What can be a possible time line to improve my data science skills in 1-2 years?

Read this, it's the best post around for this topic

This is a great foundation. I think the most important thing is to apply your knowledge to real problems. Try to find opportunities to do that in your new role.

An expert is almost by definition someone who's spent more than 1-2 years working at something.  Keep striving to be better, but plan on spending the next 8-10 years at it.  Then you'll be an expert. Edit: The original question asked how to become an expert in data science within 1-2 years.  Since the question has been edited after I answered it, my answer isn't relevant, but I'm leaving it as is.

You mentioned you'd have 15 to 20 hours per week for career development.  You're fortunate; that is a wealth of time if used properly.  I would recommend several things to accelerate your skills over the next one to two years, listed in order of priority but done in parallel: 1) Go the UCI Machine Learning Database (Google that title) and try lots of the problems.  Set a goal to produce reasonable results on 100 data sets in two years.  At first you'll take longer than the requisite one week per data set, but you'll get faster as you learn, and as you develop your own tools to automate some tasks.  Start with regressions in Excel as a baseline, and to learn the limitations most people face, then focus on R and any add-ons you'd like to try. 2) Get a mentor who'll sit with you for lunch two or three times a month.  This mentor should be a data scientist, a statistician, or an engineer with significant machine learning experience.  Talk about problems and what it takes to solve them.  Show your mentor your UCI project results and your methods, and get feedback. 3) Read like hell.  Academic machine learning papers, Wikipedia pages on concepts and phrases you haven't heard, books on everything reasonably related to the theory and practice of data science. 4) Take online courses in related subjects. Move fast, don't get caught up in making tiny incremental improvements or trying to beat published results.  Just do this large number of projects on UCI's relatively small data sets so that you're exposed to a wide variety of problems.  Forget the "big" in "big data" until later.  Right now you need to build your intuitive feel for solving problems, and how the various machine learning and statistical methods differ in both approach and results.  Don't get caught up in academic theory, religious adherence to a single method, or the latest shiny new thing.  Just solve problems and pay attention to how different measures of success have implications for practical use of the results. For the next two years, minimize the amount of algorithms and approaches that you develop for yourself -- the only development you should do is scripts for pre-processing data and automating repetitive tasks.  After the two years, you'll know whether you should invent and development something.  Be patient; build your foundation first. And always write yourself a little problem statement at the beginning of each project, and a statement at the end about how this could benefit someone -- and calculate the magnitude of the benefit and how many people worldwide would experience that benefit versus standard regression.  As mental exercise, ask yourself how much you could charge for that benefit if people bought your model.  This process will help you stay focused on the practical aspects of your skills.  It'll also help you learn to define success metrics properly for any data set. Do this for two years and you will advance your data science skills faster than 99% of new practitioners, and you will have a killer body of work as an addendum to your resume. You've made a great career choice.  Now outrun your peers.  Good luck.

A little shell programming is all you need to get started Get involved in an open source data science project, like https://github.com/CalculatedContent/tsvm What we are doing trying to do is set up a set of open source, collaborative, data science / machine learning research projects that correspond to the work described on my blog http://charlesmartin14.wordpress.com/ These projects are designed bona-fide scientific research projects that require collecting our own data and designing experiments that systematically test specific theoretical ideas.  Unlike Kaggle, which is isolated, competitive, and predefined, these projects are meant to be shared, collaborative, and requiring scientific thinking to frame the problem Each project involves 1.  mathematical understanding and advancement 2.  data science experiments 3.  developing some code We are very open to having collaborators interested in doing novel data science research

I answered a relevant question in quora.

I've got this link on my to-do list: https://github.com/datasciencemasters/go it seems to be a very up to date community-run curriculum for data science. I would suggest you scope it out as it may help to redefine your approach to what is possible in the given time frame. the people behind it are very friendly and engaging.