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

Where can i find Data science jobs at startups in bangalore?

I have 12 months of Experience with Python[Numpy,Pandas,MatplotlibSeaborn,Sklearn] and R programming. I have worked on Machine Learning , Natural Language Processing, Deep learning, Data Visualization tools like Tableau , QlikSense , Advance Excel and MySQL, looking for applying these skills on real-time data.Technical Skills:Applied Statistical Modelling for models analysing Z-score, Skew and Kurtosis, Data Transformation, Hypothesis Testing, Using ML Algorithms like Linear/Logistic Regression, Decision Trees, Random Forest,Xgboost,Naive-Bayes,PCA,SVM,K-means,KNN including working on  Natural Language Processing with TensorFlow. Sentiment Analysis on IMDB movie dataset, Image Classification using CNN, Customer Segmentation/Churn Analysis.       

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?

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.

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

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?

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 .  

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?

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

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?

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.

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. 

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.

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

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.

 

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

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

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.