IBM Watson Analytics works well for business managers !


IBM Watson Analytics was released at 4th Dec 2014.  This is new service where data analysis can be done with conversations and no programming is needed.  I am very interested in this service so I opened my account of IBM Watson Analytics and reviewed it for a week. I would like to make sure how this service works and whether it is good for business manager with no data analysis expertise. Here is a report for that.


I think IBM Watson Analytics is good for beginners of data analysis because it is easy to visualize data and we can do predictive analysis without programming the codes. I used the data which includes  score of exam1, exam2 and results of admission.  This data can be obtained at Exercise 2 of Machine Learning at coursera.  Here is the chart drawn by IBM Watson Analytics. In order to draw this chart, All have to do is uploading data, write or choose “what is the relationship between Exam1 and Exam2 by result”, and adjust some options in red box below. In the chart,  green point means ‘admitted’ and blue point means ‘not admitted’. Therefore it enable us to understand what the data means easily.



Let us move on prediction.  We can analyze data in details here because statistical models are running behind it.  I decided “result” is a target in this analysis.   This target is categorical as it includes only “1:admitted and 0:not admitted” so logistic regression model, which is one of the classification analysis, is chosen automatically by IBM Watson Analytics.  Here is the results of this analysis. In the red box, explanations about this analysis is presented automatically. According to the matrix about score of each exam, we can estimate probability of admission. It is good for business manager as this kind of analysis usually requires  programming with R or MATLAB, python.



In my view, logistic regression is the first model to learn classification because it is easy to understand and can be applied to a lot of fields. For example I used this model to analyze how the counter parties are likely to be in default when I worked at financial industries.  For marketing,  the target can be interpreted as buy the product or not.  For maintenance of machines,  the target can be interpreted as normal or fail. The more data are corrected, the more we can apply this classification analysis to. I hope many business managers can be familiar with logistic regression by using IBM Watson Analytics.

IBM Watson Analytics has just started now so improvements may be needed to make the service better. However, it is also true that business manager can analyze data without programming by using IBM Watson Analytics.  I would like to highly appreciate the efforts made by IBM.



Note:IBM, IBM Watson Analytics, the IBM logo are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. 

What is the best language for data analysis in 2015 ?




RedMonk issued the raking about popularity of programming languages. This research is conducted periodically since 2010. This chart below is coming from this research. Although general purpose languages such as JavaScript occupy top 10 ranking,  statistical language is getting popular.  R is ranked 13th and MATLAB is ranked 16th. I have used MATLAB since 2001 and R since 2013 and currently study JavaScript. Then I found that the deference between R, which is statistical language, and other general purpose languages. Let us consider it in details and good way to learn statistical languages such as R and MATLAB.


languages 2015


1.  R focuses on data

Because R is a statistical language,  it focuses on data to be analyzed.  These data are handled in R as vectors and matrices. Unlike JavaScript, there is no need to define variables to handle data in R. There is no need to distinguish between scalar and vector, either.  So it is easy to start analyzing data with R, especially for beginners. Therefore I think the best way to learn R is to be familiar with vectors and matrices because data is represented as vectors or matrices in R.


2.  R has a lot of functions to analyze data

R has a lot of functions because many professionals contribute to develop statistical models with R. Currently there are more than 7000 functions, which are called “R package”. This is one of the biggest advantages to learn R for data analysis. If you are interested in “liner regression model” , which is the most simple model to predict price of services and goods,  all you have to do is just writing command “lm” then R can output the parameters so that predictions of prices can be obtained.


3. R is easy to visualize data

If you would like to draw the graph,  all you have to do is to write the code ‘plot’ then simple graph appears on the screen.  When there are a lot of series of data and you would like to know relationship among each of them and other,  all you have to do is to write the code ‘pairs’ then a lot of scatter charts appear so that we can understand the relationship among each of them.  Please look at the example of charts by “pairs”.



R is open source and free to anyone. However MATLAB is proprietary software.  It means that you should buy licenses of MATLAB if you would like to use it. But do not worry about that. Octave, which is similar to MATLAB, is available without license fee as an open source software.  I recommend you to use R or Octave for beginners of data analysis because there is no need to pay any fee.

Going forward, R must be more popular in programming languages. It is available for everyone without any cost.  R is introduced as a major language for data analysis in my company and I would recommend all of you to learn R as I do.  Is it fun, isn’t it?

Mobile and Machine learning can be good friends in 2015 !



Number of mobile devices will be increasing drastically in the emerging markets in 2015. One of the biggest reason why it is increasing is that good smart phones are affordable because of competitions among the suppliers such as Google, Samsun and Xiaomi.  It is good for people in the emerging countries because a lot of people can have their own personal devices and enjoy the same internet life as people in developed countries do. I hope everyone all over the world will be able to be connected to the internet in near future.

Not only the number of mobile devices but the quality of its services will be changed dramatically in 2015 because machine learning will be available for commercial purpose. Let us consider this change more details. The key idea behind this is “Shift from judgement by ourselves to judgement by machines”.


1.  Machine Learning

Machine Learning has a power to change every industry. With machine learning,  computers can identify certain objects in images and video,  understand conversations with us and read the documents written in natural languages.  It means that most of information around us can be interpreted by computers.  Not only numerical data but also other kinds of information are understood by computers.  This changes landscape of every industry completely.  Computers can make business decisions and all we have to do is just to monitor it.  It already happened in the field of assessing credit worthiness of the customers  in banks many years ago.  Same things will happen in all industries near future.


2. Data

In emerging markets, more and more mobile phones will be sold so that every person might own his or her device in near future. It means that people all over the world will be connected through the internet and more information are collected in real-time basis.  In addition to that a lot of automobiles, homes and parts are also connected through the internet and send the information in real-time basis, too.  Therefore we can realize when and where they are and what condition of each is in real-time basis.  So maintenance for parts will be done as soon as it is needed and optimizations of resources used by people can be achieved as we can get such information in real-time basis.


3. Output

Output from computers will be sent to mobile devices of each responsible personnel  in real-time basis. So there is no need to stay in office during working-time as we can be notified wherever we are. It raises productivity of our jobs a lot. No need to wait for notifications of outputs from computers in office anymore.


Yes, my company is highly interested in the progress of machine learning for the commercial purpose. I would like to watch it closely.  I also would like to develop new services based on machine learning on mobile devices going forward.

Can we talk to computers without programming language?


IBM announced that Watson analytics provides us data analysis and visualization as a service without programming at 4th Dec 2014. It said that “breakthrough natural language-based cognitive service that can provide instant access to powerful predictive and visual analytic tools for businesses, is available in beta”.  Let us consider what kind of impacts IBM Watson analytics provides us.


Watson analytics is good at doing natural language processing.  For example,  if doctors ask Watson analytics how to cure the disease, Watson analytics understand the questions from doctors, research massive data and answer the questions. There is no need to program codes by doctors. It means that we may change from “we should learn computer programming” to “we should know how to have a conversation with computers”.  It may enable a lot of non-programming persons to use computers effectively.

In addition to that,  Watson analytics is also good at handling unstructured data.  These data include text, image, voice and video.  Therefore Watson analytics can analyze e-mail, social media contents, pictures taken by consumers.  So It may be possible to recommend what we should eat at restaurants by taking pictures of menus there, because computers have our health data and they can choose the best meals for our health by analyzing the pictures of menus.

In terms of algorithm,  these functionalities above can be achieved by machine learning.  So the more people start using this service, the more accurate answers by computers are because computers learn from a lot of data and are getting better.


IBM Watson analytics may change the landscape of every industry.  Traditionally data analysis can be executed by data scientists, using numerical data and programming languages. However this new kind of data analysis by IBM Watson analytics,  data analysis can be executed by businessmen/women, using e-mail, pictures and video and natural languages.  Machine translation from one language to another will be also available therefore there are less language barrier going forward.  This must be democratization for data analysis. It is exciting when it happens in 2015 !


Note:IBM, the IBM logo are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. 

Mobile services will be enhanced by machine learning dramatically in 2015, part 2


Happy new year !   At the beginning of 2015,  it is a good time to consider what will happen in the fields of machine learning and mobile services in 2015.  Followed by the blog last week,  we consider recommender systems and internet of things as well as investment technologies. I hope you can enjoy it !


3. Recommender systems

Recommender systems are widely used from big companies such as and small and medium-sized companies.  Going forward,  as image recognition technology progresses rapidly, consumer generated data such as pictures and videos must be taken to analyze consumers behaviors and construct consumers preferences effectively.  It means that unstructured data can be taken and analyzed by machine learning in order to make recommendations more accurate. This creates a virtuous cycle. More people take pictures by smartphones and send them thorough the internet, more accurate recommendations are.  It is one of the good examples of personalization. In 2015 a lot of mobile services have functions for personalization so that everyone can be satisfied with mobile services.


4. Internet of things

This is also one of big theme of the internet.  As sensors are smaller and cheaper,  a lot of devices and equipments from smart phone to automobile have more sensors in it. These sensors are connected to the internet and send data in real-time basis.  It will change the way to maintain equipments completely.  If fuel consumption efficiency of your car is getting worse, it may be caused by failure of engines so maintenance will be needed as soon as possible. By using classification algorithm of machine learning, it must be possible to predict fatal failure of automobiles, trains and even homes.  All notifications will be sent to smartphones in real-time basis. It leads to green society as efficiency are increasing in terms of energy consumption and emission control.


5. Investment technology

I have rarely heard that new technologies will be introduced in investment and asset management in 2014 as far as I concerned.  However I imagine that some of fin-tech companies might use reinforcement learning, one of the categories of machine leaning.  Unlike the image recognition and machine translation, right answers are not so clear in the fields of investment and asset management. It might be solved by reinforcement learning  in practice in order to apply machine learning into this field. Of course, the results of analysis must be sent to smart phone in real-time basis to support investment decisions.


Mobile services will be enhanced in 2015 dramatically because machine learning technologies are connected to mobile phone of each customer. Mobile service with machine learning will change the landscape of each industries sooner rather than later. Congratulations!