I’m given to spurts of activity on Quora. Over the past year, I’ve had the opportunity to answer several questions there on the topics of data science, big data and data engineering.

Some answers here are career-specific, while others are of a technical nature. Then there are interesting and nuanced questions that are always a pleasure to answer. Earlier this week I received a pleasant message from the Quora staff, who have designated me a Quora Top Writer for 2017. This is exciting, of course, as I’ve been focused largely on questions around data science, data analytics, hobbies like aviation and technology, past work such as in mechanical engineering, and a few other topics of a general nature on Quora.

Below, I’ve put together a list of the answers that I enjoyed writing. These answers have been written keeping a layperson audience in mind, for the most part, unless the question itself seemed to indicate a level of subject matter knowledge. If you like any of these answers (or think they can be improved), leave a comment or thanks (preferably directly on the Quora answer) and I’ll take a look! 🙂

• Choosing whether a data science career is for you
• A long answer that aims to disambiguate numerous terms from the data space (and I could be wrong, of course!)
• Some reasons for R’s popularity
• Operational definitions in structured and unstructured data
• Contextualizing where R and Python shine
• On learning resources for Apache Spark
• Some handy data science and linear algebra resources
• To do or not to do: Data science post-graduate courses
• Building predictive time series models
• How RDDs in Apache Spark work
• A comparison between Apache Spark and the Petuum machine learning platform
• Spark, YARN and Mesos
• Scala and Python on Apache Spark (R and Python lover here)
• Apache Spark in production
• Some of the flaws of Scikit-Learn and Python (Disclaimer: I love and use Python, as much as I love and use R) 🙂
• Doing hypothesis tests in Python (and in R, as described on this blog)
• Preparing to learn from Christopher Bishop’s book “Pattern Recognition and Machine Learning”
• Although not strictly data science, this is about genetic algorithms and optimization
• Again, not strictly data science, and about particle swarm optimization methods
• The role of self-service BI tools alongside R and Python
• Discussing the intersection of Data Science and Complex Systems. These are two of my favourite subjects. For long, I’ve studied nonlinear dynamical systems and data science has become a profession of late. In this answer, I straddle the two worlds
• My take on the future of Spark
• Some things to avoid when writing Spark programs for data processing/ML
• Differentiating three statistical methods and graphs
• Choosing between Python based libraries for data science, and Spark
• Discussing applications for linear regression – a ubiquitous algorithm
• Differentiating logistic regression and support vector machines

Happy Quora surfing!

Disclaimer: None of my content or answers on Quora reflect my employer’s views. My content on Quora is meant for a layperson audience, and is not to be taken as an official recommendation or solicitation of any kind.