onsdag 28. desember 2016

How 10 industries are using big data to win big

Two and a half quintillion bytes or 2,500,000,000,000,000,000 bytes. That’s how much data humanity generates every single day. And the amount is increasing; we’ve created 90% of the world’s data in the last two years alone.
It should come as no surprise, then, that businesses today are drowning in data. That’s because much of that data is unstructured; it takes the form of documents, social media content and other qualitative information that doesn’t reside in conventional databases and is can’t be parsed by traditional algorithms or machine analysis.
But, thanks to new cognitive computing services, that’s changing fast.
New Tools, New Insights
Cognitive services not only cut through the deluge of data, but also bring meaning to it through human-like understanding of natural language queries. They’re helping businesses across a broad range of industries respond to the needs of their customers like never before, driving increased revenue while reducing costs.

The industries boosting bottom lines and setting new standards for customer service include telecommunications, manufacturing, fitness, retail, insurance, banking, finance, government, healthcare and the travel industry.
Here’s an overview of how these industries are making all of their data work for them:
1. Telecommunications
A major telecommunications service provider uses cognitive services to index thousands of documents, images and manuals in mere minutes, in order to help 40,000 call center agents solve customer issues more effectively. The company realizes a savings of $1 for every second shaved off the average handling time per call—or $1 million a year.
2. Manufacturing
A specialty sports manufacturer was challenged to fine-tune production in order to eliminate inventory overruns and create better products while saving money. Now, thanks to newly accessible data, the company produces 900 different kinds of skis to match customer personality, preference, and snow conditions—giving customers exactly what they want and meeting the company’s goals for lower inventory and expenses.
3. Retail
Data-driven, personalized customer experiences enabled by cognitive technology are helping a major clothing retailer not only provide outstanding service, but also drive revenue at more than 225 stores. To design a better in-store experience, the company uses sensor and Wi-Fi data to track who comes in, what aisles they visit and for how long. The company also analyzes social media data from millions of followers to improve marketing and product design.
4. Fitness
Cognitive services aren’t just for customer service agents and manufacturers; they can directly serve customers, too. A sports apparel and connected fitness company uses data to power the world’s first cognitive fitness coaching mobile app. The app collects data on users’ workouts, calories burned and more in order to act as a “virtual coach” to help them meet their health goals.
5. Insurance
An international insurance company uses cognitive services to reduce the time needed to process complex claims from two days to just 10 minutes. The company is also using data to identify and eliminate hundreds of millions of dollars in fraud and leakage. The result: a more customer-centric and profitable company.
6. Banking
A consumer banking chain in New Zealand is using data collected and analyzed by cognitive computing to more than double customer engagement online—from 40% to 92%, with a 30% increase in online banking. With a view into customer sentiment as well as data on revenue generation per product held, agents can provide more personalized customer service. Customers can log on to mobile devices to perform more than 120 functions, including applying for a mortgage. As a result, mobile usage is up 45%.
7. Finance
Cognitive technology is empowering the financing arm of a major auto manufacturer to develop insights about more than four million individual customers in seconds. The system combines unstructured content and conventional data from internal and public sources, then displays all meaningful information based on a the user’s job description. Agents can thus provide customers with more comprehensive information faster, and the company can maintain data security.
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8. Government
A U.S. state government is using data to enhance the services delivered to millions of its citizens. Cognitive services enable citizens to quickly and easily search hundreds of thousands of documents, including important new insurance requirements. This allowed the state to achieve its goal of helping citizen navigate more than 1 million pages, while saving tens of thousands of dollars in upgrade costs.
9. Healthcare
A large healthcare company is using data and cognitive computing to extract key insights from unstructured patient medical history—including physician notes and dictation—covering 1.35 million annual outpatient visits, 68,000 hospital admissions and 265,000 emergency room visits. The trends, patterns and other important information captured from this data help clinicians identify patients at risk for chronic disease, critical to both improving treatment and reducing readmission.
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10. Travel
An international airline has found a way to use cognitive services to significantly enhance the customer experience. Flight crews now use mobile devices to access customer data, including allergies, food and seat preferences and previous travel history to offer truly personalized service. To show its customers that it values their information, the airline has launched a first-of-its-kind customer insights program that rewards those who share data by offering them airline miles.
A Vital Competitive Advantage
As the data deluge grows day by day, it presents greater opportunities for companies to drive more personalized and customer-centered service while boosting revenue and efficiency. But these actionable insights will only be available to companies that leverage advanced data analytics and cognitive computing to collect and parse unstructured data. Those who fail to take advantage of cognitive services risk getting left behind.
To learn more about trends in data and cognitive computing download IBM’s Evolution of Enterprise Search Webinar series. 

mandag 19. desember 2016

Modernizing data description


In the recent times, few words (like Robotics, Artificial Intelligence, Analytics, Data Mining, Machine Learning, etc.) are powerful (sometime confusing) in IT industry.

In this competitive world, it is highly important for any software engineer to understand the concepts and usage of the emerging fields. Itz essential to survive in the rapid growth IT industry.

Based on my (l)earning through the premium technology institute and related work experience, I'm writing this article with the strong fundamentals and concepts around it.

Key Areas

In my view, these emerging fields are categorized into 4 key areas. Letz see them in details:

1. Statistics

We all know that Statistics is a study of how to collect, organizes, analyze, and interpret numerical information from data. Statistics can slip into two taxonomy namely:

1. Descriptive Statistics

2. Inferential Statistics

Descriptive statistics involves method of organizing, summering and picturing information from data. Familiar examples are Tables, Graphs, Averages. Descriptive statistics usually involve measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation, etc.)

Inferential statistics invokes method of using information from sample to draw conclusion about the population. Common terminologies are "Margin of error", "Statically Significant".

2. Artificial Intelligence (AI)

AI is a broad term referring to computers and systems that are capable of essentially coming up with solutions to problems on their own. The solutions aren’t hard-coded into the program; instead, the information needed to get to the solution is coded and AI (used often in medical diagnostics) uses the data and calculations to come up with a solution on its own.
As depicted above, AI is the super set of the listed components and so itz a vast area to explore.

3. Machine Learning (ML)

Machine learning is capable of generalizing information from large data sets, and then detects and extrapolates patterns in order to apply that information to new solutions and actions. Obviously, certain parameters must be set up at the beginning of the machine learning process so that the machine is able to find, assess, and act upon new data

4. Data Mining

Data mining is an integral part of coding programs with the information, statistics, and data necessary for AI to create a solution

In the traditional reporting model, the data source is retrospective to look back and examines the exposure of the existing information. Descriptive analytics are useful because they allow us to learn from past behaviors, and understand how they might influence future outcomes.

Inter Connectivity

On connecting the dots of the above said 4 platforms, Artificial Intelligence is the foundation which is followed by Machine Learning, Statistics and Data Mining, chronologically. In simple term, AI (Artificial Intelligence) is the super set of all paradigm.

Artificial Intelligence is a science to develop a system or software to mimic human to respond and behave in a circumference.

Evolution of Statistics, AI, ML and Data Mining is depicted in the below chart.

Need of Chat Bot

On analyzing where people really spend time, you’ll probably get the details where the users are. Chat Bot is the low hanging fruit in terms of business & technical opportunity.

A Chat Bot can be easily built into any major commonly used chat product like Facebook Messenger or Slack. Latest industry data indicates that the end users reached more usage band of messenger apps than social networks, as depicted below:

We've another dimension of Messenger App usage. According to Statista, most popular global mobile messenger apps usage is pointed below, as of April 2016. Itz based on number of monthly active users (in millions).

Next Gen - Messaging

If you think about your daily interactions online, it won’t be that surprising – you use Slack or Skype to communicate with your colleagues at work, you talk to your closest friends on Facebook in Messenger, you probably have several chats with different groups of your friends depending on interests etc.

Chat Bots shift the shopping experience from browsing (web/retail stores) to recommendation. Bots learn about you, much like a trusted friend or personal shopper.

Chat Bot in Business

In Artificial Intelligence, Chat Bot plays a key tool by providing feedback to users on purchases with customer service agents on hand to provide further assistance.

In China, not only is WeChat used by close to two thirds of 16-24 year-old online consumers, but the service has capitalized on its massive market share by offering functionality well beyond simple messaging by attempting to insert itself into as many stations along the purchase journey as possible.

As the major part of digital consumers’ purchase journeys and online lives, Chat Bot will need to be non-intrusive, obviously beneficial to the user and, perhaps most importantly, present themselves as an honest assistant, not an advertisement in disguise.

As the summation of my analysis, 2 key business benefits of Chat Bot usage:

1. High automation in manual contact center business; leads to drastic cost reduction

2. Continuous improvement (on usage) is possible with the usage of Machine Learning in AI intelligent Chat Bot


What you research today may eventually underpin how you deploy a successful Chat Bot application for your business sooner rather than later once all the kinks get worked out. Get ready, folks !!

søndag 11. desember 2016

Personal Finance Application

Make Personal Finance fun again by automated segmentations, benchmarking gamification and automated machine learning budgeting tool.

ELA AS is newly founded startup with a clear mission to make use of data to benefit humanity. #DataForGood is our core value and a hashtag of our activities in social media. We have started with four projects, but we hope to continue with some more:  
1. Personal Finance Digital Assistant which is an add-on solution to your digital bank account that gives you better picture of personal finances based on bench-marking against predefined data set of your segmentation (ex: income range, family members, region you live etc.) and fully automatized data input for all segments: cost, income and balance. This will include a machine learning (ML) algorithm that will suggest you the best way to save and invest money, how to overcome a financial difficulty and how to perform in budgeting your economy the best way.  
We will soon be in Kickstarter and I hope that you will support our project!

The Deception of Supervised Learning

Do models or offline datasets ever really tell us what to do? Most application of supervised learning is predicated on this deception.
Imagine you're a doctor tasked with choosing a cancer therapy. Or a Netflix exec tasked with recommending movies. You have a choice. You could think hard about the problem and come up with some rules. But these rules would be overly simplistic, not personalized to the patient or customer. Alternatively, you could let the data decide what to do!
The ability to programmatically make intelligent decisions by learning complex decision rules from big data is a primary selling point of machine learning. Leaps forward in the predictive accuracy of supervised learning techniques, especially deep learning, now yield classifiers that outperform human predictive accuracy on many tasks. We can guess how an individual will rate a movie, classify images, or recognize speech with jaw-dropping accuracy. So why not make our services smart by letting the data tell us what to do?
Here's the rub.
While the supervised paradigm is but one of several in the machine learning canon, nearly all machine learning deployed in the real world amounts to supervised learning. And supervised learning methods doesn't tell us to doanything. That is, the theory and conception of supervised learning addresses pattern recognition but disregards the notion of interaction with an environment altogether.
[Quick crash course: in supervised learning, we collect a dataset of input-output (X,Y) pairs. The learning algorithm then uses this data to train a model. This model is simply a mapping from inputs to outputs. Now given a new input (such as a [drug,patient] pair), we can predict a likely output (say, 5-year survival). We determine the quality of the model by assessing its performance (say error rate or mean squared error) on hold-out data.]
Now suppose we train a model to predict 5-year survival given some features of the patient and the assigned treatment protocol. The survival model that we train doesn't know why drug A was prescribed to some patients and not others. And it has no way of knowing what will happen when you apply drug A to patients who previously wouldn't have received it. That's because supervised learning relies on the i.i.d. assumption. In short, this means that we expect the future data to be distributed identically like the past. With respect to temporal effects, we assume is that the distribution of data is stationary. But when we introduce a decision protocol based on a machine learning model to the world, we change the world, violating our assumptions. We alter the distribution of future data and thus should expect to invalidate our entire model.
For some tasks, like speech recognition, these concerns seem remote. Use of a voice transcription tool might not, in the short run, change how we speak. But in more dynamic decision-making contexts, the concerns should be paramount. For example, Rich Caruana of Microsoft Research showed a real-life model trained to predict risk of death for pneumonia patients. Presumably this information could be used to aid in triage. The model however, showed that asthma was predictive of lower risk. This was a true correlation in the data, but it owed to the more aggressive treatment such co-morbid patients received. Put simply, a researcher taking actions based on this information would be mistaking correlation for causation. And if a hospital used the risk score for triage, they would actually recklessly put the asthma patients at risk, thus invalidating the learned model model.
Supervised models can't tell us what to do because they fundamentally ignore the entire idea of an action. So what do people mean when they say that they act based on a model? Or when they say that the model (or the data) tells them what to do? How is Facebook's newsfeed algorithm curating stories? How is Netflix's recommender system curating movies?
Usually this means that we strap on some ad-hoc decision protocol to a predictive model. Say we have a model that takes a patient and a drug and predicts the probability of survival. A typical ad hoc rule might say that we should give the drug that maximizes the predicted probability of survival.
But this classifier is contingent on the historical standard of care. For one drug, a model might predict better outcomes because the drug truly causes better outcomes. But for others causality might be reversed, or the association might owe to unobserved factors. These kinds of actions encode ungrounded assumptions mistaking correlative association for causal relationships. While oncologists are not so reckless as to employ this reasoning willy-nilly, it's precisely the logic that underlies less consequential recommender systems all over the internet. Netflix doesn't account for how its recommendations influence your viewing habits, and Facebook's algorithms likely don't account for the effects of curation on reader behavior.
The failure to account for causality or interaction with the environment are but two among many deceptions underlying the modern use of supervised learning. Other, less fundamental, issues abound. For example, we often optimize surrogate objectives that only faintly resemble our true objectives. Search engines assume that mouse clicks indicate accurately answered queries. This means that when, in a momentary lapse of spine, you click on a celebrity break-up story after searching for an egg-salad recipe, the model registers a job a well done.
Some other issues to heap on the laundry list of common deceptions:
  • Disregarding real-life cost-sensitivity
  • Erroneous interpretation of predicted probabilities as quantifications of uncertainty
  • Ignoring differences between constructed training sets and real world data
The overarching point here is that problem formulation for most machine learning systems can be badly mismatched against the real-world problems we're trying to solve. As detailed in my recent paper, The Mythos of Model Interpretability, it's this mismatch that leads people to wonder whether they can "trust" machine learning models.
Some machine learners suggest that the desire for an interpretation will pass - that it reflects an unease which will abate if the models are "good enough". But good enough at what? Minimizing cross-entropy loss on a surrogate task on a toy-dataset in a model that fundamentally ignores the decision-making process for which a model will be deployed? The suggestion is naive, but understandable. It reflects the years that many machine learners have spent single-mindedly focused on isolated tasks like image recognition. This focus was reasonable because these offline tasks were fundamental obstacles themselves, even absent the complication of reality. But as a result, reality is a relatively new concept to a machine learning community that increasingly rubs up against it.
So where do we go from here?
Model Interpretability
One solution is to go ahead and throw caution to the wind but then to interrogate the models to see if they're behaving acceptably. These efforts seek to interpret models to mitigate the mismatch between real and optimized objectives. The idea behind most work in interpretability is that in addition to the predictions required by our evaluation metrics, models should yield some additional information, which we term an interpretation. Interpretations can come in many varieties, notably transparency and post-hoc interpretability. The idea behind transparency is that we can introspect the model and determine precisely what it's doing. Unfortunately, the most useful models aren't especially transparent. Post-hoc interpretations, on the other hand, address techniques to extract explanations, even those from models we can't quite introspect. In the Mythos paper (, I offer a broad taxonomy of both the objectives and techniques for interpreting supervised models.
Upgrade to More Sophisticated Paradigms of Learning
Another solution might be to close the gap between the real and modeled objectives. Some problems, like cost sensitivity, can be addressed within the supervised learning paradigm. Others, like causality, might require us to pursue fundamentally more powerful models of learning. Reinforcement learning (RL), for example, directly models an agent acting within a sequential decision making process. The framework captures the causal effects of taking actions and accounts for a distribution of data that changes per modifications to the policy. Unfortunately, practical RL techniques for sequential decision-making have only been reduced to practice on toy problems with relatively small action-spaces. Notable advances include Google Deepmind's Atari and Go-playing agents.
Several papers by groups including Steve Young's lab at Cambridge (paper), the research team at Montreal startup Maluuba (, and my own work with Microsoft Research's Deep Learning team (, seek to extend this progress into the more practically useful realm of dialogue systems.
Using RL in critical settings like medical care poses its own thorny set of problems. For example, RL agents typically learn by exploration. You could think of exploration as running an experiment. Just like a doctor might run a randomized trial, the RL agent periodically takes randomized actions, using the information gained to guide continued improvement of its policy. But when is it OK to run experiments with human subjects? To do any research on human subjects, even the most respected researchers are required to submit to an ethics board. Can we then turn relatively imbecilic agents loose to experiment on human subjects absent oversight?
Supervised learning is simultaneously unacceptable, inadequate, and yet, at present, the most powerful tool at our disposal. While it's only reasonable to pillory the paradigm with criticism, it remains nonetheless the most practically useful tool around. Nonetheless I'd propose the following takeaways:
  1. We should aspire to unseat the primacy of strictly supervised solutions. Improvements in reinforcement learning offer a promising alternative.
  2. Even within the supervised learning paradigm, we should work harder to eliminate those flaws of problem formulation that are avoidable.
  3. We should remain suspicious of the behavior of live systems, and devise mechanisms to both understand them and provide guard-rails to protect against unacceptable outcomes.
Zachary Chase LiptonZachary Chase Lipton is a PhD student in the Computer Science Engineering department at the University of California, San Diego. He is interested in both theoretical foundations and applications of machine learning. In addition to his work at UCSD, he has interned at Microsoft Research Labs and as a Machine Learning Scientist at Amazon, and is a Contributing Editor at KDnuggets.

mandag 28. november 2016

Start-up of the week: Instalocate- A chatbot that claims to make your travel more comfortable!

Img Source: Instalocate |

Did you know that every time your flight gets delayed your airlines owes you a compensation? Have you ever been denied boarding because the flight was overbooked? Are you aware of your rights as a flyer? Many a times we overlook on these issues and incur heavy losses, but not anymore. The one company founded by Stanford University and Indian Institute of Management (IIM) alumni in June 2016, is building an AI powered travel assistant just for you!

Instalocate– the name as it goes by – promises to watch all that for you by building a cutting-edge technology that can solve all your travel problems and make your journey comfortable. No more panicking and rushing to the airline counters, standing in long queues or calling the customer care if your flight gets delayed or baggages do not come on time! Instalocate promises to constantly monitor your travel and predict and solve the travel problems.

Not just that, it would also protect your rights as a customer and go after airlines to get your due compensation in case of any mishap.

How wonderful is that? Having a digital personal assistant that can make your journey comfortable and be always there to answer all your questions in an instant!

Talking to AIM, one of its founders Pallavi Singh revealed that the idea of Instalocate was conceived out of all the unfortunate incidences that she and her husband had personally faced.

“Anything that can go wrong has gone wrong with us. Flights have gotten delayed, we have missed connections, baggage was lost. And that’s when we realised that, most of the travel apps are working in pre-booking and there is no one to help you when things like this go wrong. Dealing with the airlines was the biggest nightmare amidst this”, she said.

And that’s how the journey to Instalocate took off with an idea of building an assistant which could help during the travel woes and deals with the airline on your behalf. Pallavi confesses “At so many times, we felt so frustrated with the airlines that we wanted to sue them for compensation, for all the trouble we went through. But we never did- mainly because we never had the time to deal with the airlines.”

With Instalocate, all you have to do is share your flight details and it will predict when you might need something and would send the contextual information automatically. Just ask your assistant anything from your flight status to the free Wi-Fi availability in the airport! That’s not all, if your family is worried about you, the assistant can pinpoint your exact location in the air. They don’t have to anxiously wait outside the airport checking their phones again and again! After reaching your destination, your cab will be waiting for you.

How is all of it achieved? Talking about the integration of artificial intelligence to Instalocate, Pallavi said “It is a predictive engine which will predict when the airlines owe you compensation. Unlike others we don’t wait for you to search for that information rather we will bring it to you. We are also building in-house NLP which makes it easier for an end user to talk to us, just as they would talk to a friend.”

There is no doubt that the bot has been received well by its users. “We have only launched our first product and the people are loving it”, marked Pallavi. Citing a use case, she said “One of our power users recently got 800 dollars from British Airways for flight delay with the help of Instalocate.”

However, the journey to its popularity was not easy. Pallavi notes that making was not as challenging as marketing. “Bots is still a new concept for people and popularizing it is a big problem”, she added.

Well, despite the challenges, Instalocate has done quite well for itself and is growing at a rate of 60 month over month with a pretty high retention rate. 

This digital personal assistant is available to make your journey comfortable and answer your questions in an instant. Talk to Instalocate within facebook at for a hassle-free travel now. There is no need to install the app separately, which adds to the many perks this travel bot has!

søndag 27. november 2016

The company that perfects Data Visualization in Virtual Reality will be the next Unicorn

Fortune 500 companies are investing staggering amounts into data visualization. Many have opted for Tableau, Qlik, MicroStrategy, etc. but some have created their own in HTML5, full stack JavaScript, Python, and R. Leading CIOs and CTOs are obsessed with being the first adopters in whatever is next in data visualization.

The next frontier in data visualization is clearly immersive experiences. The 2014 paper "Immersive and Collaborative Data Visualization Using Virtual Reality Platforms" written by CalTech astronomers is a staggeringly large step in the right direction. In fact, I am shocked that 1 year later I have not seen a commercial application of this technology. You can read it here:

The key theme that I hear at technology conferences lately is the need to focus on analytics, visualization and data exploration. The advent of big data systems such as Hadoop and Spark has made it.

Picture Source: VR 2015 IEEE Virtual Reality International Conference

possible - for the first time ever - to store Petabytes of data on commodity hardware and process this data, as needed, in a fault tolerant and incredibly quick fashion. Many of us fail to understand the full implications of this inflection point in the history of computing.

Storage is decreasing in cost every year, to the point where you can now have multiple GB on a USB drive that 10 years ago you could only store a few MBs. Gigabit internet is being installed in cities all over the world. Spark uses the concept of in memory distributed computation to perform at 10X map reduce for gigantic datasets and is already being used in production by Fortune 50 companies. Tableau, Qlik, MicroStrategy, Domo, etc. have gained tremendous market share as companies that have implemented Hadoop components such as HDFS, Hbase, Hive, Pig, and Map Reduce are starting to wonder "How I can I visualize that data?"

Now think about VR - probably the hottest field in technology at this moment. It has been more than a year since Facebook bought Oculus for 2Billion and we have seen Google Cardboard burst onto the scene. Applications from media companies like the NY Times are already becoming part of our every day lives. This month at the CES show in Las Vegas, dozens of companies were showcasing virtual reality platforms that improve on the state of the art and allow for a motion-sickness free immersive experience.

All of this combines into my primary hypothesis - this is a great time to start a company that would provide the capability for immersive data visualization environments to businesses and consumers. I personally believe that businesses and government agencies would be the first to fully engage in this space on the data side, but there is clearly an opportunity in gaming on the consumer side.

Personally, I have been so taken by the potential of this idea that I wrote a post in this blog about the “feeling” of being in one of these immersive VR worlds.

The post describes what it would be like to experience data with not only vision, but touch and sound and even smell.

Just think about the possibilities of examining streaming data sets, that currently are being analyzed with tools such as Storm, Kafka, Flink, and Spark Streaming as a river flowing under you!

The strength of the water can describe the speed of the data intake, or any other variable that is represented by a flow - stock market prices come to mind.

The possibilities for immersive data experiences are absolutely astonishing. The CalTech astronomers have already taken the first step in that direction, and perhaps there is a company out there that is already taking the next step. That being said, if this sounds like an exciting venture to you, DM me on twitter @beskotw and we can talk.

onsdag 24. august 2016

Laptop for data science

What are the laptops which are most suited for data scientists and analysts?

As we deal with heavy computations and also need to generate visualizations, something which can take the toll of it, would be recommended.

Would be preferred if it can help in handling Big Data analytics too.

Even though the analytics is done in the Map Reduce framework (or distributed computing), yet the computations are heavy and time taking and also slows down the laptop in most cases.

So, a laptop with features and OS which is most suited to handle such things gracefully is recommended.

[Price not an issue]

As I am pretty much in the same situation, here are what I look for:

SSD: since you'll likely perform many I/O on large data sets. 1 TB is my bottom line.
RAM: since it's often more convenient and much faster to keep data sets (or part of it) in memory. 16 GB is really bottom line.
GPU: Nvidia is sometimes preferable over AMD as it tends to be more supported (e.g. for neural network libraries). I had to get a MBP M2014 instead of M2015 because the latter had AMD while the former has Nvidia, and I need to use Theano.
OS: Linux tend to have more libraries (but since it doesn't have any decent speech engine software I personally use Microsoft Windows, using Linux in VM or in server).
CPU: hasn't evolved much over the last few years... some i7 3rd or 4th generation is standard.

As it's often cheaper to add SSD and RAM oneself, I tend upgrade mid-spec laptops.

If price isn't an issue, you can have a look at those overpriced Alienwares. For people who are more budget conscious, just check to what extend the laptop is upgradable (e.g. max RAM + number of SSD slots). In the US, I like Xotic PC as the max specs are clearly defined.

torsdag 18. august 2016

My life in Norway: Pursuing the dream

Born and raised in Macedonia, spent 4-5 years in Kosova and then migrated first time in Norway. My family was one of the few interested in science, specially in math, where my father was a math professor and most of my uncles studied math or engineering. I inherited the love to science and math, continued developing my self focused in math by becoming one the best in local, national and international competition of both math and physics (kind of applied mathematics).
Studied computer technology at University of Prishtina and 3 year in row won the University scholarship.
Studied with International professors from Concordia University; Vienna Institute of Technology and Institute Jean Lui Vives.

Even physically in Kosova, my dream was just to move to a more prospered countries to pursue my dream of being a great scientist. I have heard of UK, US and the big american dream, but never thought of Norway....

I moved in Norway some years ago and then I come back May 2010, pursuing my dream for a better career. I never thought that this will the time when the Revolution of my life started. I will never forget the time when I was sitting home and got a call that was actually a job opportunity to work in Norway, to work for one the best companies in the World, Nordic Choice Hotels. I answered with BIG YES and came to the first interview. It was all by the plan, the first interview was successful. Waited in Oslo for a couple of days, where I got invitation for the second round which was decisive. One day after that, I got the call of my career, saying the your job opportunity is now a job offer. Without hesitating I said YES and that was the biggest "yes" of my life, because what happened after proved this conclusion. Still not understanding in what wonderful world I was stepping in.

After signing the contract and some official paper work I started to work in June/July. I was thrilling to start with my new company and bring the successful project of Business Intelligence into live.I had time read and understand the business concept and strategy of Nordic Choice Hotels, so I was ready to dive in directly to the solution.

One of the biggest highlights of my career here is meeting the owner of Nordic Choice Hotels and bunch of other business around Norway, Mr. Petter A. Stordalen. His ability to give energy at any time in the company was special. You could feel his absence or his presence without seeing him at all.

Me and Petter Stordalen at Garden Party

During the time being at Choice, I had the opportunity to meet other important people as well, so I learned a lot from them.
Me and my department made great efforts on creating the best BI solution for the company in a given condition and situation. So we excelled by creating this solution presented in the video: 

A Visionary Choice - Nordic Choice Hotels Business Intelligence vision from Platon Deloitte on Vimeo.

But things came to an end, sometimes without our willing, so in April I had to change my job and pursue my professional dream at Sopra Steria AS

Sopra Steria is trusted by leading private and public organisations to deliver successful transformation programmes that address their most complex and critical business challenges. Combining high quality and performance services, added-value and innovation, Sopra Steria enables its clients to make the best use of information technology.
We have a strong local presence across the UK with around 6,700 people in locations in England, Scotland, Wales and Northern Ireland. Sopra Steria supports businesses in the full technology lifecycle - from the definition of strategies through to their implementation. We add value through our expertise in major projects, knowledge of our clients' specific businesses, expertise in technologies and a broad European presence.
Sopra Steria Group, a European leader of digital transformation, was established in September 2014 as a merger of Sopra with Steria. See the timeline for both companies showing the milestones achieved over nearly 50 years before becoming a single entity.

Brief Professional Summary
I am an IT professional with focus on Business and Data Analytic, prefer to call myself Data Scientist. I have in depth experience using and implementing business intelligence/data analysis tools with greatest strength in the Microsoft SQL Server / Business Intelligence Studio SSIS, SSAS, SSRS. I have designed, developed, tested, debugged, and documented Analysis and Reporting processes for enterprise wide data warehouse implementations using the SQL Server / BI Suite. I also have designed/modeled OLAP cubes using SSAS and developed them using MS SQL BIDS SSAS and MDX. Served as an implementation team member where I translated source mapping documents and reporting requirements into dimensional data models. Strong ability to work closely with business and technical teams to understand, document, design and code SSAS, MDX, DMX, DAX abd ETL processes, along with the ability to effectively interact with all levels of an organization. Additional BI tool experience includes ProClarity, Microsoft Performance Point, MS Office Excel and MS SharePoint.

Professional highlights as DATA SCIENTIST:

1. Worked for Capgemini Norway AS

2. Worked for Nordic Choice Hotels AS

3. Working for SopraSteria AS

4. Working on a StartUp ELA AS

Academic Honors:

MIT Honor Code Certificate: CS and Programming, BigData (04.06.2013)

Princeton University Honor Code Certificate:  Analytic Combinatorics (10.07.2013)

Stanford University Honor Code Certificate: Mathematical Thinking,, Cryptography (06.05.2013)

The University of California At Berkeley Honor Code Certificate: Descriptive Statistics

IIT University Honor Code Certificate: Web Intelligence and Big Data (02.06.2013)

Wesleyan UniversityPassion Driven Statistics (20.05.2013)

Google Analytics Certified

Career Highlights:

1. Over Nine years of experience in the field of Information Technology, System Analysis and Design, Data
    warehousing, Business Intelligence and Data Science in general

2. Experienced in implementing / managing large scale complex projects involving multiple stakeholders and
    leading and directing multiple project teams

3. Track record of delivering customer focused, well planned, quality products on time, while adapting to
    shifting and conflicting demands and priorities.

4. Experience in Data warehouse / Business Intelligence developments, implementation and operation setup

5. Expertise in Data Modeling, Data Analytics and Predictive Analytics SSAS, MDX and DMX

6. Strong Knowledge in Data warehouse, Data Extraction, Transformation, and Loading ETL

7. Excellent track record in developing and maintaining enterprise wide web based report systems and portals in Finance, Enterprise wide solutions and BI and Strategy Systems

8. Best new employee for 2011 of Nordic Choice Hotels AS


1. First place in regional math competitions in 2 years in a row

2. First place in Physics competition in a Balkaniada (Balkan Olympics in Theoretical Physics)

3. First place in fast math competition in International Kangourou Competition

4. Gold Medalist in Microsoft Virtual Academy (Microsoft Business Intelligence)

5. 2 times finalist as the best Business Intelligence solution:

Research Work:

1. Riccati Differential Equation solution (published in printed version Research Journal)

3. Personal Finance Intelligence; published in IJSER 8 August 2012 edition

mandag 15. august 2016

The Data Science Process 1/3

Congratulations! You’ve just been hired for your first job as a data scientist at Hotshot Inc., a startup in San Francisco that is the toast of Silicon Valley. It’s your first day at work. You’re excited to go and crunch some data and wow everyone around you with the insights you discover. But where do you start?
Over the (deliciously catered) lunch, you run into the VP of Sales at Hotshot Inc., introduce yourself and ask her, “What kinds of data challenges do you think I should be working on?”
The VP of Sales thinks carefully. You’re on the edge of your seat, waiting for her answer, the answer that will tell you exactly how you’re going to have this massive impact on the company of your dreams.
And she says, “Can you help us optimize our sales funnel and improve our conversion rates?”
The first thought that comes to your mind is: What? Is that a data science problem? You didn’t even mention the word ‘data’. What do I need to analyze? What does this mean?
Fortunately, your mentor data scientists have warned you already: this initial ambiguity is a regular situation that data scientists in industry encounter. All you have to do is systematically apply the data science process to figure out exactly what you need to do.
The data science process: a quick outline
When a non-technical supervisor asks you to solve a data problem, the description of your task can be quite ambiguous at first. It is up to you, as the data scientist, to translate the task into a concrete problem, figure out how to solve it and present the solution back to all of your stakeholders. We call the steps involved in this workflow the “Data Science Process.” This process involves several important steps:
  • Frame the problem: Who is your client? What exactly is the client asking you to solve? How can you translate their ambiguous request into a concrete, well-defined problem?
  • Collect the raw data needed to solve the problem: Is this data already available? If so, what parts of the data are useful? If not, what more data do you need? What kind of resources (time, money, infrastructure) would it take to collect this data in a usable form?
  • Process the data (data wrangling): Real, raw data is rarely usable out of the box. There are errors in data collection, corrupt records, missing values and many other challenges you will have to manage. You will first need to clean the data to convert it to a form that you can further analyze.
  • Explore the data: Once you have cleaned the data, you have to understand the information contained within at a high level. What kinds of obvious trends or correlations do you see in the data? What are the high-level characteristics and are any of them more significant than others?
  • Perform in-depth analysis (machine learning, statistical models, algorithms): This step is usually the meat of your project,where you apply all the cutting-edge machinery of data analysis to unearth high-value insights and predictions.
  • Communicate results of the analysis: All the analysis and technical results that you come up with are of little value unless you can explain to your stakeholders what they mean, in a way that’s comprehensible and compelling. Data storytelling is a critical and underrated skill that you will build and use here.
So how can you help the VP of Sales at Hotshot Inc.? In the next few emails, we will walk you through each step in the data science process, showing you how it plays out in practice. Stay tuned!

torsdag 11. august 2016

Last day(s) to participate for a chance to win a FREE space at our Data Science Boot Camp

A free place on our pioneering Data Science Boot Camp training programme is being offered by specialist recruitment agency, MBN Solutions. Places on the much-anticipated course, aimed at upskilling those with raw analytical grounding into bona fide data scientists, are worth £7,000. The average cost of recruiting a data science specialist is £15,000.
The Data Lab has partnered with New York’s globally renowned, The Data Incubator (whose courses are reputedly harder to get into than Harvard), to develop the three-week data Boot Camp as part of a drive to plug the nation’s data skills gap. It is aimed at helping to unlock the economic potential of data to Scotland, estimated to be worth £17 billion* in Scotland alone.
To apply for the MBN Solutions sponsored place, potential participants need to submit a video explaining how they would use the data science Boot Camp training in their current organisations. The video should be maximum two minutes and include:
  • Your current role and experience
  • Why you want to take part in the course
  • Why you believe improving your skills in data science is important
  • How you hope to use the skills you will learn in the course to improve your work 
  • What impact do you expect to achieve for your organisation as a result of your skills
The video must be uploaded to YouTube, the link to the video sent to by 12th August.
Michael Young, CEO of MBN Solutions, said: “With the average cost of recruiting a data scientist £15,000, the Boot Camp presents an incredible opportunity to upskill current staff and invest in your company’s data science offering.
“The Data Incubator is recognised as the go-to experts in the data training sector globally and, by sponsoring a place for a budding data scientist, we are helping to enhance Scotland’s pipeline of data science talent.
“Every day we see fantastic, innovative data science projects going on in our client’s organisations, Scotland is leading the way in data science in the UK and The Data Lab are really driving the data agenda forward. Some countries are only just waking up to the potential of data. This course marks a really exciting time for Scotland and The Data Lab and we at MBN Solutions are thrilled to be a part of it.”
Brian Hills, Head of Data at The Data Lab, said: “We’re very pleased to have MBN Solutions sponsor a place on the Boot Camp which will take us one step closer to exploiting the data opportunity in great demand and short supply.
“It is going to be an incredible three weeks with attendees gaining a highly sought after data science skillset and learnings from world-leaders in data science.
“It’s crucial Scotland remains ahead of the curve in data science. By investing in our pipeline of talent and learning from international experts, we are securing our future and taking critical steps toward exploiting the data potential available here in Scotland.”
The pioneering training initiative will allow Scottish businesses to fast track potential returns by using data analysis to drive insight and decision-making across industry. There are only a few places left for the Boot Camp which will take place in September in Edinburgh. It will focus on developing practical application skills such as advanced python, machine learning and data visualisation in a collaborative environment.
For further information on the Boot Camp, how to apply, and how to enter the competition, please check out our Boot camp pagedownload our brochure or email

About The Data Incubator

The Data Incubator is data science education company based in NYC, DC, and SF with both corporate training and hiring offerings. They leverage real world business cases to offer customized, in-house training solutions in data and analytics. They also offer partners the opportunity to hire from their 8 week fellowship training PhDs to become data scientists. The fellowship selects 2% of its 2000+ quarterly applicants and is free for fellows. Hiring companies (including EBay, Capital One, AIG, and Genentech) pay a recruiting fee only if they successfully hire. You can read more about The Data Incubator on Harvard Business Review, VentureBeat, or The Next Web, or read about their alumni at Palantir or the NYTimes.

About MBN Solutions

In a field saturated by many lookalike recruitment consultancies, MBN is a truly different business. Priding ourselves on values of deep, real subject matter knowledge in the Data Science, Big Data, Analytics and Technology space, a passionate approach to developing our own consultants and a strategy placing our clients at the heart of our business, MBN are a true market defining ‘People Solutions’ business.

Another approach to Personal Finance

Re-Inventing Personal Finance using Data Science

Existing software and new approach

Usually existing Personal Finance applications are boring, because they are all dependent of manually input of your data, in right segment, the right amount, just boooring. In addition, you can count on manual input fails together with the impossibility of live update your financial status, to make it even worst experience. These and many other reasons make the existing Personal Finance applications nearly useless.

To avoid manual input of data into your application, you need a live feed from your transaction data (credit card usage, bank payments etc...) and only manual input for cash amounts. However, cash is very small problem, as we tend to avoid it as much as possible and instead we mostly buy with electrons.

Most of the banks offer to their customers a digital bank account where all the transactions are visible and that can the best source to avoid manual input. So, why we do not ask for built-inn application that will serve as Personal Financial app with even more possibilities to serve you.

The solution

This application can save lives, can make you better at your personal finance, can avoid financial crisis and help banks get better understanding of you as customer. It is not only you as a person that benefits, but the entire society and even the bank itself. Bank can have much better credit scoring for their customers and can avoid risky loans, risky bank interests for a particular customer etc…

To build (in) this app we need to consider many things and specially the approach that Business Intelligence solutions can serve to us, but keeping in mind security and impersonation as we work with very critical data.

Therefore, I am delighted to represent you PFI that stands for Personal Finance Intelligence, which represents a non-usual approach to Personal Finance solutions existing in market today.

Personal Finance Intelligence (PFI) aims to be a built-in Business Intelligence application inside your digital bank service to serve you as personal finance and budget planner assistant.

Inspired by a Norwegian TV Show “Luksusfellen”, this Business Intelligence app approach may be a solution for all these who fail to maintain well their own economy, and for those who want to perform their economy, save more and last but not least the bank itself.
The fundament of this concept is a Customer Analytics Data Center that would have the power process data on the transaction level. The duty of the data center will be to collect, structure, clean, model and present the data to the bank customers as usual Personal Finance application do, but in addition, data will be updated automatically. This is the reporting (presentation) layer of your financial status (picture), but this application can offer you much more and here is why!

In addition to a standard PF application, this solution include also bench-marking against an standardized customer (Ola Norman) that represent the data set of Min, Max or Average segmented by customer's choice and properties, for example: How I stand against customers from 28-35 years old, from east Oslo, in buying food and beverage this month?
To have more control and plan well your own economy, targeting will be an integrated service inside application where users (bank customers) can put their targets (manually) for costs or income or can leave the application algorithm fill that with projection based on each customer historical data. You can activate a flagging service, so you are warned when approaching certain limits in your expenses and run algorithm to optimize the use of remaining budget and you do not get broke.

Big Data can help make it even better

Last technological findings can make this approach even more interesting and meaningful. Imagine what Big Data and Data Science can do by adding external data for customers that will allow opening of their social media accounts to the bank application. Social media behavior is very important and can bring very important segmentation inside customer categorizations.

Machine learning algorithms can help make the decision and budgeting much better based on other decisions and budgeting techniques.


As I mentioned before, data impersonation and security are a showstopper as we are going to work with bench marking data sets that implies set of other customer’s data. Here we can have a potential data leak from one customer to another, so our system must ensure consistency in both sides and the bank system has everything under control. Transaction details of customers can make banks expose their ‘hidden’ costs and fees. Many banks will hesitate to offer this service to their customers just because of this; in other side customers have legitimate right to have such information.


Beneficiary to this approach are not only the customers and world economy, but also the bank itself in cases when they want to perform customer evaluation (credit check) and behave reaction to certain financial statuses. Today’s Credit scoring system lacks on better decisions because they miss important data.

I am on the way to build business concept and the technical architecture of this approach. My team and I would love to share this approach on details, including implementation, if any company, association or bank in the world is interested to offer this service to their customers. This can be the best preventive for World financial system to stay sustainable and not crush as it did before.

© Copyright All rights reserved to Besim Ismaili 03051982

Oslo, January 2015