the customers rather than depending upon documentation process.
  3. Continuous customer interaction: One of the most critical factors to be considered is constant interaction with the customers to understand the exact requirements of the customers.
  4. Quick response to change: Agilists need to work faster and quickly respond to the changing environment.

Role of Change Managers in the Agile framework

It is imperative to set up a highly effective change management team for successful implementation of Agile practices at the organisation. Since the success of the project is highly dependent on the performance of the change managers, it is imperative to have an experienced change management team for agile projects.
Training should be continuously provided to the teams according to the changing times and requirements. It should be focused, precise to keep them abreast of the current developments.
Communication should be direct, precise and frequently performed to achieve clarity on the topic.
The change managers should be able to tackle resistance at middle-level management through continuous communication.
A change management team comprising of professionally experienced change managers will contribute towards successful completion of the agile project.

How Can I Double My Salary in Two Years? Data Analytics is Your Answer!

Posted on by Imarticus Learning

Many people ask if it’s possible to increase their earnings by moving into data analytics. The answer, clear and concise, is “YES”! But, there are some requirements one needs to meet for that.
Data analytics is today what computer was when it first came around in the 1960’s and 70’s. Network, as a newfound tool, attracted the attention of engineers and scientists across all fields with the new use of the machine being discovered almost every day due to its ability to fast and error-free calculation which later updated to storing of information and even receiving and transmitting them very fast. This led to widespread applications of the computer during that time which has only increased since then. Data analytics is in a similar situation today.
Also Article : Career Opportunity in Data Analytics

The sudden interest in Data Analytics

Data analytics jobs have emerged as a significant field of attention in recent years due to the advancement of computational and mathematical models by companies such as Google, Amazon, Microsoft, etc. Add into it the near-universal connectivity and access provided by technologies such as GPS, cloud and smartphones in modern time, and we get a giant network of interconnected devices continually generating data which can be used by the organisations for their benefits. The analysis and of this data and concluding from it falls under data analyst responsibilities which is in significant demand right now as companies are willing to offer much pay package and amenities to data analysts.
However, people often tend to forget that the salary offered to them by companies comes from the profits earned by it from the end user side. So, unless a working professional can add a sort of value to the company, he/she cannot expect an excellent salary even from the data analyst job.
Some of the factors required from a person for value creation are –

  • Mindset
  • Knowledge
  • Skills

A data analyst career needs an analytic mindset just like a career in computers required a computing mindset earlier. All of the students and professors of data analytics must ponder over the points mentioned below –

  1. All individuals related to the field of data analytics must be able to make sense out of a sense of numbers which seem unconnected at first glance. They must possess an eye for patterns and the ability to connect those patterns with the information available to them.
  2. The knowledge of analytics has evolved a lot over the years in the form of optimisation models, machine learning, statistical models, pattern recognition techniques, artificial intelligence, etc. Such a dynamic evolution of the field demands the professionals to be at their toes always. Any person looking to thrive must undergo proper data analytics training to survive the competition in the area.
  3. Just as the advent of the computer prompted professionals to learn programming and coding in the 60’s, data analytics requires the learning of analytical tools and languages such as Java, R, C++, etc. An analytics certification goes a long way in establishing the qualification of the person in the field.

So, the people who ask whether it is possible to double their salary by turning into data analytics must ask themselves whether they possess the mindset required for it or not first. It’s true that a data analyst salary is excellent compared to another field in today’s time, but one needs to do a lot of hard work and must possess the ability to impart value to his/her organisation to achieve that. Getting a top rank in analytics doesn’t guarantee success. Instead one must possess the ability to apply the knowledge for the company to make it.
Related Article : Data Analytics Popular Algorithms Explained

RBI Forms a Unit On Cryptocurrency, Blockchain and AI

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All of us were shocked to hear the news of RBI banning cryptocurrency in India. Few of us were afraid that such a promising technology would go unrecognised by the Indian Government. But after banning cryptocurrencies, the Reserve Bank of India has now formed a particular unit for the development of blockchain and artificial intelligence lead by a general manager. Even though the official announcements about the team are yet to reach you, the unit is also expected to draft rules and regulations for the cryptocurrencies and other emerging technologies.

RBI and Cryptocurrencies

In 2018 April, the Reserve Bank of India barred all the banks and financial institutions from dealing with virtual currencies. The risk associated with cryptocurrencies had led the RBI to consider this regulation. It meant that your banks would not allow you to buy cryptocurrencies to protect your money.
However, the blockchain technology is identified and has been studied for the improvements in the banking sector. Recently RBI tested a blockchain based trade application successfully.
RBI is not impressed with the non-fiat structure of cryptocurrencies, but the underlying technology is identified and being studied.

Role of the New Unit on Cryptocurrencies and Blockchain

As a regulator of financial activities of a country, RBI has to be on the top of every emerging tech to decide whether to adopt or not. In the financial sector where we are slowly changing from paper to digital, it is essential for RBI to look into cryptocurrencies and blockchain which is presumed to be the future of money transactions. In that sense, RBI is doing precisely what the formation of this unit supposes them.
The RBI has recently mentioned the plan to set up a data science laboratory with professionals from economics, econometrics, data analytics, computer science, statistics and finance background. The new unit is expected to be able to use data analytics with the aid of new technologies for predicting different RBI functions. Inflation targeting, policy enforcement and banking regulations are included in these RBI functions.
Also, despite the ban, the RBI is not completely ignoring the possibilities of cryptocurrency. A fiat digital currency named “Lakshmi coin” is in construction by the RBI. Shedding light on the regulatory framework might be another critical role of the new unit.

Central Banks Around the World

The central banks around the world agree with RBI on the fact that privately issued cryptocurrencies such as Ethereum and Bitcoin are not going to replace the traditional currencies. LIke India, a lot of the countries are working on cryptocurrency issued by the central bank of the country. The fraudulent activities associated with the technology is delaying the release of such coins. The states also have to ensure that their systems are mature enough to handle the payment systems.
All countries are going forward in this matter despite their different speeds. Unfortunately, a collaborative than an individual study on digital currencies backed by global central banks as a legal tender is yet to happen which will ease the process of cooperation in the future.

New To Data Science? Start With These 10 Python Libraries

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New To Data Science? Start With These 10 Python Libraries

Data Analysis has become the forefront of every organisation. Companies combine Big Data and cutting-edge data analytics to arrive at actionable insights that benefit business performances.
We know that Data Science has been dubbed as one of the sexiest jobs of the 21st century. If you’ve always wanted to learn Data Science, then R and Python are your bread and butter. To get started, here are the top ten Python Libraries you should sink your teeth into:

  • NumPy

NumPy stands out as a beginner-friendly Python library. It features sophisticated broadcasting functions, powerful multidimensional array objects, and matrices. It doesn’t use loops and lets you transfer data to external libraries that are written in C, C++ or Fortran Code.

  • SciPy

SciPy is NumPy’s best friend and relies on its speedy N-Dimensional array manipulation. SciPy offers users various numerical routines such as numerical integration and optimisation. SciPy, when coupled with NumPy, is used to solve multiple tasks related to integral calculus, linear algebra, probability theory, and others. The latest editions of SciPy involve significant build improvements and bundle the new BLAS and LAPACK functions.

  • Pandas

Pandas is a Python Library that lets you translate complex operations with data in just a few commands. It includes built-in features like grouping, time-series functionality, filtering, and lets you combine data sets. Its numerous bug fixes and API improvements make it a must-use library for Data Science enthusiasts. Additionally, Pandas lets you perform custom operations.

  • Matplotlib

Matplotlib is a low-level Python library used for data visualisation in interactive environments and hardcopy formats. It lets you create graphs, histograms, pie charts, scatterplots, and more. There’s a colourblind-friendly colour cycle feature, and the latest versions include support different GUI backends on operating systems and lets you export graphics/images in various formats like PDF, SVG, GIF, JPG, BMP, etc. The legends and graph axes are automatically aligned, and when you use it with the iPython Notebook, it becomes your visualisation playground, literally.

  • Scikit-Learn

Scikit-Learn lets you quickly implement various Machine Learning Algorithms on your datasets. It gives you apply algorithms on tasks related to logistic regression, classification, clustering, etc. It’s a popular module that’s built on top of the SciPy library and is perfect for beginner and advanced Data Scientists.

  • Theano

Theano is a Python library explicitly used for mathematical computations. It lets you optimise and evaluate mathematical expressions to your liking and uses multi-dimensional arrays for blazing fast calculations. It also works as a core computational component in libraries like the PyLearn 2.

  • Statsmodels

Statsmodels lets you statistically explore data and includes various classes and functions that help you estimate statistical models. Its ‘estimator’ brings a list of ‘result statistics’ that let you test your analyses against existing statistical packages which are released under an open-source license.

  • Plotly

Plotly lets you create complex visualisations, maps, financial charts and various graphical presentations that meet publication quality online. It works with interactive web applications and bundles features such as ternary plots, 3D charts, contour graphics, etc. Crosstalk integration, “multiple linked views” and animation generation make it one of the hottest visualisation tools in Data Science.

  • Bokeh

Bokeh lets you create scalable and interactive visualisations using JavaScript widgets. It includes a small zoom tool, customizable tooltip field enhancements, linking plots, and many versatile (but interactive) styling and graphing features.

  • Gensim

Gensim is a free Python library used for building scalable semantic statistics. Its retrieves structurally similar documents and speedily implements Machine Learning algorithms for useful statistical analysis. Perfect for topic modelling with large data-sets and is used popularly in text mining projects.
Conclusion
Use these libraries to kickstart your ML projects and avoid writing algorithms from scratch. They save time, are ideal for beginners and advanced Data Scientists, and are highly recommended in the Data Science community worldwide.

How Machine Learning is Changing the World?

Posted on by Imarticus Learning

It’s no new fact that the present generation of information thrives on data. If you think regarding digital bits, every company targeting any specific field has tons of data to manage. And eventually, it seems human hands are limited to process all of them. Thus Machine Learning comes to our rescue and as the name might suggest- we teach the machines how to do their stuff and get the work rolling out of them. To define Machine Learning, one may say that it is a core part of the developing technology called Artificial Intelligence (AI), in which you can program the devices to work by themselves on the input data without the need of an explicit programmer.
Also Read : How to Start a Career in Machine Learning?
The advancement in this sector can enhance the growth of a nation. Machine learning programs are evolving on a reasonable scale in India, and several companies are welcoming AI engineers. For any person interested in the field of science and robotics, Machine Learning courses are an exciting option with lots of scope for growth. There are several methods which are taught in the course mainly involving in-depth analysis of mathematical discourses, computer programming and formation of networks and algorithms. While Python Machine Learning is considered to be the most preferred language for coding- Java, C++, R and other words are also convenient options for those who are well versed in it.
Where can you explicitly see the outcomes of Machine Learning? Take for instance Google and Amazon. That’s where you look at Machine Learning on a smaller scale to enhance your web surfing into a personalised one. It’s, however, a developing field and industries are working on it on a massive scale to create such AI programs which can quickly change your daily lifestyle. The technology of Internet of Things (IoT) and Cloud Computing and its likes, all submerge into the growing implementation of Machine Learning to enhance objects and gadgets into being “Smart” for themselves. The potential massiveness of such a concept is endless from the current standpoint.
Managing data can be crucial in interdisciplinary fields like education and thus, Machine Learning comes in very handy in such areas. The current pedagogy of classrooms is an evolving one where the teacher is necessarily required who is thoroughly learnt in the subject. Smart classrooms have been developed into expanding the database of resources. But real enhancement in the school of fifty individuals can come only by following a method where every child benefits and is given resources according to their needs. That’s where you need to learn digital systems which can record every individual’s performance and provide an accurately customized report of their specific needs.
Having talked about classrooms, one may analyze other such institutions where a large number of data needs to be managed. Take a law court for instance which sees multiple cases each day of varying degree of importance.
In the current scenario, a lot of handworks is needed to collect and categorize data. Machine learning can be boons at this point to several lawyers who won’t need overspending time on an essential collection of data. Even manual labor in industrial sites which is often risky for workers can be replaced by automatically functioning machines which can finish the task faster and more efficiently.
Health sectors can utilize a digital system to self-diagnose patients and cross-referencing of symptoms. Your daily life at home can become very easy with automatically working ACs, refrigerators, washing machines and any switch operated device. With the current rate of development, one can easily say that Machine Learning is positively here to evolve the world.
Related Articles:

Using Blockchain in Healthcare

Posted on by Imarticus Learning

The term blockchain might sound familiar, ever since the rise of cryptocurrencies. It is a peer to peer distributed ledger technology that was primarily used as the underlying architecture for the cryptocurrencies. The significant advantage that attracts everyone to this tech is its ability to keep your data unrecognisable until it reaches the recipient, making it the safest system for data sharing. The financial sector has already started to make their progress through blockchain. Now the healthcare sector is using this new element of the tech world to grow.

Health records in a distributed environment

The blockchain is valued in healthcare primarily for its ability to privately, securely and comprehensively track the health records of a patient. Think about how the health records of a person are currently stored. It is a puzzle with its pieces spread amongst many providers. Your primary care doctor carries a part of it. Several pieces are held by other specialists you have ever visited. The remaining bits can be found in many wearable devices that track your health. The blockchain technology helps the experts to bring all these pieces together and make a brighter image of your health. When a blockchain based system is used, you can walk into your physician’s room with all your medical history located somewhere in the network. With your consent, the doctor can examine and edit your data. The follow-up doctor can access all these updated data later again with your permission.

Insurance claim evaluation and disbursements

A recently published research paper by IBM reveals that the currently used methods for storing and reconciling financial data in insurance companies are outdated and fragmented. Also, the data sharing in the insurance workflow is not smooth as it supposed to be. Many organisations are hesitant to share data due to multiple reasons, such as having to abide by laws and regulations (HIPAA). This reluctance in sharing data helps criminals to make false claims and get away with it. The blockchain technology can offer a remedy to this problem by enabling the secure sharing of data among insurers. It can minimise counterfeiting, double booking and contract alterations. What the technology allows for the insures to have a permanent and auditable record of claim activities while creating receipts at any stage of the claims process. Along with improvements in data sharing, automation of insurance process can be achieved through blockchain. With the aid of up to date shared health records and smart contracts, the automated process can avoid delays and reduce overhead cost.

Investment scenario across the healthcare landscape

” Global Blockchain in Healthcare Market- Analysis and Forecast, 2017-2025” by BIS shows that the blockchain in health care is roughly $177 million in 2018 and would be over $5.6 billion by 2025. The drive for blockchain in health care is going to be the urgent need to improve the interoperability and security of healthcare information system. By 2020, 70%of the organisations are expected to have research going on to get the technology onboard. With the success stories of early adopters like Estonia’s healthcare system, significant disruption is assumed in this field.

Infographics: Who Am I? – Data Scientist

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Infographics: Who Am I? – Data Scientist

Everyone wants to become a data scientist, after all, it is considered and labelled as the ‘sexiest job of the 21st century!’ There is a lot of mystic in the tile ‘Data Scientist’. The data scientist is considered as a wizard, with mystical skills, which when applied can get great insights from the existing data, which holds the key to the future success. But as much as we would like to believe otherwise, it is the way the data is interpreted, analysed and methods of data science applied by the data scientist that gets us excellent results.
In this infographics let’s understand “Who is Data Scientist?”

 

The Economics Behind Artificial Intelligence!

Posted on by Imarticus Learning

The introduction of Artificial Intelligence in various applications is set to overhaul the economics of multiple industries. Due to rapidly advancing technology within Artificial Intelligence, the cost of prediction is decreasing at a fast pace. This decrease in prediction costs results in projection being used to solve many new problems, even ones that we generally don’t use prediction to solve.

For example, let us consider the case of autonomous driving. Before AI, autonomous driving, as we see it today, did not exist. It merely consisted of engineers programming a vehicle to move around in a controlled environment with instructions to run in case of obstacles and following directions to reach a destination. But with the introduction of modern AI, autonomous vehicles have gotten the capability to be smarter.

AI in Autonomous Cars
Today, when an autonomous vehicle is being “taught”, a human driver is put behind the wheel and drives as they normally do. The AI then uses various sensors onboard the vehicle to observe how the human drives and comes up with its protocols for use in particular situations.

The predictions that the AI makes, in the beginning, will undoubtedly be flawed sometimes, but the AI can learn from its mistakes and update its protocols accordingly. The more “practice” that the AI gets in this way, the more accurate its predictions keep getting and can ultimately replace the human at one point. This method of “learning” by the AI works the same way wherever it is applied.

Errors in Prediction
As the cost of prediction drops, the demand for human-based prediction will decrease. Human prediction is prone to failure due to a lot of factors like human error, clouded judgment, or even negative emotions. Using AI for forecasts removes all of these problems. Hence if adequately applied, AI can make much better predictions when compared to humans. Since AI is more efficient and costs less, eventually the value of the organization or company using it goes up.

The only area where AI falls short is human judgment. An AI can make predictions and give them to a human, but it is ultimately up to the human to decide what to do with it. Some companies like Amazon are working to remove these limitations, and their work has shown that ultimately AI can be used to make judgments based on their customers’ preferences and spending habits. For example, if a customer regularly orders a product, then the AI can decide to place the order for the customer when the time comes, thereby increasing the chances of selling the product.

Organizational Benefits
AI will be the most beneficial to organizations that can define their objectives and goals clearly. As we have seen above, the method of “training” AI makes it essential to have clear-cut objectives to reap the benefits. We have already seen AI making substantial disruptions in industries where it has been applied.

A 2013 study conducted by Oxford University estimated that AI could replace 47% of jobs in the coming years. A similar survey conducted by OECD estimated that AI could return 9% of jobs just within the next two years. Another study conducted by Accenture concluded that over 84% of all managers advocate the implementation of AI to make things more efficient.

Hence, to conclude, AI will have drastic implications for every industry, with it replacing humans in several roles. However, the savings to be gained from AI will make business practices more efficient and increase profitability.

Infographics: The Adaptation of Artificial Intelligence in The Industry

Posted on by Imarticus Learning

AI (Artificial Intelligence) is the simulation of human intelligence processes by machines, especially computer systems. Particular applications of AI include expert systems, speech recognition and machine vision. It (AI) is a branch of computer engineering, designed to create machines that behave like humans. Although AI has come so far in recent years, it is still missing essential pieces of human behavior such as emotional behavior, identifying objects and handling them smoothly like a human.
Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals.

Artificial Intelligence

How do you Measure Change Success?

Posted on by Imarticus Learning

” I can’t change the direction of the wind, but I can adjust my sails always to reach my destination.” – Jimmy Dean.
Change is inevitable, and there is a need to be implemented almost on a daily basis. Evolution is a tricky topic. Honestly, not many of us like changes in our life be it at the workplace or in our personal lives.
Just imagine a situation wherein approximately 20 years back, if people would not have accepted computers and continued with the typewriter, what would have happened then? Would there ever be a scope of so many improvements and technological inventions we are witnessing today? A change, even if it is a small one might change our ways of living.
It is always said that the road to success is never easy. The difficulties include changes, patience, perseverance and a strong will power to achieve something different and valuable. But how do we know that the change we are implementing will lead to sure shot results? So in this post, we discuss how do we measure change success.
Before discussing the metrics of change success, we need to understand that it’s not always the result which matters many times the way we manage a situation, and how we tackle the changes is what matters the most.
Success parameters
The Project Success Measure parameters determine if a project is successful or not. But this is just a perspective of viewing the result of the change; there are many other facets of evolution which you need to consider before concluding. For instance, many projects look lucrative from a marketing perspective, but the overall profitable benefits are limited.
Collective team adoption
For any change implementation, there needs to be a change in team formation set up first. Cooperative compliance by the employees especially in the field of technology, systems, and implementation is what you need to consider. We all know that total adoption of change policy cannot be implemented from day one, it will be gradual and over a period.
Often, we tend to misjudge the situation when there is a low benefit realisation. And then the change management team will be held responsible for not performing as per expectations. But the actual reasons for low benefit realisations are many, and that may or may not be related to the efforts taken by the change management team. For example, the change team would have achieved great success, but the overall policy would have been illogical due to which the benefits realisation stands low.
Profits/Benefits Realisation
Benefits realisation is the most uncertain part of the change topic one can explore. Benefits tend to appear over a period, there is no sure shot guarantee to measure the profit realisation beforehand. Hardly there are any projects which would generate profits immediately after implementation, and it takes time.
The process of change and the pathway to success
The quote by Robin Sharma “Change is hard at first, messy in the middle and gorgeous at the end” perfectly puts light on the insight of how a change process takes place. But what matters the most is the execution. A well-planned implementation of a change policy can do wonders to the organisation, and at the same time, a poorly executed plan can fail.
Amazon is an iconic example when we think of change and success. When Amazon went online in 1995, it used to sell books. Next, they started with CDs and DVDs, and today I doubt there’s anything Amazon doesn’t sell.
When we talk about change management, not all changes will result into success, some may occur, and some may not. At times the change policies fail, and at the organisational level, these failures need to be sustained and catered for either with an alternative plan or by just bearing the losses. Scrutiny of ‘what went wrong’ and how to overcome it next time backed by a strong SWOT (Strength Weakness Opportunities and Threats) analysis will lead the path to success.