I'm a big fan of Angular for responsive web applications. For charting applications I combine Angular with D3.

Here are some examples:

• A very responsive and comprehensive timezone app.
• Directives are a great way in Angular for writing re-usable code. Find here a showcase of directives from dynamic graphs to data tables to editors & barcodes.

Teaser: change text below and scan the code. Great way to copy text between devices?

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Django is my platform of choice for web development. I'm passionate about web technologies including HTML5, CSS, Javascript frameworks & SVG.

Below are some examples:

• One of my very first webapps: CrispyNotes.
• Personal image gallery at http://pictures.arunsworld.com/.
• A collection of tools.
• A web app to manage personal finances: bank accounts, assets & liabilities. With smart search, export & import capabilities with a variety of visual tools and reports.

I take a lot of joy in coding for myself. I enjoy the challenge of learning a new language, a new concept, solving problems & building tools.

Python is always my go to language of choice due to it's clean coding style (no curly brackets) and plethora of library support.

I enjoy C whenever I have the chance or need for it particularly in programming devices such as the Atmel ATmega328 chip.

I enjoy functional programming and use the concepts in any language I use.

I've also tremendously enjoyed creating apps for the iPhone, iPad & Android devices. Though now discontinued I had a few apps on the Apple App Store that sold in the tens of thousands.

Uses Cases (continued):

• Data Engineering: Often the source data is not at the right level for analysis or not all together. Pandas is great for a variety of these tasks:
  • Merge different datasets to combine all required data into one set.
  • Pivot data to improve it's shape and ability to be analyzed.
  • Group By to create hierarchical data from flat data.
  • Modify data selectively or derive other features from existing data. Example: derive day of week from date.
• Data Analysis: Extracting meaningful information from the data:
  • Find holes in the data, understand and fill them.
  • Group By to aggregate data up to higher levels.
  • Search within the data, slice and dice it.

Pandas is also easily speeded up by writing computationally intensive code in C dealing with numpy arrays and compiled with Cython.

I use scikit-learn for solving Regression & Classification predictive analysis problems. The library works great with Pandas.

A typical workflow:

• Read the data into memory (Pandas dataframe) and ensure data types are as they should be: time element if any, strings, integers, floats...
• Find any holes in the data and figure out how to fill them (delete them, replace with defaults, replace with calculated values - averages, medians etc.)
• Analyze the distribution of the data using histograms, boxplots, scatter plots, line & bar plots.
• Convert categorical data appropriately. Typically to dummy variables using get_dummies(data,columns)
• Choose a regression algo for continuous variable or a classification algo for a multi-class variable and fit the data
• Measure accuracy of fit and beware of overfitting by using the KFold test. Constantly plot to ensure reasonable results.

I started photography in 2000 with my first film SLR camera and my passion at that time was shooting B&W film and developing at home. It was very exciting, a lot of learning, often satisfying but also frustrating at times.

Photography meant a lot of travelling; I did many road trips around the US.

Over the years I moved on to the digital world starting with a Sony 0.5MP camera, moving on to Olympus 1.2MP camera and finally to the DSLRs.

My first SLR was a Canon and I've stuck with it to reuse the lenses as much as possible.

My equipment today consists of a 50D body with a 50mm F1.8 lens, 70-200 F4 L lens and a Panasonic Lumix Point & Shoot.

A lot of my early work appears lost during the many moves and negligence. I've mostly shot landscapes & some portraits. Low light photography has also been of particular interest. After my daughter was born lugging a tripod along was no longer an attractive option and my passion for photograph has since waned.

Still, here are some lessons I can share:

• When buying equipment give priority to light (lower F stops) rather than resolution.
• Higher resolution isn't better if it means more noise due to physically smaller sensors.
• Use Zoom to change perspective rather than to frame. Walk forwards or backwards for better framing instead. Fixed focal lenses are more fun, higher quality & lighter than zoom lenses.
• Use post-processing to improve dynamic range & color correct. Shoot RAW in challenging conditions.

My favorite projects include:

• Building a GPS device capable of recording location information on an SD card, displaying location, speed, and direction information.
• Using an Infra-red sensor to decode patterns emitted by TV remotes.
• Programming a chip with infra-red sensor input to control relays for lights and other high voltage devices using a TV remote..
• Building an infra-red signal repeater/translator to control multiple devices with one remote.
• Motion controlled lights for the house.
• Boiler controller via a remote thermostat communicating via NRF24L01 chips. Remote thermostat equipped with Nokia screen to provide a programmable interface.

My secret ingredient for all successful electronic projects: Hot Glue. It not only holds things in place but works as a great insulator to prevent shorts.

The Raspberry Pi while not exactly DIY electronics is a lovely device to interface with sensors, connect to HDMI devices, run servers with WiFi support and delve deeper into Linux.

My RPI projects include:

• Setting it up as a media player streaming content over WiFi.
• Setting it up as a router and WiFi repeater to extend WiFi coverage.
• Interfacing the RPI with Arduino over SPI to build sophisticated devices.
• Linux learning tool, custom kernels, boot loaders etc.