I had a few questions sir. Could I ask them to you? Like mainly based on the direction in which I should work or learn. I don’t mean to bother you. But it’s hard to find the right people who can guide new comers like me.
I first explained why people don’t really give advice for free. I am using principles I learnt from Reader’s Digest about something known as a Fermi Problem. Fermi Problems are common in tech interviews.
there are 3000 newcomers to every such right person (who gives free advice to newcomers he does not know).
out of them only 300 will get the courage to ask the right people.
out of them 250 will write a badly written email.
so by the time the right person has gotten 250 spam emails, he is not responding to the 50 out of the 3000 who
write well, and
are passionate about learning more.
that is an example how you can use mathematical thinking to understand why things work.
Then I gave in and gave her some free advice on what direction a data science newcomer should put efforts in
which direction should you work?-
interest/passion/quality – do something you are good at, because then only it will sustain your interest and you will be put up the 10000 hours to be great at it. and
greed (higher salary) versus fear (different skills)– it should make you money but you make more money if you create your own niche. so should you be like thousands of analysts in credit card analytics (easy route) or should you do analysis on videos ( tougher).
networking– why dont you atleast go to data science meetups, and try to take part in a few kaggle competitions. also, have you stopped reading r-bloggers.com or kdnuggets.com.Do this for three months and you will find enough opportunities or data. take decisions based on data not from anecdotal advice from experts.
I hope I was able to be useful. What do you think?
Enrico Fermi, Italian-American physicist, received the 1938 Nobel Prize in physics for identifying new elements and discovering nuclear reactions by his method of nuclear irradiation and bombardment. The Fermi technique is named after physicist Enrico Fermi as he was known for his ability to make good approximate calculations with little or no actual data. Fermi problems typically involve making justified guesses about quantities and their variance or lower and upper bounds. Probably you can use it for Big Data Analysis about online chatter when your machine learning is not able to process videos (Youtube) or Images ( Instagram) as efficiently as it analyzes text.