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I dropped out of college but am interested in a career in data analysis. Now I am self-studying approximately 10 hours per day. Browsing through job postings on Linkedin has allowed me to compose a rough curriculum. It would be of great help to me if you would either add a subject I have omitted or eliminate a subject that is not necessary for success in the market place.

Curriculum (in 3-subject groupings):

Group 1

  1. Single-variable calculus

  2. Intro to python

  3. SQL

Group 2

  1. Multi-variable calculus/linear algebra

  2. Discrete math

  3. Data structures and algorithms

Group 3

  1. Calculus-based statistics and probability

  2. Hadoop stack

  3. Differential equations

Group 4

  1. Statistical learning/predictive modelling

  2. Python data analysis techniques/Statistical programming in R

  3. Fundamentals of machine learning


All the while I plan to practice using any data sets I can find online. Will this be sufficient to land a job in data analysis? Of course I plan to learn far more than just this, but is this foundation solid enough to land an entry level data engineering/science position?

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At least based on what I and other data analysts/scientists do in my company, your technical topics list seems sufficient. But I would also add:

  • Visualization (ggplot2 in R, matplotlib in Python, d3.js for really cool stuff)
  • Design of experiments

Communication skills are also quite important.

For more inspiration, here's a good "curriculum" represented as a metro map: http://nirvacana.com/thoughts/becoming-a-data-scientist/

Let me also recommend building up a portfolio of data science projects. This could consist of your analyses of data sets you find online (e.g. on UCI), Kaggle competitions, or class projects (e.g. via Udacity or Coursera). That way, you can give direct proof of your technical skills, your communication in the form of reports or graphics, and your ability to extract insight.

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