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Data science jobs cover a wide range of different activities so any answer is likely to be subjective. I'm in academia so my knowledge of the job market is limited, but from what I can see: The current context is very favorable to data scientists looking for a job, so anybody with some basic knowledge of ML has a chance. You're already above this level so ...


4

I don't disagree with the other answers, but here's a different perspective you should bear in mind. Also, I can offer answers to your specific questions as someone who left academia (applied math/CS) for data science. In short, understanding the underlying needs and use cases for a business problem is paramount to any project, and so developing a strong ...


3

Just Don't take this advice blindly: The subjects you have mentioned in mathematics are core to solving problems using machine learning/Deep learning, programming is a tool to implement all this theory that you learn and on that basis you create your hypotheses and then test by implementing it in code for that you do not need coding skills of a coder you ...


3

Erwan nailed it (+1). But I think my addition is a little too long for a comment. You seem to be well ahead of where I was when I landed my DS job. I was in pure math, a couple of postdocs in, and had only a short time of self-study when I was applying for industry data science. On the other hand, I had actuarial exams in my undergrad, which probably ...


3

I highly recommend you take a look at this anomaly detection survey paper. It not only provides with a great review but also categorises the different approaches focusing in different types of data or domains where these techiques have been applied. If you go through Section 1.3, you will find a series of interesting references which go as far as 1985. So ...


2

Try to look for implementations of deep belief networks. https://github.com/albertbup/deep-belief-network https://github.com/JosephGatto/Deep-Belief-Networks-Tensorflow https://medium.com/analytics-army/deep-belief-networks-an-introduction-1d52bb867a25 https://skymind.ai/wiki/restricted-boltzmann-machine https://www.csrc.ac.cn/upload/file/20170703/...


1

file.io or transfer.sh are good choices. transfer.sh has a 14 day lifespan for all files, while you can increase or decrease the lifespan on file.io. Additionally, you can use both from a command line or terminal via curl, and they're anonymous.


1

Lag features are target values from previous periods. For example, if you would like to forecast the sales of a retail outlet in period $t$ you can use the sales of the previous month $t-1$ as a feature. That would be a lag of 1 and you could say it models some kind of momentum. But you could also apply a lag of 12 to model the sales of the same month a ...


1

the 3 main layers for CNN are convolutional layer, ReLU layer and pooling layer Not necessarily in fact. Conv layers are the only layers that you absolutely need in order to implement a CNN. All the other are not strictly necessary. For example, I trained a CNN on Fashion MNIST dataset using ELU activation (you can check my Notebook here). Moreover, if the ...


1

Not a book, but a webpage with Open Source code is the ELKI Data Mining library of distance functions.


1

You could search on Google Trends all the mentions to generative models. Similarly, you can do something like this on ArXive papers and/or Google Scholar.


1

As Benj said, there's no general answer since it depends not only on the algorithm but also a lot on the data. It's easy to find examples where the exact same size of data with the same algorithm performs terrible in one case and perfectly in the other. Given a particular dataset and a particular algorithm, there are experimental methods which can help ...


1

You can have a model with a single training example. The real question should be, how good is your model with only a single training example. The answer? A learning algorithm $h(\theta)$ is an approximation of the actual relationship between your features and your target. However, the performance on how good $h(\theta)$ relies on good data. Your model is ...


1

Natural Language Processing in Action: Understanding, Analyzing, and Generating Text with Python is a new practical textbook that covers all the latest (2019) topics.


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