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Artificial Intelligence & Data Science

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Why companies prefer experience in Python over R in a data science interview?

Nearly 80% of the respondents in our ongoing survey say that recruiters prefer Python over R in a Data science interview. What's been your experience?

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Do companies have clear hiring process for Data Scientist role?

We recently started a survey on "Data Science Interviews in 2018". As per the current results, around 40% people think hiring companies don't have clear job role as well the correct hiring process.What's been your experience? BTW, to get the final survey report, please take this survey here.

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You can find some of the answers on hiring over here at the end. https://medium.com/@pratikbhavsar/the-definitive-guide-to-get-in-data-science-186329786f72

answered by Pratik Bhavsar

Can anyone please help me interpret this box plot?

I understand what box plots are but I am unable to draw any insights from images like this.

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Seems like designed by the pros, for the pros. Don't see an above average person understanding this. :)

answered by Nikunj Verma

Will demand of data science jobs reduce in longer run?

There is a huge demand for data science jobs in short term (<1-3 years) but I'm afraid that this demand may go down in longer run (>3 years). Reasons:I have observed that after the initial data modelling and implementation, there are no new projects in many companies. For routine improvements, companies may just need a fraction of data scientists they employ right now.Just like web development, ML and data science is seeing modularization and increasingly being packaged as a service (AWS and Google are marching ahead in this). This means there will be fewer data scientists needed to implement same projects in future than today.What do you folks think?

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Sure, the two factors you mentioned will likely hurt the demand. Many companies I see are currently hiring data scientists just because of FoMo ("Fear of Missing out"). This is "artificial" demand and will certainly go down once the reality sets in.But will this correction actually hurt data science professionals?I don't think so. In next 3-5 years, I also see a huge demand getting created by mainstream companies that are yet to make a move in data science. Think about ICICI bank, Maruti, Air India, Tata group and then a ton of PSUs like Indian Oil and NABARD. Given their huge scale, they will need an army of data science professionals and that demand will be 1000x of what we are seeing today. In sum, I think these are initial days of data science profession. The real boom is going to actually arrive in next 5 years and will continue for at least another 10.

answered by Nikunj Verma

How to detect video language programmaticlly

Hi,I am working on a project where user will upload his/her video from android app. Once it gets uploaded on the server. I  want to detect the langauge of the video programmatically using php. And currently i am looking for free solution i don't want spend money at this stage. How i can achieve this, do i need to perform machine learning or AI on this .  

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Big Data and ML algorithms

Would like to know how and what technologies to be used to get distributed data from many servers that to be used in R or Python for ML algorithm.  Assume we have data files stored in distributed servers. How to get those "big" data into R or Python to get Machine Learning (ML) processed?

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You can use Spark Machine Learning Library for building pipelines on your disturbed data. You can install Spark on your Hadoop distributed cluster and build ML pipelines. 

answered by Dinesh Ladi

Why exactly is Python the most popular langauge for data science use cases?

Stackoverflow says Python is the fastest growing major language: https://stackoverflow.blog/2017/09/06/incredible-growth-python/They don't say why, but a big reason for that growth is apparently due to Python's popularity for Data analytics, data science and BI use cases.What specific features and capabilities make Python so popular again for these use cases?

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When you ask this question that why Python is so much popular, ask yourself first that if not Python then what else??

answered by Mayukh Sarkar

How to develop a Bot from scratch?

I want to build a bot which will show minimum price for hotel booking and flights

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I just created a bot, all from scratch. I will provide a detailed explanation of the steps.Firstly you provide a user some mechanism to enter his input. That can be voice based or text based. There are various options available for voice recognition and synthesis. I used https://github.com/TalAter/annyang for speech synthesis on web and I must say, it is highly intelligent.Then you send this transcript or user's input text to a AI/ML model which can extract meaningful keywords along with the intent from this input. You need to train the model with some sample user commands/intent. This model will then parse the text and provide you a structured form of the input.  I used https://api.ai which also gives you options to integrate with various platforms.The structured form of the input received from the AI model can be used to query the data from any data store of your liking, e.g:- mongodb, elastic search etc. You can then display the results to the user as per requirement.Thats all you need to build a bot outta nowhere.

answered by Anshul Rastogi

Reddit's feed algorithm - how can you improve it?

I read about Reddit's feed/recommendation algorithm:  https://medium.com/hacking-and-gonzo/how-reddit-ranking-algorithms-work-ef111e33d0d9This is how it looks like:# Rewritten code from /r2/r2/lib/db/_sorts.pyxfrom datetime import datetime, timedeltafrom math import logepoch = datetime(1970, 1, 1)def epoch_seconds(date): td = date - epoch return td.days * 86400 + td.seconds + (float(td.microseconds) / 1000000)def score(ups, downs): return ups - downsdef hot(ups, downs, date): s = score(ups, downs) order = log(max(abs(s), 1), 10) sign = 1 if s > 0 else -1 if s < 0 else 0 seconds = epoch_seconds(date) - 1134028003 return round(sign * order + seconds / 45000, 7)​Question: Not changing the business rules, are there any improvements you could make to improve the algorithm?

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How to gather trending news on a topic ?

Let's say we have a community that is interested in a topic such as "marketing and growth" or "DevOps". Every day we want to discover trending news for this community. How to do this keeping the relevance high AND with minimal manual curation? Some options we have exploredUse a service like Discovery service from Watson. We tried it but didn't find it much relevant. Is there an alternative like Google news?Follow around 25 Twitter handles or a specific hashtag and analyze thee tweets. Apply some heuristics to check if they contain news and use engagement metrics to guage the quality. Rank all of them to find the winners. (Twitter's engagement may be more on "virality" than "quality")Subscibe to RSS feeds from various blogs (do they still exist?)What'd you try?Any other ideas would be welcome too.

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I don't know how efficient the following approach would be , but you can scrape some 10-15 websites for articles. Then put up a rule based filter on content after preprocessing to identify relevant articles. You can automate the script to run every morning to give you list of relevant URLs .  If you gotta some time, you can build a semi supervised / unsupervised model to find relevant articles instead of rule based approach.

answered by Potuganti Prudhvi

Facebook just open sourced its deep learning framework Caffe2, how does this compare to other options?

The deep learning framework can be found here https://caffe2.ai/ since A.I. is very hot right now, how will developers decide which framework they can adopt, given the various options available. 

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If you are writing for production (not for research purpose) then caffe 2 seems to be a good choice.  Caffe 2 and Tensorflow both use static graphs which can be optimized. Caffe 2 core written in C++ and build upon caffe so Nvidia frameworks like (TensorRT and Jetson) also support it. They are great for deploying CNN into production. 

answered by Rajat Gupta

Are we moving towards AI everywhere, will the skills we have currently be obsolete in the near future?

I recently came across an article that had Elon Musk's new startup called Neuralink which suggests that in a couple of years we will have human to human telepathy.

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We certainly are moving towards AI everywhere but I do not support the fact that AI will replace humans, be in terms of jobs or skills. I believe AI will show humans the true meaning of being human. We are wasting a lot of human potential in doing repetitive jobs and AI will relieve us from this. Our skills will certainly evolve with AI backing us up.

answered by Shoumik Goswami

What kind of AI will Facebook need to read brain waves?

Read this article and was wondering how will Facebook do this.https://www.bloomberg.com/news/articles/2017-04-19/facebook-s-building-8-envisions-using-brain-waves-to-type-words

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Why is R such a popular data science tool?

I've seen a lot of people talking about R in general. What makes it stand out from everything else available in the market right now.

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The most important reason I think is that it is free and open sourced. Universities usually used to go for the more mature statistical tools SPSS or STATA - student package or a cheaper version of some kind. Students would pay up a small price and so would the universities. But their preference shifted to R as it started becoming much more powerful in its own way, and flexible in comparison to the other 3.Now, by all means, toolkits like SAS are the market leaders when it comes to analysis. But think about it. Employers that learnt Data Science through R are inclined to think that if someone doesn't know R, they aren't really in it. It is sadly true in most cases. So your hiring chances drop too. R definitely has a steep learning curve. Hence in the beginning years, you are bound to lose sight of doing the actual data analysis and are more focused on getting the syntax right, or getting the code to work, which is kind of a bad thing. Most large scale companies, where cost doesn't matter much, stick to the other 3. That being said, R is still much more flexible and a programming language that gives it an upper hand in some ways. It's not just a statistical tool. It's a language. My knowledge on this is actually very limited, and this answer is based on an article I read, and I don't remember which though. My apologies.

answered by Flyn Sequeira

How can R and Hadoop be used together?

 

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Hadoop is an ecosystem of various components. Some of the components you may be interested to use from R could be SQL(Hive , Impala ,Spark SQL) or for datascience activities. SQL : An example scenario could be where you need to pull data from the underlying File system , which in Hadoop is HDFS. One simple way is to use a thrift server which exposes the data like how any database would do. DataScience tasks : You can use SparkR for building data pipeline like pulling data from multiple sources , cleaning the data , applying distribnuted Ml .All these tools are best used based on the problem you are trying to solve . Thanks,Vishnu Subramanian

answered by VISHNU SUBRAMANIAN

How confident are you about building AI algorithms that can predict user behaviour?`

A.I. is the new rage, companies are using it in creative ways. How likely are these algorithms going to scale?

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A.I. can scale as large as the data can scale. The way I think about AI is like this: What is the data I as a human need to predict a particular user behaviour; lets take the example of buying a product..  What do I need to guess:1. Need for the product (If you need/want or not) 2. Brand, Quality, Cost etc of the product 3. Persons budget or economic situation Given this information, i can make a pretty good guess if someone is going to buy or not. And if a human can predict, so can a ML model given this info. Now the beauty(and a little creepy) part is that we have all this data. Let me explain: 1. The need for the product is inferred by: search queries, time you spent on looking at that product, if you added to cart or not etc., 2. The product properties are already there in the system; 3. Economic data is also available thanks to the telecom operators, (remember the lousy form you filled when you were taking a sim card?) and things other data providers who identify you by linkedin etc.All of this data is available with almost all the E-Commerce companies sold by 3rd party data providers.So as long as the data required for the predictions exist, AI can scale.! 

answered by Meher Vamsi

How will you redesign the recommendation system for LinkedIn?

LinkedIn's feed is becoming messier with time. As a data scientist, how will you build a better recommendation system?

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At the MVP stage in a company, data is not abundant. How does one go about making machine learning models at this stage?

At the MVP stage where data is not available in abundance, how do you cope up with analyzing and predicting data. If you are able to build and predict with this small dataset, will the algorithm still hold relevance or will there need to be changes when there is a sudden inflow of complex data points. 

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as soon as the data increases you will surely face complexity in analyzing it and the current algorithms will hold no/less relevance. You didn't mention what kind of data you are working with but as far as I know if working in text analysis,video,image or audio there is data available in abundance to train the machine learning model. anyhow try building a model which is flexible , do some research on the type of data you are working on, read some research paper and see how the complexity changes and design the algo accordingly

answered by Samim Ekram

Why would you choose Tensor Flow over Theano?

  

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A year or two back theano was awesome. Even today there are use cases where theano is still good. But tensorflow comes with more advantages likeA great community backed by Google.A lot of new additions to the framework in frequent intervals. It has become the most popular ML framework in Github.It has other components like Tensorflow serving layer , Tensorboard for visualizing the networks you build . Which is very useful in debugging. If you are just starting with deep learning , I would recommend you to try Keras. At present you can switch the backends to either theano or tensorflow . 

answered by VISHNU SUBRAMANIAN

Which deep learning library/framework have you used?

There seems to be too many machine learning libraries out there. TensorFlow, Torch, Theano, Caffe, Paddle and so on. Which one have you used? When would you suggest to use what?

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Go for Tensorflow because it's the most active library on Github right now with GPU support. Also, it has good modelling support for both CNN and RNN. It's a bit slower than Torch but that won't be the case after a few months because of active development of Google. Source https://svds.com/getting-started-deep-learning/

answered by Pratik Bhavsar
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