Remembering Steve

It’s been 4 years since Steve Jobs’s demise. But, he still remains in our hearts.


I am a huge fan of Steve. He is my model person. It was my dream to see him live, to attend his keynote, but it has been left behind as a dream forever.


His work and his vision will be remembered forever.


I always watch his commencement speech to get inspired. I always watch his keynotes to learn and mimic his presentation skills. I always remember him.


Whenever I visit a beach, I never forget to write down Steve’s name remembering him.

At SantaMonica Pier

At SantaMonica Pier

At Atlantic City Beach

At Atlantic City Beach

At SantaMonica

At SantaMonica

I wish he was there, so I could tell him how big a fan I am.




10 Essential Lessons for Young Entrepreneurs

A fantastic talk has been delivered by Heidi Roizen recently. Today, In the morning I was reading the blogpost shared by the Stanford Business school and I duplicated it here in my post. In her talk for the Stanford Entrepreneurial Thought Leaders Series, Draper Fisher Jurvetson Operating Partner Heidi Roizen (MBA ’83) shared advice for young entrepreneurs on topics ranging from ethics to hiring.

1. Look for hard projects and problems to tackle.
“If you’re not doing something hard, you’re wasting your time,” believes Roizen. She encourages entrepreneurs to challenge themselves by looking for something difficult to take on each day or week. “I can tell you from my entire history of my career, there is nothing that has being as rewarding as being an entrepreneur and coming through the other side of that really, really hard stuff.”

2. Don’t compromise your ethics. Ever.
If you cheat, you will end up regretting it. How you act when you’re faced with ethical decisions sets the tone and culture for the entire company that you’re building.

3. Trust your gut.
Our intuition is built from months and years of observing human nature and interactions; it has been informed sometimes in ways we don’t even understand – so trust in it. Roizen shared the regret she felt when she didn’t go with her gut on some hiring and firing decisions.

4. Strive to NOT be the smartest person in the room.
The most important thing that you’re going to do as an entrepreneur is pick your team. “My goal truly is to be the dumbest person in the room,” said Roizen. You have to take the risk to find people who are so much better at you in certain areas. Your job is to manage and empower your team – not to know more than them.

5. Every time you meet someone, think relationship – not transaction.
“I believe everything is about relationships,” stated Roizen. Strive to build a connection with the people you meet so that when you actually need to ask them for something, you will have a deeper relationship as a foundation and will be able to collaboratively help each other.

6. Expect that life is going to be messy.
“Life actually is really, really random. Bad things will happen to you. You will fail, things outside of your control will happen.” Expect the messiness.

7. Get back up when you fall down.
“It’s not how many times you fall down, it’s how many times you get back up,” Roizen emphasized. “If you fall down and stay down, you will be down for the rest of your life.”

8. Allow randomness into your life.
Go to a meeting without an agenda. Meet somebody new. Allow yourself to be open to random opportunities.

9. Follow the 20-40-60 Rule.
Remember this rule: “At 20, you are constantly worrying about what other people think of you. At 40, you wake up and you say I’m not going to give a damn what other people think of me anymore. And at 60 you come to realize that no one is actually thinking of you.”

The takeaway, says Roizen, is “when you make mistakes, don’t worry about it. Because no one is thinking about you as hard as you’re thinking about yourself.”

10. Be your own advocate.
“If you are in a job you don’t like, you need to think about changing it. You cannot sit in your office and wait for someone to come and bring you an answer.” And if you are involved in something you don’t like, you need to empower yourself to go do something else because no one is going to do it for you.


How to install Apache Spark on Mac OS X Yosemite

Hello data scientists,

This is a quick installation guide to install the Apache Spark on your local machine. I found the documentation on the website little confusing.

1. Download the Apache Spark tar file from the [Choose any version you would like from the dropdown menu. I recommend anything 1.3.1 or above]

2. Unzip the file into your home directory.

3. Open your terminal and go to the spark directory by doing cd spark-1.3.1 [Assuming you are in your home directory]

4. Now, simply run

build/mvn -Pyarn -Phadoop-2.4 -Dhadoop.version=2.4.0 -DskipTests clean package

5. It takes at least 10 minutes to complete the whole build.

6. After the build’s completed. It should look something like the following:

Build Success

7. Now run

./bin/run-example SparkPi 10

8. You should see something like this:

Screen Shot 2015-07-24 at 1.29.07 PM

As you can see here, it says the Job has been finished which means you have successfully made it running :)

Note: I am assuming you have Java installed properly on your machine. This is very important.

How to make a background image fit the whole window using CSS?

The following piece of code will solve the problem:

background: url(‘../images/black_bg.png’) no-repeat center center fixed;
-webkit-background-size: cover;
-moz-background-size: cover;
-o-background-size: cover;
-ms-background-size: cover;
background-size: cover;



Adding a placeholder to the select tag in HTML5

There is no placeholder attribute for the select tag. But there is a way around it. The following piece of code will help you solve the issue.

HTML Snippet:

<select name=”browsers” required>

<option value=”” disabled selected>Choose a Browser</option>
<option value=”chrome”>CHROME</option>
<option value=”safari”>SAFARI</option>
<option value=”opera”>OPERA</option>



select:required:invalid {
color: #999;
option[value=””][disabled] {
display: none;
option {
color: #000;



The hot and sexiest job of the 21st Century!

You might be wondering what it is? It’s called “Data Scientist”. How cool is that? You are not just referred or called as a programmer or a software developer, but referred as a “scientist”. Pretty neat right?

It’s in huge demand throughout the corporate America. This year, the demand for data and analytics resources will reach a whooping 4.4 million jobs globally, but only one-third of those jobs will be filled, according to researchers at Gartner. The emerging role of data scientist is meant to fill that skills gap. And the reason for this explosion in what Harvard Business Review terms “data scientists”? Big Data  — a concept and an approach to business management that most large corporations are now embracing.

One of the nation’s largest companies, IBM, believes that “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. Meanwhile, leading organizations are developing new roles, focusing on key challenges and creating new business models to gain the most from big data.

Huge Ocean of Data

In this fast paced technological era, when most corporate functions depend on the technology of the internet and or the cloud for easy but secure access anytime, anywhere – data collection is no problem. Consider these statistics:

  • Twitter generates 1000 “tweets” per minute.
  • Google handles 700,000 searches” every hour.
  • Facebook features 700,000 “status updates” per hour.
  • YouTube handles 600 new “video uploads” every minute.
  • And “email messages” fly through the technosphere at the rate of 168 million per second.

Big Data is being generated by everything around us all times. Every digital process and social media exchange produces it, Systems, sensors and mobile devices transmit it. Big Data is arriving from multiple sources at an alarming velocity, volume and variety. This is a true statement that “Early adopters of Big Data analytics gained a significant lead over the rest of the corporate world.”

“We now live in the age of Big Data,” says Cynthia M. Wong, a senior researcher for Internet and Human Rights. Our communications and activities routinely leave rich digital traces that can be collected, analyzed and stored at low cost. In parallel, commercial imperatives drive a range of companies to amass vast stores of information about our social networks, health finances and shopping habits. The plummeting cost of storage and computing means that such data can be retained for longer and mined for future, unforeseen purposes.

Know, data collection is no problem but data management is. What do you do with mountains of data available to your learning organization? How do you determine what is relevant to learning? How do you incorporate it into a overall learning plan and, subsequently, into individual learning initiatives? Those are daunting questions, even for the most astute and technologically oriented learning professional. To extract meaningful value from Big Data, you need optimal processing power, analytics capabilities and skills.


When mountains of proprietary data are being stored, security is always a question. Because corporate investments are put on line when the decision is made to integrate Big Data into a corporate philosophy, the first question that C-level executives might ask is, “Is building an advanced analytics capability really worth it?” Until now, the answer to that question has not been resolved.

But a recent Bain & Co. study found that early adopters of Big Data analytics gained a significant lead over the rest of the corporate world. Examining more than 400 large companies, Bain found that “those with the most advanced analytics capabilities are out performing competitors by wide margins.”

Early adopters are:

  • 2x as likely to be in the top quartile of financial performance within their industries.
  • 5x as likely to make decisions much faster than market peers.
  • 3x as likely to execute decisions as intended; and
  • 2x as likely to use data very frequently when making decisions.


The first thing that managers of corporate learning programs must realize is that you are not in this quest alone. You, your business leaders and your information technology leaders must join forces to realize the value of the data at hand. And it won’t hurt to have one of those sexy “data scientists” on loan from the business side.

“Learning adds veracity and value to Big Data,” says Eric Burner, chief technologist at GP Strategies Corp. “Learning data can include the time of day during which it is referenced, the format/device from which it is consumed, where it’s being consumed, what kind of ratings the different lessons are receiving, how long it’s on the user’s screen, and how many starts and stops the user is making to consume it. It can tell you what your learners like and dislike.”

Where do you find existing Big Data? According to Douglas Laney of Gartner, unused sources of Big Data can be found beneath your feet in the form of operational data you collect during the normal course of business. You can start with data that your learning management system already spits out.

“Increasingly,” Laney says, “organizations are looking to extend this data with additional sensors or instrumentation.” Many organizations are also finding value in the intersection of operational data with externally available data. This external data comes from a growing cadre of syndicated data providers and public data, made available by government open-data policies and initiatives by many western countries over the past few years.” Of course, Laney adds, organizations should already be tapping into social media streams. “If you are not listening, then you won’t be leading,” he notes.

Good Learning data:

  • is relevant to the user;
  • comes in smaller chunks;
  • is simple;
  • is always available (just-in-time);
  • is flexible;
  • fits in the work stream; and
  • provides a means of interaction.

Furthermore, IBM believes that “insights from Big Data can enable all employees to make better decisions, deepen customer engagement, optimize operations, prevent threats and fraud, and capitalize on new sources of revenue.” But escalating demands require a fundamentally new approach to architecture, tools, and practices.


Big Data’s most important corporate function is to increase the effectiveness of decision-making within the organization and, more specifically, within your learning/training department. It will help you determine the programs and initiatives that are best for your learners, how to proceed with their implementation, and hot to adjust them over time.

“Big Data analytics is the application of analytic capabilities (descriptive, diagnostic, predictive and prescriptive) on enormous, varied or rapidly changing datasets”. The application of analytic capabilities combined with increased scope, content, and context of big data – particularly when merged with more traditionally structured datasets – has drastically increased the variety of use cases for decision support, and in some cases, decision automation.

Source: Elearning magazine.