Don't worry about needing to catch up. Stuff is moving so fast these days, you're always working with something new. Everyone is in a continual update mode so it's not like you have 10 years of catching up to do. Tech has turned over a 10 times since then. You could say 10 years and 2 years are functionally equivalent from a new tech point of view.

And don't worry about corps and recruiters. Focus on a problem you want to solve, and update your skills in the context of learning what you need to know to solve that problem. If you can leverage your industry experience in the problem domain, even better.

Data is driving everything so developing a data analysis/machine learning skillset will put you into any industry you want. Professor Yaser Abu-Mostafa's "Learning From Data" is a gem of a course that helps you see the physics underpinning the learning (metaphorically of course -- ML is mostly vectors, matrices, linear algebra and such). The course videos are online for free (http://work.caltech.edu/telecourse.html), and you can get the corresponding book on Amazon -- it's short (http://www.amazon.com/Learning-From-Data-Yaser-Abu-Mostafa/d...).

Python is a good general purpose language for getting back in the groove. It's used for everything, from server-side scripting to Web dev to machine learning, and everywhere in between. "Coding the Matrix" (https://www.coursera.org/course/matrix, http://codingthematrix.com/) is an online course by Prof Philip Klein that teaches you linear algebra in Python so it pairs well with "Learning from Data".

Clojure (http://clojure.org/) and Go (http://golang.org/) are two emerging languages. Both are elegantly designed with good concurrency models (concurrency is becoming increasingly important in the multicore world). Rich Hickey is the author Clojure -- watch his talks to understand the philosophy behind the design (http://www.infoq.com/author/Rich-Hickey). "Simple Made Easy" (http://www.infoq.com/presentations/Simple-Made-Easy) is one of those talks everyone should see. It will change the way you think.

Knowing your way around a cloud platform is essential these days. Amazon Web Services (AWS) has ruled the space for some time, but last year Google opened its gates (https://cloud.google.com/). Its high-performance cloud platform is based on Google search, and learning how to rev its engines will be a valuable thing. Relative few have had time to explore its depths so it's a platform you could jump from.

Hadoop MapReduce (https://hadoop.apache.org/, http://www.cloudera.com, http://hortonworks.com/) has been the dominant data processing framework the last few years, and Hadoop has become almost synonymous with the term "Big Data". Hadoop is like the Big Data operating system, and true to its name, Hadoop is big and bulky and slow. However, there is a new framework on the scene that's true to its name. Spark (http://spark.incubator.apache.org/) is small and nimble and fast. Spark is part of the Berkeley Data Analytics Stack (BDAS - https://amplab.cs.berkeley.edu/software/), and it will likely emerge as Hadoop's successor (see last week's thread -- https://news.ycombinator.com/item?id=6466222).

ElasticSearch (http://www.elasticsearch.org/) is a good to know. Paired with Kibana (http://www.elasticsearch.org/overview/kibana/) and LogStash (http://www.elasticsearch.org/overview/logstash/), it's morphed into a multipurpose analytics platform you can use in 100 different ways.

Databases abound. There's a bazillion new databases and new ones keep popping up for increasingly specialized use cases. Cassandra (https://cassandra.apache.org), Datomic (http://www.cognitect.com/), and Titan (http://thinkaurelius.github.io/titan/) to name a few (http://nosql-database.org/). Redis (http://redis.io/) is a Swiss Army knife you can apply anywhere, and it's simple to use -- you'll want it on your belt.

If you're doing Web work and front-end stuff, JavaScript is a must. AngularJS (http://angularjs.org/) and ClojureScript (https://github.com/clojure/clojurescript) are two of the most interersting developments.

Oh, and you'll need to know Git (http://git-scm.com, https://github.com). See Linus' talk at Google to get the gist (https://www.youtube.com/watch?v=4XpnKHJAok8 :-).

As you can see, the opportunities for learning emerging tech are overflowing, and what's cool is the ways you can apply it are boundless. Make something. Be creative. Follow your interests wherever they lead because you'll have no trouble catching the next wave from any path you choose.