Power of Big Data Indeed

Big Data is a term for huge amounts of structured or unstructured data that possess problems for traditional data processing applications. As an organization or company expands, its data expands too, leading to high volume unstructured data spread over a multitude of database systems. Nowadays, more and more companies are spending money on Big Data and the accurate analysis of this data to help in better decision making, greater operational efficiencies and reduced costs.

Data can be classified based on volume: the size of data, velocity: the size at which data is being streaming, variety: data can be in different formats, complexity: data can be complex and come from many different sources and come at different times.

Big Data can come from a variety of sources, majority of which is social media. Other than that, many Internet of Things devices also contribute a major chunk of the incoming data. Other sources include websites and publicly available resources on the internet.

With large amounts of data, come huge challenges, especially of storing data and analysing it. How do you find the relevant information from a huge resource of data and how do you best use it to your advantage? That is the question that Bid Data wants to offer with the help of analytics and high performance computing.

With huge data, you need faster and better computational power. Some of the technologies, that is helping Big Data are, cheap and abundant storage, faster processors, open source data platforms like Hadoop, cloud computing, fast connectivity and other data processing techniques.

Keeping all this in mind, many open source services have sprung up that help in migrating and analyzing Big Data. While many services are available for data migration and Big Data, the two key standard service providers used by big companies are Amazon and Oracle. These open source services are the standard method for migrating data as they provide convenient methods and being open source, they are free.

So how is Big Data helping you? Well, with better analysis, companies can better understand their consumers and make them products which are useful for them while setting prices that maximize sales and customer satisfaction.

Acquiring all this data and successfully analyzing it can bring high profits to a company and give them a better understanding of their customers.

Unlocking IoT and AI

Internet of Things, in short IoT and Artificial Intelligence (AI), are few buzzwords you must’ve been seeing on social networking sites every now and then. Before we shed light on their importance in the 21st century and how they are interrelated, allow us to briefly explain what IoT and AI mean.

Internet of Things (IoT):

Imagine a house with multiple objects which you use every day. If you connect all those devices over a network thereby allowing them to exchange information between themselves (i.e send and receive data) it’s nothing but IoT. To put it in simple words, connecting multiple objects over a network by establishing a means of communication to exchange data. Here, we used a house with multiple objects to explain the concept but, in reality, anything tech can be connected to a network. Smartphones, automobiles, and even cities. In fact, the possibilities are endless.

Artificial Intelligence (AI):

Before we explain about Artificial Intelligence, it’s important for you to know what a rational agent is? In simple terms, the major functionality of a rational agent is to select an optimal outcome from all the possible and practical outcomes. A rational agent can be any of the following: a firm, person, software, or even a machine.

As the name itself suggests, the intelligence showcased by a rational agent (machines or software) to improve the success rate by offering better decision making is termed as Artificial Intelligence.

Artificial Intelligence plus Internet of Things:

Internet of Things, in the process of exchanging data, generates large volumes of data. It would be next to impossible for humans to extract small and useful details (knowledge) from these huge sets of data since it consumes large amounts of time. That’s where AI comes in. Data generated, when combined with Artificial Intelligence allows for predictive analysis and automation thereby making life easier.

Amazon can be considered as a good example, for instance, whenever you shop at Amazon, it observes your shopping patterns and suggests you with other related products you may be interested in.

Entertainment apps like Spotify and Netflix also pretty much do the same, they observe your previous interests and depending on them, they recommend you with similar music and movies respectively.

Both the technologies, Internet of Things and Artificial Intelligence are growing rapidly. However, there are few potential threats, security is one of them. With the increase in data, the onus of protecting this valuable information from hackers is also increased. Data, in wrong hands, can prove out to be very damaging even for an individual or for an organisation.

Although both the technologies, Internet of Things and Artificial Intelligence are not yet fully matured, with the pace they are currently growing, things will be entirely different from what they are right now five years down the line. Are you excited about the future? We can’t wait to see what else these technologies will bring to the table.

Decoding Artificial Intelligence in Reality

These days we hear a lot of companies using the buzz words Like Machine Learning, Deep Learning and AI in their products and services, however the real world problem is that when asked about the impact of the services or the product they have, then most of the times it comes to on this single statement of “overall increase in business efficiency”, thus it creates confusion and the statement is pretty ambiguous in nature, which is sometimes baseless and without experimental driven. Hence in this article, I want to uncover this above statement and make it easier for both companies and the clients to calculate the actual impact of the product/services.

Being a technology consultant and working in the domain of AI, I personally used the exact statement whenever I wanted to pitch my services to the client, but when realizing the quantitative impact of the services I was unable to justify my piece of work because the services offered didn’t provide me the numbers and the figures. Facing this conundrum I got to understand the fundamentals of the buzz words in practicality.

“Technology is relative to time”

Reviewing the archives of the 19th century on the industrial revolution, I summarized that it was a way of automation in those days, and there was growth in the overall production of the products just by getting new machinery in the supply chain. The whole change of system can be called AI of that era in a simpler way.

Now coming up in a today’s world the definition of AI is not limited in only technology space the ground reality is whenever there’s an automation of the manual process that can be defined as Artificial intelligence, the end result should be always growth not just limited to efficiency because efficiency without sustainable growth results is of no use.

Recently we worked with one of the leading grocery chains in Europe and we didn’t write a single deep learning algorithm but integrated two in-house software systems one was POS(point of sale ) system and the was an ERP, the integration of the databases made it easy for the organization to get instant results of the per cashier transaction in an hour and then the company was able to identify the lethargic cashiers with actual figures on real time.With this chain effects, the average time per customer billing reduced to 45% and sales increased by 30%, which was something with an actual growth chart.

Conclusion says that in long run one cannot use ambiguous terms like business efficiency or business productivity, without demonstrating the actual growth charts, as growth means value addition and that what is really a need in this high paced world.

Predictive Data for Enterprises Via SaaS

So here we would explore about the trending topic of predictive analytics, many of you just stumble upon this word on the various technology magazine or tech blogs, however some of you are oblivious of this term and think that it might be another term. Thus we decided to provide you with complete insight on predictive analytics in this article, further we will also dwell on the uses of the same in wide industries and take you to the future.

Predictive analytics is statistical approach to predict the future outcomes through existing data using various statistical and mathematical tools including linear regressions and multivariable regressions. Predictive analytics comes as a union of mathematics, statistics and behavioral psychology. These days we are able to find this term on tech blogs because the growing data has contributed to use machine-learning algorithms to curate the data and provide predicative insights to the intuitions and companies.

The modern day algorithmic computation has resulted in paradig in shift the field of predictive science, earlier getting particular insights from data were not that precise and a tedious job, although the introduction of new computation techniques has resulted in more accurate predictive outcomes. The power of statistics with computation has revolutionized the meaning of data analytics.

As we have understood the fundamentals of predicative analytics now its time to get to know the real uses of predictive modeling and how the large institutions are amalgamating different approaches with predicative models, one of the quintessential industry to use predictive analytics is Retail and the other is logistics i.e. the supply chain management.

In retail industry the data sets are humongous and to get real time predictive insights is a critical job to be, therefore retail industry’s biggest player has integrated predictive analytics with its point sale system (POS) to track their customer behaviour and the results are impeccable as now Wal-Mart is able to launch new products and target right audience, not only Point of Sale (POS) but Wal-Mart has integrated social media to get insights from 2.5 petabyte data per hour and has acquired a small startup based in Silicon Valley working in the field of Big Data and predicative analytics for retail customers.

The next industry using predictive science widely is Supply chain management (logistics) where the predictive analytics helps the organisations to make better decisions and increases the profit margins.

The hard fact is that only handful of big organisations are using predictive techniques to expand their business and many of the Medium scale enterprises are not aware that the data collected by them is just garbage when there is no proper analytics. More than 40% of the business people are not aware of the power of predictive intelligence and its positive impacts especially in the country like India where we have more than 1.25 billion people.

Hence conclusion would like to state that “Predicative Analytics” and “Big Data” are the buzzwords of today’s tech industry, However more than typical faddish fuzz, big data and predictive analytics carry the opportunity to change the business model design and day to day decision making that accompany emerging data analysis. This growing combination has deep implication in industries like retail, manufacturing, Hospitality and banking.

The Theory of Computational Economics

The extract will explore the intersection between the fields of economics, computation & mathematics.

In this technology driven world, we have witnessed many startups that have become big multilateral organizations like Microsoft, Google, Facebook and most recently uber, all these companies have used technology to make life simpler and have been able to solve the problems with their products, these companies have resulted in the formation of new industry and era of computational economics, which is amalgamation of the concepts of computation, economics and algorithms.

Today we are able to get the data of simple human actions in a more detailed way through these tech companies, we can analyze everything ranging from tastes and preferences of various content on the internet, most searches on a particular content, the spending of person on daily travelling needs and or even the most heavy routes of the city in a micro levels with given homogenous sets.

Hence, when we compile this free-floating big data with proper logics, algorithms and represent it with computation modeling, we will be able to formulate suitable economic polices which would be more intimate and apt with changing trends of the population as compared with traditional econometrical models which do not include the computational logics.

Now analysis of the data would also provide the economists with better growth models of particular industry by giving the statistics with relevant agents to drive the growth. These agents are critical to speculate the future growth in complex adaptive systems, which are dynamic in nature and mostly interrelated with individual and collective actions.

We have seen that how Google is working in the field of biosciences (complex adaptive systems) and using the concepts of computation to evolve the human healthcare, similarly the same concepts are being used by Palantir to help the US government in the fields of cyber security, defense & counter-terrorism.

Though not many companies are focusing on the applications of the computation in economics, operational research & finance, thus leaving an open space for entrepreneurs to make life simpler by inventing new tools with help of computation and providing professionals to compile the data more efficiently.

The Collaborative Economics

Sharing economy is one of the trending sectors among the investors and entrepreneurs, thus now the research world is taking a deeper analysis on the economical impacts of the collaborative world. A hot topic of this discussion is whether the sharing economy is bringing more wage opportunities to more people, or weather it is resulting the net displacement of the secured traditional jobs, while just creating a land of part time jobs with low paid work.

It’s a debate that continues to play across communities in the world especially in the developed world of west. Thus forcing reporters to weigh competing claims and varying in tone from boosterism to the warning of the new economy’s dark side.

According to recent studies of Alan Kruegar (professor of economics at Princeton) based on Ubers’s data finds clear benefits for “driver-partners ”, it has also helped to generate financial opportunities for hundreds of people with driving skills, further the paper helps us to understand the overall economic and social growth through technology advancement as the app has helped to boost the demand of ride services, with establishing a proper market for the consumers in low price as compared with traditional taxi companies.

Therefore resulting increase in income levels of these “driver-partners” and leading them with better living standards.

Another report in 2015 from Center of American Progress notes the heated debate in Britain over “zero hours contracts” charges that highly insecure and contingent employment leads to the exploitation of workers – the repot was co-authored by Harvard University’s Professor Lawrence Summers and Ed balls a British Labor Party MP it illustrates that technology has allowed a collaborative economy to develop in the United States and in many countries; these jobs provide flexibility to workers, many of whom are working a second job and using it to build income or are parents looking for flexible work schedules. However, at the same time, when these jobs are the only source of income for workers and they provide no benefits, that leaves workers or the state to pay these costs.”

I would like to summarize the above two findings by stating that a perfect economic scenario for everybody is not possible in any given situation, we can only desire to attain a perfect equilibrium in an economy, however we would need to think that what’s better for the majority.

Thus we will like to end this with the question:

Why buy when we can share?

Top 10 Algorithms & Models Every Data Scientist Should Know- Continued (Unsupervised and reinforcement learning)

For now let’s start with unsupervised learning and reinforcement learning, although both of them can be done with multiple algorithms but here we will discuss only a few popular ones. However, this does not mean you should try the other cause every problem in data science needs a special solution with the right hypothesis in the veracity and uncertainty.

Unsupervised learning:

I would like to define unsupervised learning as where there are no output datasets and the datasets are clustered under different classes. Thus you don’t have any trained dataset.

So the popular algorithms to solve it, are as follows:

Clustering Algorithms: As the name suggests clustering algorithms are used to group or regroup those elements that have similar traits, there are few clustering algorithms here below:

· Centroid-based algorithms

· Connectivity-based algorithms

· Density-based algorithms

· Probabilistic

· Dimensionality Reduction

· Neural networks / Deep Learning (Please don’t get into this cause the Wikipedia page will make you go insane, hence we will cover this in the next article).

PCA — Principal Component Analysis:

These algorithms are used to convert possibly correlated variables into linear uncorrelated variables known as components, where the procedure is used is known as orthogonal transformation (little mathematical but if didn’t get it we have an example too).

In layman terms PCA help[s to streamline the 3D graphs into 2d by making the variables as linear as possible. However PCS doesn’t work with too noisy data and while dealing with computer visions , but still it’s one of the best we have.

Singular Value decomposition:

PCA is said to be a simple application of SVD but the algorithms are mainly used is computer visions, although autoencoders are one of the best approaches to deal with computer vision because it is based on neural networks.

ICA independent component analysis: ICA is more powerful algorithm than PCA, the underlying logic of these algorithms remains the same as in the case of PCA but here the variable is treated mutually independent and non-gaussian.

The technique is used to identify the speech signals and most of the voice recognition system like Google Assist and SIRI use this algorithm.

10 Algorithms data scientist should know

There is no second thought that the subfields of statistics and neurology have gained huge popularity in past years. As Big data and AI are said to be next big thing in the tech industry, machine learning & NLP have powers to predict what will happen next and all this based on the past data collected. Some of the most common examples of statistics and NLP use by companies are the Facebook news feeds display algorithm and Amazon book recommendation engine.

However when the dataset becomes humongous then identifying right patterns is not a cake walk because each new data set correlates with one another in various tangents, the arduous task is to find the right pattern in the matrix of complicated information.

Hence to get the right data and patterns these are the few algorithms that are really essential for any data scientist and machine-learning engineer:

We can classify these Algorithms into three different subsets that are supervised, unsupervised and reinforcement leanings all of these subsets derive their operators and logics from statistics, neurology and mathematics.

So let’s start with fire:

A) Supervised Learning: Supervised learning is suitable for that dataset where the label is available for certain data, and from that label, the filtrations are done to achieve the predictive values.

1) Decision trees: One of the simplest ways to produce well defined predictive algorithms, though over concentrating and making unnecessary large trees might not help you in building appropriate predictive algorithms. Decision trees are built by answering yes/no questions on certain parameters.

2) Naïve Bayes classification: Too complicated right but it’s actually not , the classifier is built upon the high school math baby’s probability formula. The major use of this classification is in face recognition software and yes that trending Snapchat filters use the same thing to detect your face correctly.

3) Linear Regression: sometimes is really good to use the basic regression models like linear regression or least square regression. It’s just fitting the data set in a formula of the straight line and drives the predictive outcome while using the formula and the model.

4) Logistic regression: logistic regression is used when we want to get the binomial outcome of one of more explanatory variables, it consists of discrete series and measures relationship with a categorical variable with one or more independent variable. Practical uses range from gets credit risk score m measure the ROI of marketing campaigns etc.

5) SVM – Support Vector Machine is a binary classification algorithm where one needs to hardcore mathematics to determine that how two points on data set are different from each other and the degree of their similarities & differences can be visualized with it.

6) Ensemble – One of the best algorithms to use for getting right predictive model under supervised learning. The advantages of using it are as follows:

• Reduce the degree of biases by taking account various parameters strategically.

• Reducing the variance and hence producing subtle, it’s just done with the handful of scoring techniques based on probabilities.

Math & Data transforming Organisations in 21st Century

We are in the world where data is moving at high velocity over the Internet, people generate new data each moment from various activities like while checking their Facebook feeds to just searching something on Google or even buying a share of a company listed on a stock exchange, each new action creates abundance amount of data in these days. As more and more people get connected to Internet the data size will become in yottabytes in coming year.

The Problem –

So the next big problem that would be faced by the Internet industry will be to create enough amount of storage for the same data and to grow the storage size with respect to it. That’s an arduous task right, so what’s the solution to it.

Thus many companies have understood that to solve this problem they would need quant and machine-learning scientists, thus the researchers or mathematical wizards are in huge demand. Quant analysis with Machine Learning help to create algorithms that convert huge data into compressible small packets, which indeed make optimize the use of server storage space.

MUORO’s Technology:

Using the same underlying fundamentals of machine learning, stats and AI MUORO’S proprietary of dataShelter* helps to make the data smaller through converting it into packets as a result it reduce the storage cost, further it also helps in forming predictive models by doing analytics with your data, thus automates the entire process cycle of the data.

In recent times the leading institutions are suffering from brain drains as well, most of the tech giants are hunting Artificial intelligence & machine learning researchers. Recently BAIDU the Chinese search giant hired Andrew Ng Stanford professor as the company’s Chief Scientist thus to strengthen their machine learning division

Quant in finance:

The scope of quant and machine learning is not limited to only internet industry, in the current scenario, finance industry needs it the most, as now with modern technology, high-frequency trading or black-box trading. Banks and hedge funds need robust algorithms for their risk aversion since the financial crisis banks, insurance firms and hedge funds have become more cautious on quantifying risk and are less keen to develop new products in the form of derivatives.


Hence the rise in the demand for researchers is justified but with this quant solution MUORO one can always get powerful analytics on the go instantly while making machine learning and quant a hassle free infraction of the cost as compared with pay packages of the mathematical researchers.

Top 3 Programming Languages for Machine Learning

Machine learning is a process to build AI enabled algorithms with which machines are able to learn or produce codes automatically through analyzing the given data.

Machine learning is the subset of Artificial Intelligence and again has the intersection with many fields including math and psychology.

Now after giving a brief introduction let’s start with the tech part of the article:

After doing intensive research, I clustered these following languages, but please don’t be afraid to learn the other programming languages because to become a competent programmer and data scientist you must know a dozen of tools to stumble upon one that works the best in a particular situation, hence you can’t restrict yourself to a language or two. Again to mention different jobs are best done in different languages.

 1)   R Language:

This language was developed to as a modern version of S language developed in Bell labs, R language is combined with lexical scooping, which tends to provide the flexibility in producing statistical models. R is a really powerful language to start with machine learning, as there are many specified GNU packages available. One can surely choose to use R for creating powerful algorithms and plus the R studio has an easy statistical visualisation of your algorithms. Though the language is widely used in academic research and gaining really well recognition in the industry use most recently.

2)   Python 

Python language is one of the most flexible languages and can be used for various purposes. Python has gained huge popularity base of this. Python does contain special libraries for machine learning namely scipy and numpy which great for linear algebra and getting to know kernel methods of machine learning. The language is great to use when working with machine learning algorithms and has easy syntax relatively. For beginners, this is the best language to use and to start with.

3)   C language:

The mother of all language is definitely a great programming language to build your predicative algorithms. Developed at Bell Labs by Denise Ritchie (Turing Award winner Computer Scientist). This language is not a cakewalk and should be only be considered when you have strong fundamentals of computer science and programming languages, however, once you are proficient in C language then there is nothing that can stop you developing your advance algorithms. One does not need Ph.D. but knows the computer programming concepts thoroughly.  You can build your own regressions analysis and time series simulation easily, which would create strong machine learning algorithms.


In conclusion, I would like to add that there are many other languages that you can use after going through  the above ones. Once you get deeper you can explore the functional languages like Haskell, Erlang , Julia and Scala, these tools need you to have good knowelge of C first. As a beginner, you can start with Python and move to other languages once you get the command of that.