data scientist | shuntzblog

In layman’s language, Machine Learning as the name suggests, involves “making the machine learn”.

Learn what?
Learn the decisions to be taken on a particular case, just like humans do. Hence, Machine Learning is a branch of Artificial Intelligence.

Learn How?
How do humans learn something?
By practice, i.e by applying your knowledge many times on different cases.

Imagine you’ve joined Tennis coaching classes. Your coach tells the assistant coach shoot balls at you in different directions and with different speeds. He spins the ball sometimes. He might also shoot the ball at you in ways which is rare. For every ball thrown at you, the coach tells you to hit the ball in a particular way. Maybe for a straight shot with less speed he will tell you to hit it towards the opponents net, while away from the nets for some other shot. In this way you’ll keep practicing on various shots and learn tennis one day. After you’ve learnt tennis, you’ll be able to hit the ball without the help of your coach.

Now,
You- Learning Model
Assistant Coach- Training Data
Coach- Decisions on Training Data

After Learning,
You- Trained Model able to take decisions on Test Data

In machine learning, we give the algorithmic model some training data which already has the decisions taken. The model learns from the data using the algorithm. Next time when you give the model an unknown case, it will make the decision.

There are various ways through which you can learn machine learning, but I think taking this Stanford MOOC by Andrew Ng will be the most helpful way. Apart from being the most popular MOOC, it covers all the topics in Machine Learning with informative videos. It also has multiple quizzes and programming assignments to test what you’ve learnt. Machine Learning – Coursera Machine Learning

You’re gonna love Machine Learning 🙂

– Aditya Awalkar

Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured.

Big Data originally emerged as a term to describe datasets whose size is beyond the ability of traditional databases to capture, store, manage and analyze. However, the scope of the term has significantly expanded over the years. Big Data not only refers to the data itself but also a set of technologies that capture, store, manage and analyze large and variable collections of data to solve complex problems.

Data is emerging as the world’s newest resource for competitive advantage.As the value of data continues to grow, current systems won’t keep pace.

Big Data is projected to be a $28.5 billion market in 2014, growing to $50.1 billion in 2015 according to Wikkbon.

Big Data Technologies

NoSQL databases
MapReduce
Hadoop
WibiData
MongoDb
Cassandra

Applications of Big Data

Big data analysis played a large role in Barack Obama’s successful 2012 re-election campaign.

Big data analysis was, in parts, responsible for the BJP and its allies to win a highly successful Indian General Election 2014.

Determine root causes of failures, issues and defects in near-real time, potentially saving billions of dollars annually.

A number of cities are currently piloting big data analytics with the aim of turning themselves into Smart Cities, where the transport infrastructure and utility processes are all joined up. Where a bus would wait for a delayed train and where traffic signals predict traffic volumes and operate to minimize jams.

Improving Sports Performance: Most elite sports have now embraced big data analytics. The IBM SlamTracker tool for tennis tournaments uses video analytics that track the performance of every player in a football or baseball game, and sensor technology in sports equipment such as basket balls or golf clubs allows us to get feedback.

E-Commerce: eBay.com uses two data warehouses at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising.

Improving Security and Law Enforcement: National Security Agency (NSA) in the U.S. uses big data analytics to foil terrorist plots (and maybe spy on us). Others use big data techniques to detect and prevent cyber attacks. Police forces use big data tools to catch criminals and even predict criminal activity and credit card companies use big data use it to detect fraudulent transactions.

Improving Science and Research: CERN data center has 65,000 processors to analyze its 30 petabytes of data. It uses the computing powers of thousands of computers distributed across 150 data centers worldwide to analyze the data.

Improving Healthcare and Public Health: The computing power of big data analytics enables us to decode entire DNA strings in minutes and will allow us to find new cures and better understand and predict disease patterns.

Google Flu Trends uses aggregated Google search data to estimate flu activity.

AND

BUSINESS ANALYTICS – This is perhaps the most important reason why Big Data is so valuable. Big Data Models and algorithms help to make decisions involving profit of million dollars.

Job Scope in Big Data Field

New skills are needed to fully harness the power of big data.Though courses are being offered to prepare a new generation of big data experts, it will take some time to get them into the workforce.

Gartner finds that by 2015, the demand for data and analytics resources will reach 4.4 million jobs globally, but only one-third of those jobs will be filled. The emerging role of data scientist is meant to fill that skills gap.

Big Data experts are paid lucratively.Data scientists are some of the most expensive and coveted professionals around today.

Qualified big data analysts command impressive salaries. Someone right out of school can earn $125,000, while someone with a year or two of experience and a demonstrated skill set can easily make double that.