Recent Big Data and Machine Learning Info Digest 2016/01/31

How can a Data Scientist student develop domain expertise?

Deep Learning with Spark and TensorFlow

15 big data and analytics companies to watch

8 ways IBM Watson Analytics is transforming business

Hadoop Jobs: 9 Ways To Get Hired

Recent Big Data and Machine Learning Info Digest 2016/01/27

Marvin Minsky Reflects on a Life in AI

Best Data Science Online Courses

Better Understand Your Data in R Using Descriptive Statistics (8 recipes you can use today)

5 steps to land a data science job in just 6 months

Using Statistical Algorithms for Success in Kaggle’s Data Science Competitions

13 big data certifications that will pay off

Is Blockchain the most important IT invention of our age?

48 Amazingly Cool Free Applications for Doing #DataScience

Recent Big Data and Machine Learning Info Digest 2016/01/24

How the artificial intelligence revolution was born in a Vancouver hotel

Hadoop and Big Data: The Top 6 Questions Answered

New Data Sources for R

100+ Interesting Data Sets for Statistics

UC Irvine Machine Learning Repository

Recent Big Data and Machine Learning Info Digest 2016/01/18

This is the story of analytics at Kickstarter 

Interview: Brad Klingenberg, Director of Styling Algorithms at Stitch Fix 

Advice for data scientists - StitchFix 

Monetizing Mobile Deep Linking: Who is Really Controlling the User? 


Thanks Shaohua for above information sharing.

Talks (video) Given by Well-known Data Scientists

Hilary Mason (was chief scientist at bit.ly and was regarded as one of the most powerful women in data tech)

DJ Patil (U.S. Chief Data Scientist at White House Office of Science and Technology Policy)

Jeff Hammerbacher (built Facebook data science team, chief sicentist as Cloudera)

Recent Big Data and Machine Learning Info Digest 2016/01/13

Michael Stonebraker: Big Data is (at least) Four Different Problems

• Stonebraker on "SQL Databases v. NoSQL Databases"
• Stonebraker on "NoSQL and Enterprises"
• Stonebraker on "mapReduce and Parallel DBmss"

10 More lessons learned from building real-life Machine Learning systems — Part I

Artificial Intelligence: Can Watson Save IBM?

Algorithms can predict how students will answer questions, and even explain why they would get questions wrong [paper]

Epic visualizations from FlowingData in 2015

Benchmarking streaming computation engines at Yahoo!

The untapped potential of health care APIs 

Microsoft researchers win Imagenet computer vision challenge

Learn python overtakes learn java

Data scientists keep forgetting the one rule every researcher should know by heart


Thanks Shaohua for above information sharing.

Starting R Programming course

Following our course list, I started my first one - R  Programming from Coursera today. 

According to the Wikipedia, R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. 

R  is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme. S was created by John Chambers while at Bell Labs. There are some important differences, but much of the code written for S runs unaltered.

The philosophy of S (so as R ) is what John Chambers said in the "Stages in the Evolution of S":

"...we wanted users to be able to begin in an interactive environment, where they did not consciously think of themselves as programming. Then as their needs became clearer and their sophistication increased, they should be able to slide gradually into programming, when the language and system aspects would become more important."

Choose Big Data Courses

To start our journey, we first need to choose the Big Data courses. 

There are so many courses (online and offline). Just google the 3 words, there are 121,000,000 results.

So, how should we start? Some people can help us with their recommendations:

• 10 Big Data Online Courses
• Latest MOOCs on Data Science
• New Year Resolutions for a Data Scientist

Based on our own research and experience, K and I come out the following courses first (we might adjust along the way).

What do you think? Want to study together?

New Year, New Journey.

Last year we have been organizing 3 Big Data related events (one of them with IBM Big Data University) through ACSIP.

When K and I started to explore the idea about Big Data education/training, we realized that we need to equip ourselves first. From May 5 to June 16 last year, we both took the MIT course- Tackling the Challenges of Big Data. We then together gave a try on one of Kaggle Competitions - Titanic: Machine Learning from Disaster. When Toronto Blue Jays was on their way to clinch a playoff berth and division championship in 2015, we worked together to make predictions of Jays' games by using A Markov Chain Approach to Baseball with MLB statistics data available online.

At the beginning of new year, we would like to get deeper into the Big Data field. We will start to learn more related courses together. 

It will be fun and challenge. If you like, you can follow our journey.