nltk.corpus for data science related words?
I have a way through which you can solve your problem. For it you will require a,


A pretrained embedding generator. It can be Word2Vec or GloVe. Any of them could work.



  Next, we have a corpus of words which have higher frequencies. Suppose
  we have a set of 100 such words where the 1st word has the highest
  frequency.


Now, we convert every word in this set to a vector using our pretrained word embedding. 
Hence you will have a set of vectors for the words from the corpus. Let's call it $z_i$

We have the word "data science". Get a vector for this too. Let's call it $x$


Measure the euclidean distance between the vector $x$ and $z_i$.
Or, you can measure cosine similarity betwee $x$ and $z_i$.
Both of above methods will produce a set of values which would show proximity of $x$ with values of $z_i$.
From these 100 values, we get the least 10 values and convert them to words again.


These 10 words would have the highest similarity with the word "data science".