Marketing scientist Kevin Gray asks Professor Bing Liu to give us a quick snapshot of text analytics. The interview was first published in GreenBook on January 24, 2017 and on Linkedin on February 9, 2017.
KG: I see “text analytics” and “text mining” used in various ways by marketing researchers and often used interchangeably. What do these terms mean to you?
BL: My understanding is that the two terms mean the same thing. People from academia use the term text mining, especially data mining researchers, while text analytics is mainly used in industry. I seldom see academics use the term text analytics. There is another closely related term, called natural language processing (NLP). Text mining and text analytics usually refer to the application of data mining and machine learning algorithms to text data. NLP covers that and also other more traditional natural language tasks such as machine translation, syntax, semantics, etc. But there is really no clear demarcation between the terms. Read more →
So you have heard about the hot new analytics field, the crazy demand for well-trained analysts and the great salary. You’ve decided to throw your hat in the ring. But before you go too far, you must know these 5 key things.
- Analytics Aptitude is a must for success. Just because your best friend is an analyst and very happy with his job doesn’t mean you will be too. Make sure you were born to think analytically by testing your analytics aptitude If you score 16 or above in this test, you would likely be happy being an analyst.
- Analytics skill development is not optional. It is one thing to have an innate aptitude, it is entirely another to land a job and conduct analytics to drive a business forward. So once you know you have an aptitude, invest in developing hands-on business analytics skills at a bare minimum for any analyst or analytical role. Additionally, having hands-on comfort in predictive analytics skills and A/B testing will come in handy for pure analyst roles. If you are serious about your career transition, I will personally train you when you enroll in our analytics career transition track, which includes the above courses plus real-time experience on a client project.
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Another Halloween just got over. I am sure all of you had a great time putting the lights and carving the pumpkin – at least your kids had really a fun time. I too enjoyed standing at the door and giving out candies. My 11-year-old boy just came after a 2 hour Trick-or-Treat journey in my neighborhood. Not only that he was tired, but he was very upset. I asked him on his concerns and he listed a few: Read more →
Most Organizations today aspire to be a data driven company but what exactly does it mean and what does it take you to get there? I am going to put my thoughts here based on my interactions with several of the mid size companies(less than a billion in revenue) in United States.
As we have seen in the last few years, the term “Big Data” was going through its hype curve and most of the leaders thought (still thinking I guess) that “Data Driven Enterprise” is all about Big Data. To be “Data-Driven” means creating a Read more →
The Art of Story Telling in Data Science
I was watching a Television interview with one of the famous regional movie actors of India who rejected to play the lead role in a film that apparently turned out to be a big hit with another second in line actor. The reason he gave for his decision not to take up that film was – the director couldn’t articulate the story well so that he could visualize the final product. So it was not that the story was bad, it was not that his expertise on direction is limited, it was not the hero was not keen – but the main issue was the way in which the story was told.
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We all are hearing this question – “Can machines replace Data Scientists?” However, I thought I would tweak it a bit –“Should machines replace Data Scientists?”
We have heard a similar promise on Business Intelligence tools a few years back – if you use their stack of BI tools, any body can deliver insights regardless of their data and domain knowledge. We all know where that promise is now! And we all now can easily accept the fact that it will never be so.
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