Rowen Remis R. Iral

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Teamwork is Essential

"Alone we can do so little; together we can do so much" -Helen Keller

Most of us have been working alone, but sometimes if the task is heavy that you might need the helping hand of others.  Often times, we think we can handle the task at work alone, there are times there is a need to ask for help.  If you are a given an easy task but it is so repetitive that it will take so much time let’s say encoding tasks, you can shorten the time to do that by the help of others who will also encode with you.  Deadlines add the burden to us if the task is tedious.

Working with a team is fun.  You get to share ideas with the team members, you have the time to lighten the task.  A days work can be minimized to half of the day or shorter depending on the capabilities.  Interesting ideas comes when you are brainstorming or having a meeting with others, it makes a great way to think more, since you are not alone.  However, at some point, there are conflicts that arise.  In spite of the conflict, proper team management, cooperation and leadership can help eliminate the conflict.

They say, “two heads are better than one.”  If you are thinking on solving a certain problem and you are alone, you might some points to solve.  But with the help of teams or other people you will be able to look at a situation in a different angle, this gives a different look at the problem.  In complex organization and projects, teams are essential as they have to move forward towards their tasks.  For example, in a contact center, groups or teams are formed to effectively manage the agents.  This further help the company to keep the high quality of service they provide with clients. 

Teamwork gives a different look at a problem helping you to easily solve the problem.  Having a team lighten the task that you have.  More ideas are generated in a team.  A team with members of different expertise or skill set diversifies the team.  Teamwork is essential, you must then as an employer or leader have a team or someone who can help you on your tasks. 

Sep 2

5 D’s of Data Science

Here are the 5D

5 Ds of Data Science

  1. Data
  2. Digitalization
  3. Description
  4. Depiction
  5. Discovery

Data

In data science, the most needed is the data, the observations or examples.  With this, we can describe how much, how strong, what are the value or measurement there is about a situation or a thing.  Data existed when define the description of an event or if we measure something.  This is the most important building block that we need to have in doing Data Science tasks.  With data, we are able to show quantity and quality, and this will be the basis of our equations and statistics.  We observe or sometimes use instruments or probe in order to gather data for our analysis or research.

Digitalization

We cannot process raw data when it is not digitized or put into a computer system or encoded into forms that can be processed.  The format is not limited to text, graphics, spreadsheets, vectors, audio, video, we can use any digital format that we like.  Through digitization, we can speed up the process of analysis and procedures being applied to gather the measures in statistics.  We can then infer from the findings of things, and we can create more insight.  Digitization makes the sharing of information easier as the data can be stored and retrieved for future use.

Description

Through the tools that we have, mathematical equations and statistics, we can describe the data that we have.  We can determine if assumptions are right or wrong through hypotheses that we formulate.  We can then deduce from what we have gathered, and those will help us understand more, and can guide us on the next steps on what we can do with data in order to solve a problem or understand a situation or use it to teach machines/computers. These machines in return will be put into practical use which can aid the human ability in different aspect of our lives, not limited to traffic, medicine, marketing, economics, planning, production, operations, understanding behaviors and many more.

Depiction

In Data Science, where use to do machine learning, we mine information, create training and testing sets, we can then depict or predict the future.  Also with visualization, we can explain what we have just found out through insights.  We can share the information available for consumption at a wide range of audience from academe, profession, medicine, science and the like.  With depiction/visualization we can help different people understand what we have just found out.  This is where data science becomes an art, a place of creativity and targeting with mass consumption.

Discovery

At the end of most research of a Data Scientist, a discovery from different insights is mostly been found or through the process clarity comes as the prize of hard work.  The discovery from the tasks conducted can help to predict reality, give warnings and inform the people.  Most stakeholders are the pharmaceutical company, doctors of medicine through BioStatistics and analysis, and some business or entreprise.  The information uncovered can be a great help in making future decision on improving medicine, process, product or strategy such as those used in marketing campaign, designing educational things and also providing new products/services for the benefit of the people.

Sep 2

Machine Learning, A Look in the Past

Before the Big Data become popular, there were at the back of Web 1.0 the machine learning of the past which utilizes Market Basket Analysis.  These are very dominant in advanced e-commerce stores and online shops.  The Job sites also utilized these technology before, and how did they implement it?  Cookies, not those in your kitchen jar, but those text files that remembers your preferences, your visited sites and the things that you’ve clicked on the internet.

And what was that? Machine Learning, a part of the task of a so-called Data Scientists of today.  Facebook analyzes all of our likes, shares, streams today, Twitter can also do it, I have even tried to do sentiment analysis of tweets using python.  Google with their intelligent algorithms, Yahoo the early adopter of Hadoop for HDFS (a Big Data System).  A lot of other database management systems like SQL are there used widespread.  In those days, MatLab is a mostly used software, SPSS, SAS, S-Plus, and now R.  Nowadays there is Pig to simplify MapReduce, the language for Hadoop management.

But who are those that have benefit from data science in the past?  Amazon, the online book store have utilized data science, data mining, data analysis in order to show you the most relevant product that you can buy, they are now an online store and have even adopted into Cloud Service Provider company.  Their algorithms can help upsell and show you related items to what you have already bought.

The most successful in utilizing BIg Data and Data Science is Walmart, they know how much to display on store, they know how much to carry on their inventory and they even know when you will buy your next coffee beans, sugar and even the infant milk and cereals that you consume and buy on your scheduled shopping.  The likes of forecasting sales, that is why Walmart grew because of this so called business intelligence, it is data science, they use algorithms, mathematical equations, operations research tools in order to manage and understand the consumer behavior.

So the realization of Data Scientists today are thing of the past, but now, a successful e-scientist must have the skills in diverse fields (multidisciplinary-skilled) like business / marketing, economics, mathematics, statistics, operations research, some IT skills, big data and creativity.  Yes, creativity, without it there will be no spark of wisdom, and this is mostly part intuition, insight and looking the world/data at a different angle to predict, to deduce and to induce.

Becoming a Data Scientist

Most probably most of you are looking into becoming a data scientist or e-scientist.   With the advent of technological advancement the way we manage data is now digital, we use computers and large storage systems to store the data that we have.  In an era of information we are very well informed that Big Data or the large data are now handled by servers in the cloud or a cluster of many computers storing and processing data.

Many of those in the BioStatistics field, Informatics, Statistics and Mathematics have the edge on the core part of the field of Data Science.  Numbers as we know are quantitative descriptions of our environment and the world we lived in.  We also used to quantify qualitative data as it was held true in the past and suggested so that we can apply mathematics in everything that we do.  Being a data scientist is a part of work wherein you have to have skills in statistics, a little of basic mathematical foundations and also the love of insights and intuition. 

The creative part of being a data scientist is on the insights, data exploration and intuition.  You cannot explore an unknown data without being creative and that is part of which tells that data science is.  In data science, you are a scientist dealing with data and have the goal of achieving insights or ideas from the given set of information.  The hard part there is being able to clean out the bad part of the data and making it neat so that you can further process the information.

Also programming skill is needed, which will help you to automate some parts of your work, like applying functions or summation or complex formulas to be applied to a million data or your big data.  You have to be able to be familiar with Information Technology which is, most of your tools in e-science or data science will involve working with both commercial and open-source statistical software, programming languages, database systems and other storage systems that handles massive amount of information.

We aren’t done yet, you must be versed in systems or the topic or field you are doing research.  Most of it will not be limited to genomics, linguistics, disease prediction, medical field, and many other fields wherein you are also asked to predict.  To predict properly you must have understanding of statistics and machine learning which will give any system with the power to be an artificial intelligence power house.  Most of the current big data that we have can be used to power new robots connected to a cloud powered computer which are the works of data scientists on super computers.

Business skills is also part of data science as this will relate more to visualizations and also the profit for the stakeholders supporting your work as a data scientist.  Overall, data science is a diverse field wherein a mixture of skills is needed.

This article is helpful for looking into yourself of what type of data scientist are you, or are you a data scientist with a future since you have the basic skills needed to be into the world of data analysis, mining and science.

I fall evenly with the skills of a Data Scientist.

Related article: http://data-magnum.com/how-to-become-a-data-scientist/

analyzing the analyzers

It’s Not Bad to Explore to Begin with Something

"Creative success means balancing your love of starting things with a habit of finishing them." -Marie Forleo

Being able to explore something is not bad at all.  Whatever your hand finds to do, as long as it does not harm anyone or is ethical it is ok to try and do so.

Most of us are lost sometime, you wanted to do something, but if you don’t really even try, how will you know that you really can’t do it.  Some people limit themselves as they are not experts yet in a certain thing to do.  This is actually opposite, if you would have the time to ask an expert how he become expert or very good at his craft, he would simply tell you, I failed a hundred or thousand times until I get this thing at its best right now.  Most the successful experts never stopped, they are never hindered by mistakes, they use those mistakes as experience and ways to begin more carefully again.  They learn from their mistakes.

I would be talking about machine learning in some of my future posts, on how machines/computers learn and do their predictions by the help of humans at first and they do can do it on their own.  

Most people are having a hard time believing that they can begin small, especially not perfect, not right.  The very first thing we can do to explore things, is start doing it, and finding the right ways to do something.

Begin, that is take your very step to learning something and you will be one day become an expert on your chosen field. Step one is the only secret, it is called “Begin” or “Start” and the next is continue and improve along the way and learn from those who have done it right.

You always Invest in Something

"No one invests in nothing." -Glen Turner

Glen Turner a man who have succeeded in MLM business and with over 78 businesses with different products.

During those times of his rise, he was the Tony Robbins of his time, a great motivator, inspirational teacher.  And had several businesses which some are failures but more with successes.

Born on August 19, 1934, a school drop-out and discharged from school, he succeeded at some point in his life.  He was motivated by Dr. Gray by donating him a scholarship.

During his endeavor with MLM, he used to deliver jokes and started a large amount of sales from his jokes.  Being able to comprehend that most of his so-called sales came from the joke, he produced more of it and use it as if he was just talking straight to people.  With this, he had succeeded in the world of Multi-Level Marketing (MLM).

His success came from meeting people, talking to them, delivering jokes and direct marketing to them.  It was an awesome skill that he has during those times.  Avon was also a direct selling company.  

His wife died at 1960, at the time this documentation was written, he has 7 kids and have married 4 times.

Holiday Magic, gave him thousand dollars of money through marketing and selling.  Later in order to make more and keep his money, he invested into buying homes, the houses in real-estate.

At that point of time, he is very great that people try to stop him on what he was doing.

"People try to stop you when you are doing something great."

Later on, one of the business he founded is “Dare to Be Great”, where his strategy involved hiring smarter people than him to do the work behind.  At this very beginning, he was motivated by other people.  He worked well with them, take care of this great talents and they gave him excellent work that made him became prosperous.  He was able to beat the existing markets and motivational companies around.

"You have to be honest to take anybody forever."

Credibility of everyone is the important thing in MLM.  The compensation plans are just a bonus but most of the important things that matter are trust, honesty and integrity in all you do.

These words from Mr. Glen Turner are words of wisdom that every marketer / multi-level marketer must have in his mind.  His greatest success is he was able to have 78 corporations.

If content is king, then distribution is queen.

If you are not willing to risk the unusual, you will have to settle for the ordinary. -Jim Rohn

In factories we make perfume, in shops we sell dreams.

Memory is not a noun, memory is a verb. -Jim Kwik