nltk.corpus for data science related words? from job description I scraped from the internet, I've went through all nlp processes and I've got to place where I found:

freq = nltk.FreqDist(lemmatized_list)
most_freq_words = freq.most_common(100)


which outputs:

[('data', 179),
 ('experience', 86),
 ('work', 78),
 ('business', 71),
 ('team', 59),
 ('learn', 56),
 ('model', 49),
 ('skills', 47),
 ('science', 41),
 ('use', 41),
 ('build', 39),
 ('machine', 37),
 ('ability', 36),.....


and so on. My problem is I do not want to consider words like "experience", "work", and only consider keywords related to data science. I'm guessing there is a corpus for data science terms which I can use like how I use stop word corpus to not select them. Let me know if there is a way, Thanks!
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".