There is a paper recently published to show the Neural Networks are never perfect and being easily fooled. In the paper, the research team is giving “Strange Poses of Familiar Objects” for a deep neural network engine to recognize. However, the result of detection is extremely bad with more than 99% the poses are mis-classified.
Here is the original paper published:
In reality, the collection of data may lead to “Strange Poses” and not a perfect data. Thus, the writers of the paper suggested 3D datasets / objects to study rather than just 2D image to reduce the chance of classification failures.
Lots of technologists are promoting replacement of data warehouse by data lake. In the view point of our data science team, a modernized data warehouse should carry its own value for the analytics nowadays.
There is an article by TDWI.org sharing the viewpoint of CEO of Yellowbrick – Neil Carson what vital technology and tools being used.
His opinion is aligning with our data science evangelist – Samuel Sum on Hadoop. It is a large-scale data store but it is not easy to access and manage in many situations. It is always better to have a structured data store like data warehouse for easy user access. Also, a key-point of a successful data analytic environment is the storage speed storing the data. With SSD (flash memory), the data warehouse (both ETL and access) are now several times faster than before.
Finally, the editor of this webpage suggests read an article of Samuel Sum – talking about the Data Lake (Data Lake VS Data Warehouse).
Happy Chinese New Year! May the new year bring you an abundance of health, wealth and joyful memories!
In this week, we would like to share an article with highlighting the keys of success for Machine Learning.
For the original article, the author(s) had list out the keys of success for machine learning:
- Start small with Machine Learning – this is similar to all other data analytic projects to have a smaller scoped for better management
- Machine Learning Must Have Data Quality to Succeed – data is the most important ingredient for data analytic; so, the quality is critical.
- No Universal Machine Learning Algorithms – Machine Learning itself is the approach to solve one single “specific” problem and the related algorithms should be unique by the corresponding use-case.
Original Article at DataVersity.net
Sharon Di, assistant professor of civil engineering and engineering mechanics at Columbia Engineering, has discovered the patterns of traveling highly related to the types of people. The research is based on data collected by University of Michigan Transportation Institute (UMTRI) with 349 vehicles’ continuous one-year mobile traces (19,130 travel activities).
- Seniors, who travel to a wider variety of places in a day
- Workers, who stay mostly at work or at home
- Parents, who visit more individual places in a day
News Shared by insideBigData.com
Recently, the mainland China technology giant Tencent has published their big data research report on the usage pattern on WeChat (similar to WhatsApp mobile App). They have found that people born in 90s most stressful. On the other hand, people born in 70s are those with most leisure time.
In our opinion, the findings should be valuable for planning in the society. However, the privacy should be maintained and only “masked” identity should be used for analysis.
Big data and data science become mature and it’s the great time to take your corporation as a data-driven organization. There are lots of fruitful facts for a business using data analytics as an ordinary operation and management tool.
- Improve the Brand image
- Attract audience by picking the right content (product / service)
- Simplify the meaning
- Leverage the power and reach of social media
Entrepreneur Magazine (original article)
HMV is a failure example of traditional company and the streaming service is now about 34% of the total earning of the Music industry. There are still lots of people getting their living in the Music industry in alternative ways. With the Internet, all business is facing competition globally. It is important for businesses to improve themselves with the help of data.
As 2019 is just started, it is time to share different experts’ viewpoints on the trends in data science.
It’s very interesting that they are saying something “not too surprised” and many of them are running in reality. For example, large corporation management like Oracle is still talking about the Artificial Intelligence (AI) and Machine Learning (ML) with the interview with technopedia.com.
Article by Technopdiea.com
However, there is another article trying to consolidate different sources to see any common ground about the data science trends in 2019.
Article by DataVersity.net
In this article, more different areas are being covered such as Virtual Reality and Information Security.
To sum up, it is more mature to have more solutions by the support of data science. We are moving from data analytics to intelligent automation.
Our team leader / founder – Samuel Sum has written an article on his blog about Data Lake and Data Warehouse. There are lots of people trying to drop their data warehouse. However, Samuel is providing his viewpoint on the value of the data warehouse. Also, his suggestion on data lake architecture is being discussed by the real-world experiences with our professional service team.
In our team, most of the team members are working hard to grow themselves as top data scientists. There is an article to discuss how-to have a career in Big Data.
Original Article by insidebigdata.com.
To be honest, there are tons of things to learn for becoming a leading data scientist. Our team leader has reviewed this article and suggested all team members to read it once.
We are always visiting our clients to discuss the value of data analytics. One of the area is being argued about data analytic failed to provide insights for exceptional big deal in the B2B business. The real case situation is being applied in a consulting business like our team with 1 to 3 mega deals in millions scale contributing up to 30% to 40% revenue in total.
Most people think that data analytics should only be good in “regular” deals rather than “mega-deal(s)” due to the data availability. Nevertheless, we have won some projects by lots of research and development based on our own KPI knowledgebase of KPI, open data and data from the Census and Statistics Department.
Original Viewpoint from McKinsey:
The article of McKinsey shares their viewpoint for high quality “small” data could be the key of making Megadeal.