Hafidz Zulkifli

1. First off, ensure that you have a wired connection to your laptop. 2. Using Lenovo Access Connections, ensure that your laptop is connected to your LAN. 3.  Go to 'Mobile Hotspot'. Fill the necessary details. Start the network. 4.  From your mobile, you should be able to see the SSID. Connect to it. 5. From Windows, find out which network is your wifi using. In our case it's 'Wireless Network Connection 7.  6. Initially you will not see the connection being able to connect to the internet. 7. Go to Local Area Connections. 8.  Go to Properties, then Sharing. Enable your sharing to allow your adhoc wifi network to connect to the internet via your LAN. 9.  Allow the needed services. 10. OK everything. 11. Wait for a few minutes to allow the changes to take place. 12. Your mobile should now be able to connect to the internet vi...

A lengthy title. I know. Cutting to the chase, I needed to transfer data from my RDBMS to a Hadoop cluster and I only had access to Hue/Oozie (since I’m not an admin). I knew that I could use Sqoop to do it — but I’ve never really done it before. It was freaking hard/annoying! So to help others out there who might be in a similar predicament as I was, here are some 101. 1. I assume you already know how to use the Workflow Editor, so from there, create a new Workflow. 2.  Drag a Sqoop action from the panel above and click OK. 3. You’ll get some pre-filled sqoop command in there which you can use as reference. Hop to Apache Sqoop to learn more about all available arguments you can use. 4. There’s 2 way you can go about entering the Sqoop command from here on out. You can either type in the Sqoop command in the text box, OR if you’re thinking of using a query in your command, my recommendation is to use the argument window. The latter is based on a post ...

Below is a great course by Stanford University on Natural Language Processing. There hasn't been any recent sessions as of late in Coursera, but you can still access the archive at this  link. I'm currently working on my capstone for the John Hopkins Data Science Specialization, where we're asked to build a data product that is able to predict the next sets of word based on what users type into a textbox - similar to stuff like Google Autocomplete or Swiftkey. Pretty psyched about it - looking forward to the challenge! :)

Below are a few notes (most are copy pastes) of stuff that I've covered during my time at Dilip Soman's Behavioural Economics online course at edX. The topic is a recent interest of mine after spending some time earlier this year learning about social graphs and basic graph theory in general. So as a natural extension to that, a question that comes to mind is, how do people make purchasing decision? The notes have been mostly compiled in Slack - somehow I kinda take a liking in the way the notes there get formatted. It's relatively easy too - perfect for lazy people like myself. Below are the public links of my notes in Slack with regards to the topic: Early General Notes A Theory of Decision Points Choice Overload   Glossary of Concepts Consumption Vocabulary Recent Nudge Experiments Decision Aids Disclosure One thing that I really like about the course is that it also talks about how to conduct experiments should you have an idea that you'd like ...

One of the reason I like to join these online courses are that it gives you the chance to meet with people from different backgrounds, industries and countries. In this particular post, I'm quite amazed with the dedication and thought that was put in by one of my classmates. The assignments required that we create our own data product using R - so that we become a wholesome data science practitioner - we acquire data, process, model, document, and create data products for others to consume. It's one thing for doing assignments for the sake of completing the course, it's another to produce a beauty such as the above. You may explore the Shiny app here at this link , and have a look at the forked source code here . I've forked it since I know I'll be making use of this in times to come.

Lately I've been studying Shiny and how to use in R. It's a cool arsenal to have while using R as you can really quickly develop a data product right from R itself. Of course you could say load the data up in tools like Tableau or Qlik Sense are have a much cooler/sexier visualisation - but that's not the point I'm trying to bring here. For a quick preview of what I managed to conjure up with Shiny, pop over to Social Network  . Couldn't help it - it just had to be a network graph - again :P. A real sucker for graphs I am. Anyways, what it aims to demonstrate are how from a social network graph like that, you can derive the centralities (degree, closeness etc) and from there - the roles of each nodes based on how they are connected to each other. I'll not be making the claim that it's correct in any way - it's just something that I've picked up from Drew Conway, based on his presentation on Socio-Terrorism . For more details - check ou...

The below excerpt are taken from  this page.  Copying it here for future reference in finding the right R packages for different types of analysis - god knows it's hard to find the right packages in R. :) Several add-on packages implement ideas and methods developed at the borderline between computer science and statistics - this field of research is usually referred to as machine learning. The packages can be roughly structured into the following topics: Neural Networks  : Single-hidden-layer neural network are implemented in package  nnet  (shipped with base R). Package  RSNNS  offers an interface to the Stuttgart Neural Network Simulator (SNNS). An interface to the FCNN library allows user-extensible artificial neural networks in package  FCNN4R . Recursive Partitioning  : Tree-structured models for regression, classification and survival analysis, following the ideas in the CART book, are implemented in  rpart  (shipped w...