a journey into data science: earning the white coat · “a journey of a thousand miles begins with...

A JOURNEY INTO DATA SCIENCE:

EARNING THE WHITE COAT

Anthony DutraProject AccountantEMC Corporation

2015 EMC Proven Professional Knowledge Sharing 2

Table of Contents

The Journey Begins ................................................................................................................... 3

What is Data Science? ............................................................................................................... 4

Big Data and the Internet of Things ............................................................................................ 5

The Personas of a Data Scientist ............................................................................................... 7

The Nerd ................................................................................................................................ 7

The Artist ................................................................................................................................ 9

The Business Professional ....................................................................................................10

Roll Up Your Sleeves! ...............................................................................................................11

What is Hadoop? ...................................................................................................................11

What is R? .............................................................................................................................14

A Little Taste of R... ...............................................................................................................16

Resources for Future Data Scientists ........................................................................................18

How to Get Involved ..............................................................................................................18

Friendly Competition: Kaggle .................................................................................................18

Data Science Communities: Sharing is Caring ......................................................................19

Learn More ............................................................................................................................20

Learn via MOOC ...................................................................................................................20

Learning in Certifications from the Industry Frontrunners.......................................................21

Saved by the Bell - Formal Education ....................................................................................22

A Single Step... .........................................................................................................................23

Appendix ...................................................................................................................................24

Code for Sentiment Analysis17 ..................................................................................................26

Disclaimer: The views, processes or methodologies published in this article are those of the

author. They do not necessarily reflect EMC Corporation’s views, processes or methodologies.


The Journey Begins

“A journey of a thousand miles begins with a single step.”

Lao-tzu, The Way of Lao-tzu

Do you yearn to discover? Do you want to decipher intricacies within unstructured data?

Perhaps take seemingly disjointed files and construct a cohesive masterpiece? Challenge

complex hypotheses? Develop data-driven models? If you’ve answered yes to any of the above,

then becoming a Data Scientist may be the career track for you...but where does one begin?

In the initial phases of discovering Data Science, it’s natural to feel intimidated by the volume of

available information. This article will provide a baseline understanding of Data Science and the

concepts associated with it. It will challenge the claim that Data Science is a discipline only for

those with an advanced degree in mathematics or computer science. While it is crucial to have

such an educational background, it is more imperative to have a curiosity for the unknown world

of data, a passion for discovery, and a yearning to analyze to create significant solutions. This

article is written from the perspective of someone who has researched the topic of Data Science

for approximately two years and it’s time for that knowledge to be shared amongst all aspiring

Data Scientists, with the hopes of inspiring each one to embark on their journey to earning the

white coat.

My expedition into the world of Data Science began similarly to the approach many of us have

used when learning to swim. Initially, it all seems easy enough to grasp. Watch any Data

Scientist present their research, they do so in an amusing yet respectable manner; their white

coats illuminate in front of a crowd of inquisitive faces listening to the exhibition with intent. They

make it look so easy that we can’t help but want to plunge blindly into the depths of Data

Science. Not long after we realize that we’ve started treading water, at best. We are overcome

by the endless waves of available information on such a complex topic. What was once

fascination quickly becomes fright. The world of Data Science can be overwhelming, especially

if you’re like me, and should have initially approached the vast ocean of information by first

floating in the shallow end with water-wings on.

It’s time to start swimming in the world of Data Science. First, we will dive into the topic of Data

Science by describing what it is and how it is embodied by the framework of Big Data and the

Internet of Things (IoT). Next, we will introduce the three personas I like to apply for Data

Scientists: the Nerd, the Artist, and the Business Professional. We will explain two tools of the

trade, followed by a quick example of what the start of a Data Scientist project may look like.


Then, we will surf to online competitions and communities. Finally, we will be led ashore by

exploring some available educational avenues that provide structured learning in the world of

unstructured data. Together we will traverse the intricacies of Data Science and begin our path

to donning those white coats! Are you ready, future Data Scientists?

What is Data Science?

Data Science is a “buzzword” frequently used across various industries in a variety of contexts.

So what is it? Vasant Dhar (2013) in his abstract Data Science and Prediction defines Data

Science as “the extraction of knowledge from data1.” As vague as that notion may appear, it is

actually quite profound. It is a term used to describe the marriage of statistics, computer

science, art, and business all used to extract meaning and interpretation from data. A Data

Science project requires a lot of collecting and cleaning the data, as the vast majority of the data

is not readily available. This process requires tenacity since the data isn’t always available in the

proper format. A Data Scientist should develop an initial hypothesis that is inspired by a deep

understanding of statistics and knowledge of computer science principles. This hypothesis and

its validity tested via exploratory data analysis. Adapted from John Tukey (1977), exploratory

data analysis (EDA) is defined as an approach to analyzing data sets to summarize their main

characteristics, often with visual methods2. These methods are paired with business soft skills,

ranging from grasping the biases found in the data set to debugging code in a created model. In

Data Science, it is crucial to be able to form a final piece of communication amongst team

members and leadership in concise and understandable language. These presentations require

effective data visualizations so that the conclusion may enhance the decision making process of

an organization. The best illustration describing The Data Science Process is from Doing Data

Science: Straight Talk from the Frontline:


Figure 1: The Data Science Process3

The aim of all Data Science activities is to derive meaning from data and produce solutions that

can be used to influence decisions which create value. The discipline of Data Science spans

numerous domains including healthcare, biological and medical sciences, and social sciences.

Arguably, the largest domain impacted by the practice of Data Science is business. Virtually

every aspect of business is open to data collection and often even instrumented for data

collection: operations, manufacturing, supply-chain management, customer behavior, marketing

campaign performance, workflow procedures, and so on4. The study of Data Science has

changed the look, feel, and value of data, bringing to it new and exciting methods and concepts

never before applied on such a massive scale.

Big Data and the Internet of Things

We are now living in what I suggest to be the Information Ages’ “Wild West of Information

Discovery”. As we share more of our lives via social media and organizations generate more

information daily, a data overload begins to develop. According to a quote from IBM as

mentioned in a British Broadcasting Company (BBC) article in March 2014, over “...2.5 exabytes

- that's 2.5 billion gigabytes (GB) - of data was generated every day in 2012... About 75% of

data is unstructured, coming from sources such as text, voice, and video5.”


Big Data is a term for any “large” collection of data sets. These data sets are so enormous and

complex that they become difficult to process using traditional data processing applications. The

databases are, “denormalized”, meaning there are instances of redundancies in the data sets.

Industry analyst Doug Laney (2001) conceptualized the now conventional definition of Big Data

with the inception of the three V’s: Volume, Velocity, and Variety6:

Volume. Numerous factors contribute to the increase in data volume. These factors

range from transaction-based data to unstructured data. Storage costs are decreasing;

excessive data volume is now the new norm. The challenge now is to determine the

relevance of the data within large data volumes and how to use analytics to create value.

Velocity. Data is being uploaded, downloaded, and streamed at groundbreaking speed.

To maintain its value, this data must be processed in a timely manner or it will become

stale. Reacting quickly enough to deal with the always changing data is a challenge for

most organizations.

Variety. Information today looks very different than how it was presented in the past. We

can share videos and photos instantly, comment on a thought using social media, and

transfer funds in our bank accounts on our smartphones. So it’s no surprise that our data

today comes in all types of formats. Structured data resides in a fixed field within a

record or file. This includes data contained in relational databases and spreadsheets.

Semi-structured data does not conform to the formal structure of data models but

contains tags or other identifiers to separate semantic elements. Finally, there is

unstructured data which refers to information that either does not have a pre-

defined data model or is not organized in a pre-defined manner (i.e. text, HTML, etc.).

Organizations struggle with deriving value from each type of data.

Another cause of this data explosion can be attributed to the Internet of Things (IoT). As defined

by Techopedia, IoT is a computing concept that describes a future where everyday physical

objects will be connected to the Internet and be identifiable to other devices7. An object that can

represent itself digitally becomes something greater than the object by itself. It does so by using

emerging technologies such as near field communications, real-time localization, and

embedded sensors to convert –- as Tarry Singh (2014) stated in his article, Digital

Transformation –- “dumb things into smarter things8.” The item enveloped with IoT technologies

develops what can be referred to as a digital identity. The object no longer relates just to you,

but is now connected to surrounding objects and database data.


The challenges that arise from both Big Data and IoT are plentiful and the changing landscape

of how data is created through these methods leaves much to discover. Luckily, there are

individuals who are willing to resolve these challenges and are not intimidated by the necessary

work needed to decipher such intricacies. These individuals are the Data Scientists.

One may ask: Who are these information-savvy individuals? How can I emulate them? To

satisfy my own curiosity, in the learning process I developed the following personas that I

believe you may find helpful when thinking about Data Scientists:

The Personas of a Data Scientist

Data Scientists are unique individuals that push the boundary of machine and human learning in

an effort to discover what cannot be seen by others. A Data Scientist is a balanced role and

requires someone with the skills to organize, develop, create, and share their work amongst

their colleagues and upper management. This section is comprised of detailed explanations of

three personas that every Data Scientist possesses: the Nerd, the Artist, and the Business

Professional9 (Dutra 2015).

The Nerd

Figure 2: The Nerd9 (Dutra 2015)


The fundamental roots of Data Science today are composed of vastly more intricate educational

fields of study: mathematics, statistics, and computer science. A Data Scientist is skilled to

some degree (or at the very least has an interest) in these main educational areas. As well, a

Data Scientist should have some domain experience within their area of research. This

combination of educational and domain experience is what makes a Data Scientist a true

expert. Alongside this notion, it is important that a Data Scientist have experience and a

curiosity to solve problems in this format. Data Scientists train these models over several data

sets so that the results are as accurate as possible. If you want to be a Data Scientist, it’s

probably best to read further on the statistical fundamentals shown below, as they will be the

foundations of analysis when working on an issue or topic:

Linear/Logistical Regression

Naïve Bayes Classifiers

K-Means Clustering

Decision Trees

Autoregressive integrated moving average (ARIMA) modeling

Analysis of variance (ANOVA) modeling

There are also non-mathematical, more computer science-based principles on which a Data

Scientist bases model creation. Text mining is a good example of such a principle. Also referred

to as “text analytics”, text mining is a way to derive worth from unstructured, qualitative data. I’ll

show an example of this later in the article but for now it is central to understand that a Data

Scientist must be well versed in each discipline.

These fundamental mathematical, statistical, and computer science principles backed with a

solid understanding of scripting languages and a basic understanding of Big Data hardware

functions, are important aspects of a Data Scientists domain. Remember, these are areas of

knowledge that can be taught. It is crucial that an aspiring Data Scientist learn and practice

these fundamentals as a daily routine. It is closed-minded to think that only individuals that hold

a degree in mathematical or computer based backgrounds fit the bill. Data Scientists are curious

individuals who challenge the norm. There are other, more natural, aspects of a Data Scientist

that are crucial to the position.

These aspects are derived from a Data Scientists’ passion for finding and interpreting rich data

sources. Data Scientists must be able to manage large amounts of data despite hardware,


software, and bandwidth constraints, merge data sources, ensure consistency of datasets, and

build mathematical models using such data. They must be able to utilize tools and learn the

newest technologies as quickly as possible. For most, these tasks seem like a chore. For Data

Scientists, this is only a portion of the job. Aspiring Data Scientists must embody and share the

love of data analysis.

The Artist

Figure 3: The Artist9 (Dutra 2015)

It’s one skill to analyze the data; it’s another to create with it. Our world is more visual than ever,

we are constantly viewing videos and sharing images with one another. Each day there is a new

photo or video created in an attempt to make a point on a specific topic. If the piece fails to grab

the attention of its audience, the intended message is lost. To capture an audience, we must get

them to listen with their eyes. The creative element of a Data Scientist is arguably the most

important aspect of the role. It is not enough to be able to find patterns amongst the numbers, a

Data Scientist has to be able to paint the picture of the data and inspire change amongst their

peers and authority. A Data Scientist must be critical of their work and open to ideas and input

from their colleagues.


This artistic function of Data Science requires an individual with visualization skills, meaning

they, as the artist, have the ability to work the results of their findings into a comprehensive and

creative masterpiece utilizing visualization tools.

A Data Scientist is passionate about the business they are involved in. The expertise in their

respective domains gives an edge to the Data Scientist over the typical analyst, so it is crucial

that when creating these visualizations, the passion is demonstrated in their work.

The Business Professional

Figure 4: The Business Professional9 (Dutra 2015)

Even with the math skills of a thousand computational devices and the artistic skillset of Pablo

Picasso, at the end of the day, the Data Scientist is still all business. A Data Scientist must have

basic business soft skills to be successful. A Data Scientist must proactively look toward the

broader landscape of any problem, and be able to explain the value of their contributions to the

larger body of work. Of course, obstacles to this feat cannot be taken on alone. Therefore, a

Data Scientist usually works as part of a team, similar to other delivery (program management,

solutions specialists, etc.) roles whose members have complementary knowledge and skills.


Perhaps the most critical aspect of a Data Scientist’s job is presenting and communicating not

only the resulting insights of the data to colleagues, specialists, and upper management on their

team, but the value. Story-telling skills are immensely important to explaining the value of a

long, resource-intensive project.

Final Thought on the Three Personas

The Nerd. The Artist. The Business Professional9 (Dutra 2015). In my findings, these three

personas comprise the essence of each Data Scientist. All of these can be developed in each

individual striving to don the white coat. A Data Scientist is an intense discipline, blending

copious skill sets into one role.

Roll Up Your Sleeves!

There is no better time than the present. Whether you are twenty years old or wish you could be

twenty years old again, why not take charge of your career passion today? In the world of Data

Science, you can only learn by rolling up your sleeves and taking action! It is important for

someone who is new to the world of Data Science to learn and play at the same time and now is

the time to do it. There are two frequently used tools in a Data Scientist’s toolkit: Hadoop and R.

The section below will provide an introduction to Hadoop and provide a short history of its

creation and its structure. This is by far one of the more complex tools, but it is an enabler of

exponential value to organizations. Following Hadoop, I will introduce R, an open-source tool

utilized by many in the Data Science community, in part since it’s known for its ease of use. This

will be a great tool for you to start engaging with (almost) right away. Finally, there will be an

introduction to some of the more well-known communities that you should engage in to further

your knowledge before deciding whether to pay for lessons in the discipline of Data Science.

What is Hadoop?

Hadoop is an open-source Apache Foundation project (originally started in 2002 under the

name Apache Nutch) created by Doug Cutting, the creator of another Apache project named

Apache Lucene (a high-performance, full-featured text search engine library written entirely in

Java. It is a technology suitable for nearly any application that requires full-text search,

especially cross-platform10). Hadoop’s original purpose was to build a web search engine from

scratch; however, once Doug Cutting and his team realized their architecture would not scale to

fit the needs of the Internet, the project shifted gears. Once Google released their version of

Google’s distributed file system (GFS) in 2003, Cutting and his team set to work on creating an

open source implementation of GFS. Jeffery Dean and Sanjay Ghemawat (2004) explain that


Google published the white paper that introduced MapReduce to the world11. Following that

release in early 2005, the developers had a working MapReduce implementation in Nutch. As

described by Tom White (2011) in Hadoop: The Definitive Guide, by the middle of 2005, Nutch

algorithms had been ported to run using MapReduce and NDFS12. Here we see the structural

foundation and development of Hadoop.

The challenges that Big Data creates have become a harsh reality for organizations of all sizes.

Companies utilize traditional data stores and warehouses that serve their business needs. The

traditional tools of data stores and warehouses include massive scale-up servers, Relational

Database Management Systems (RBDMS), and traditional Extract, Transform, and Load (ETL)

tools. These technologies have an original design, built-in operational systems that rely on

structured data, a type of data that conforms to a highly constrained and strict format. The

physical limitations (i.e. server process power, lack of fault tolerance, etc.) of such systems are

minute in comparison to the rate of data growth for an organization and as the volume of the

data increases, so do the costs to maintain and scale-up. When correctly set up, Hadoop

contributes massive benefits by providing the business with opportunities to analyze and

correlate data across structured and unstructured data sources.

Inherent in the Hadoop platform design is the ability to consume, store, and process large data

sets with no constraints on the shape or format of the data. The Hadoop Distributed File System

(HDFS - the changed name of the aforementioned NFS) can be best explained by an adaptation

of the process from EMC Corporation, in which Hadoop takes a large data set, breaks it up into

manageable buckets, and distributes it across a cluster of nodes13. The newly bucketed data is

distributed and then processed with Hadoop’s MapReduce engine. Hadoop is built for easy

access to and storing of very large files with streaming data access patterns running on clusters

and can process enormous volumes of data rapidly due to its distributed nature of compute and

storage. Built from commodity hardware, Hadoop is prone to frequent failures and does not

work well with multiple small files, so it is important to remember that Hadoop is a tool that

works best with the properties of Big Data.


Hadoop has two components; Hadoop Distributed File System (HDFS) and MapReduce.

Hadoop Distributed File System (HDFS)

Figure 5: What is Hadoop? (Adapted from EMC2

Corporation13

)

Hadoop stores data types through HDFS and does so by utilizing three major components that

are also referenced by Tom White (2011), EMC2 Corporation, as well as others:

1. Clients A client can access HDFS multiple ways: application program interface

(API), command line interface (CLI), or other paths that exposes Hadoop’s API,

libraries, or protocols.

2. Namenode The brains of the whole HDFS operation, the Namenode manages the

file system namespace. It maintains the file system tree and the metadata for all the

files and directories in the tree. This information is stored persistently on the local

disk in two files: the namespace image and the edit log. Since it’s running the show,

the Namenode is considered a single point of failure (SPOF), so the vast majority of

the money spent in hardware will go to maintaining this piece of HDFS as well as

failsafe technologies.

3. Datanodes These are the worker nodes of the file system. They store and retrieve

blocks when requested by a client or by a Namenode. Each Datanode is assigned to

a specific Namenode. The Datanodes periodically sends a “heartbeat” to the

Namenode. This report provides the Namenode with lists of blocks that are available

for storage.


MapReduce

Hadoop processes data via the MapReduce function, which can be best explained by an

adaptation of the process from EMC2 Corporation. MapReduce is written using the Java

programming language and is a Java Virtual Machine (JVM) running on the Datanode servers13.

The MapReduce function starts by calling upon an API which then calls for a MapReduce job to

run. MapReduce is a two-step process that can be simply defined as: Map and Reduce. The

Map step takes the data input by the client and divides it into smaller sub-jobs called key/value

pairs. At the time of input, Hadoop supplies the key, which is unique to every record. The Map

function then distributes these pairs to the Datanodes. The Datanodes then process these even

smaller pairs and pass that result to their respective Namenode. The main idea of the Map

function is to divide the input into keys and values in such a way that the values, when put

together for the same key, start to make sense. The Reduce step collects the sorted value pairs

of all the sub-jobs and combines them to form an output. This output is the result of the original

data set, neatly positioned into the defined characteristics of the client.

Below is a simple visualization of a MapReduce word count process14 (Dutra 2015):

Figure 6: The Dutra Simple MapReduce Word Count Process14

(Dutra 2015)

What is R?

R is both a language and a working environment for statistical computing and visualization

creation. Developed at Bell Laboratories, R has become the popular of the two implementation

languages created by John Chambers and colleagues15. The S language falls short in

comparison because R is easier to use, it’s an open-source software, and it utilizes well-

designed publication-quality plots that can be produced, including mathematical symbols and

Mapping Algorithm Reducer Algorithm

Inputto be or not to be

to, 1be, 1or, 1not, 1to, 1

be, 11

not, 1or, 1to, 1

1

Key and

values will

then sort

themselves

into

alphabetical

order

Resultbe, 2not, 1or, 1to, 2


formulations where needed. R can be run and compiled on a wide variety of UNIX platforms and

similar systems (including FreeBSD and Linux), Windows, and MacOS15.

R provides a wide variety of statistical and graphical techniques, each uniquely developed.

These techniques include but are not limited to:

Basic Statistical Testing

Linear and Nonlinear Modeling

Classification(s)

Time-Series Analysis

Clustering

Text Mining Social Media through APIs

Visualizations-Graphs, Maps, Wordclouds, etc.

All of these techniques can be addressed because they are integrated into this one suite of

software that is complemented by a wide array of benefits. R can efficiently store massive

amounts of data that is loaded into its environment, which can then be utilized by a built-in

operator for calculations on arrays or in particular matrices. In this simple and effective

programming language, R initially comes integrated with a small collection of intermediate

functionalities for data and graphical analysis, which can be extended further via packages15.

These are user-created software which can be easily downloaded into your R environment with

only a few lines of code and are available through the Comprehensive R Archive Network

(CRAN), a vast number of websites that cover a wide range of modern statistics. Each package

comes with its own LaTeX-like documentation, which is used to supply comprehensive

documentation, both online in a number of formats and in hardcopy15.

A great way to begin experimenting with R would be to download its developmental

environment, RStudio, a creative interface developed in 2008 by a group of professionals with

various educational backgrounds that came together and were inspired by the R tool16.


A Little Taste of R...

Now that we have the basic background of R, here is a quick and fun little project that you can

do once you’ve downloaded R and RStudio from their respective websites16.

This example is from a quick data visualization project17, created to see what Twitter users were

saying in regard to major events of that year. Roughly 1000 tweets were pulled from Twitter with

a designated hashtag via three R packages17. The most frequent words from those tweets were

taken and the results were utilized in the following actions:

1. Performed a sentiment analysis17 (also known as opinion mining) on the results and

created a histogram of both positive and negative sentiment

2. Using the list of words pulled by R, then built a wordcloud using a website Tagxedo©

Figure 7 showcases results from the Twitter hashtag: “#RIPRobinWilliams”.

Figure 7: #RIPRobinWilliams Raw Histogram from R15

A Data Scientist would ask: what story does this visualization tell? We can only assume that the

frequency of negative terms has a higher frequency which, in this case, would make sense.

There just are not enough details behind the data or the visualization to validate a hypothesis.

This is a (very) basic starting point for a Data Scientist who may be trying to study a specific

social media topic, discover patterns within the topic, or analyze a basic trend in a social media

(in this case TwitterTM) environment. A Data Scientist could continue the research by classifying


and plotting the distribution of the emotions and polarity of the results, compare those results to

Twitter hashtags, then evaluate. There are many project avenues that can branch from a small

set of data and a very quick visualization of that data. The challenge to you is to investigate this

topic further and see what projects you could create!

Wordcloud of all word results for #RIPRobinWilliams

Figure 8: #RIPRobinWilliams Wordcloud created using www.tagxedo.com18

Data Scientists want to showcase the results of their studies, so it is crucial to expand your

toolset and experiment with different visualization tools. From my research, the most successful

Data Scientists “think outside the box” and venture to utilize whatever is available to create

powerful statements.

By no means does this little “project” fully demonstrate the complexity of an actual Data

Scientist project. As mentioned in the section, “What is Data Science?” the actual process for

such an undertaking is to have a strong hypothesis, a detailed procedure, analysis of the

results, and a final goal in mind which generates value for an organization. Again, the purpose

of this article is to help familiarize you with the roles of Data Scientists and to immediately get

you engaged with the tools they use.

Suffice it to say, there are countless other tools that you can get started with, but R and Hadoop

are the tools to use when beginning any Data Science venture. Not to mention the free videos,

documentation, and blogs online describing in detail the steps to download, experiment,

troubleshoot, and create with said tools. No one article could even begin to cover all of the


intricacies, but introduction to these two common tools will help you better understand the kind

of tools Data Scientists work with in their day-to-day activities.

Resources for Future Data Scientists

How to Get Involved

Now do you think it’s time to get involved? Getting involved in the world of Data Science

provides you with the opportunity to improve your skills, as well as showcase your work. An

artist is only as good as the portfolio they present, and we both know part of being a Data

Scientist is being an artist. If you’re anything like me, at some point this all has become

overwhelming and perhaps a bit intimidating. The amount of expertise demonstrated by some of

the available content can make even the hardened Data Science expert think that they’re work

has been nothing but child’s play. It is important to remember your work is your own, and the

goal in all of your efforts is not to be the best, because in such a dynamic field of study, there is

no “best”, only ongoing opportunities to grow and improve! The purpose is to approach each

problem with passion and purpose and learn as much as possible along the way.

Friendly Competition: Kaggle

Kaggle is a website for Data Scientists and supports a broad forum where its members actively

engage in questions on machine learning methodologies and best practices. Kaggle is best

known for its competitions, which allows users to submit created models for hosted

competitions. Of course, no Data Scientist is especially good when flying solo, so Kaggle

encourages users to join together to tackle some of their competitions. There are many types of

competitions, ranging from those that have hefty cash prizes to beginner walkthroughs of tools

and techniques. As of the time of this article, there are currently over 240,000 players and

746,855 entries for its 17 active competitions19. This is a website built on discovery, creativity,

and community. While a little healthy competition is never a bad thing, do not let the

leaderboards discourage you from giving it your best.

Beyond its competitions, Kaggle offers a wide variety of tutorials, job postings and forums to

help aspiring Data Scientists get started right away. Its content is comprehensive and engaging,

so it is one of the better places to begin to get involved.


Data Science Communities: Sharing is Caring

Looking back at the meaning of Data Science from the section “What is Data Science?”, you

notice a missing key element...the human element. Through implementation of statistical and

computer science elements, Data Science is built on the foundation of discovery through

collaboration. There are some basic ways to get the conversations flowing: through following

many of the Data Science-related Social Media profiles and commenting on their content to

starting your own blog. As long as the content you share is rich and you stay engaged, there are

no limitations to what can be discovered.

Below are three points to be cautious of when searching for a website that is dedicated to the

discussion and collaboration of Data Science and its projects:

1. Membership Not all Data Science community websites allow any user to sign

up. It is important to thoroughly read what the community is about and follow the

process for becoming a member, as certain knowledge requirements may need

to be proven as necessary.

2. Content Verification Be vigilant of the content shared among these

communities and what you put your name on when posting. It is easy to mistake

collaboration for plagiarism. Many communities will combat this issue by

approving posts before they are published, so it is in your best interest to find an

online community with everyone’s best (and legal) interest in mind.

3. Community Support and Engagement The most effective Data Science

communities will share training content as new techniques and technologies

develop, schedule webinars with organizations whose core competency is Data

Science and a offer a mature community that will engage a user to better help

them learn and enhance their Data Science knowledge.


Learn More

You’ve done your research, explored the Data Scientist communities, and even strutted some of

your stuff over the inter-webs...but it’s just not enough. You want more. It may be time to go

back to school, but where? This section will explain three methods available to enhance your

Data Science knowledge in a more structured environment. First, it will explore the growing

market of Massive Open Online Courses (or MOOCs) and describe what to look for in one that

would be worth your time and money. Then, it will go over what to look for in industry-standard

certification courses. Finally, it will showcase opportunities offered by colleges/universities and

some tips on finding the one best suited for you.

Learn via MOOC

MOOCs have exploded onto the internet within the past five years. Coined in 2008 by Dave

Cormier of the University of Prince Edward Island20, a MOOC is any course of study made

available over the Internet – usually without charge – to a great number of people. These

courses can be a series of videos or weeks-long virtual classroom sessions. Examples of well-

known MOOC websites21 are Udemy, ALISON, and Coursera. The price of a MOOC ranges

from being completely free to about a thousand dollars (fairly cheap in comparison to other

educational avenues). It is common for some colleges or universities to collaborate with MOOCs

to provide a certificate of completion to potential students who complete a course. These online

platforms allow select users (generally with an extensive background in a specific field) to share

videos and supplemental materials to teach the masses. Each course structure is run at the

discretion of the teacher and, depending on the course materials, can be submitted by users

and usually is graded.

MOOCs are great places to begin when testing the waters of Data Science, although the only

value they provide is to the user that takes the most out of them. Taking a MOOC in Data

Science domain is not a good selling point on a resume, but if you want to learn more at a low

cost, there truly is no better outlet. Here are three things that I advise you to consider when

searching for the right Data Science-related MOOC:

1. Content Overview Most courses provide users a detailed syllabus of what will be

covered in the course. Make sure this is as detailed as possible and is what you’ve

signed up for. It may be the case that this MOOC was set up to only cover a certain

topic (or product).


2. What is Required Be ready to commit time and resources to some of the extensive

MOOCs offered in the realm of Data Science. Some require you to turn in homework,

collaborate with individuals online, or download tools you are completely unfamiliar

with. While these tasks are not necessarily negative things, it’s recommended to use

your best judgment when initially signing up for such an undertaking.

3. Teacher’s Background This is something I wish I had the opportunity to dive into

when I went to college. The vast majority of teachers on MOOCs provide a general

background of their degrees, work experience, etc. In the realm of Data Science

MOOCs, they will more than likely have profound educational backgrounds in Statistics,

Computer Science, Market Research, and so on. Make sure you take some time to

validate their credentials and find out about their teaching style.

Learning in Certifications from the Industry Frontrunners

Another approach to delving deeper into the world of Data Science is to take certification

courses. Courses are offered through a variety of outlets. The first would be a classic, instructor-

led course, where you would attend a week-long class at a designated location. The second

would be a Virtual Instructor-Led Training course (VILT). These courses are delivered via a

CD/USB drive to a user by mail or streamed online. This provides the user an option to take a

course when is best for them. The cost of such certification courses tend to run in the price

range of $1,000 (for VILTs) to upwards of $5,000 (for an Instructor-led course). After the course

is taken, an exam is needed to be passed before certification can be achieved. The cost of an

exam is generally around $200 per attempt. While attaining these certifications will not

necessarily land you a job, they are fantastic jumping off points and provide a structured, self-

paced approach to learning Data Science.

Below I have alphabetically listed three organizations who offer certification tracks in Data

Science and Data Science-related topics:

1. Cloudera University Offers a wide range of certification tracks in Hadoop (both a

Developer and Administrator track) and an introduction course into Data Science. Its

offerings are presented either online or in a classroom setting with an option for

private tutoring. There are other hands-on tools available on the website (for an extra

cost) to assist in the Hadoop-specific certification tracks22.


2. EMC Proven Professional Offers one “open” (meaning the contents of the course

are vendor neutral) path for the Data Science discipline. The Data Scientist track

(EMCDS) has an Associate Level course, Data Science and Big Data Analytics

(DSBDA). This course comes in three different delivery options: Instructor-led, a USB

Flash Drive Video ILT, and a streaming VILT (VILT-Stream). The course provides

foundational training on Data Science. This offering comes with a lab, providing a

hands-on approach to learning the intricacies of Big Data projects23.

3. Hortonworks Offers in-person and virtual classroom training on multiple Big Data

and Data Science subjects, but is best known for its certification tracks that focus on

Hadoop. Its Hortonworks Data Platform (HDP) Data Science course covers the

basics of Data Science principles as well as some hand-on programming practice

with some of the languages used by Data Scientists24.

Saved by the Bell - Formal Education

My father always told me to stay in school. This advice proves worthy in assisting aspiring Data

Scientists ready to take their education to the next level. Many colleges today offer a wide

variety of Data Science Master’s programs that you may want to consider taking the time to

research and invest your time in as the average salary of a Data Scientist, according to

Payscale.com, is $95,587 per year25.

Try “Googling” Data Science Classes. At the time this article is being written, the outcome is a

yield of about 28,700,000 results26. To assist in wading through the massive output of results,

here are three key items to check for when choosing a Data Science education path:

1. Cost This one was obvious from the start and I am sure it is first on your list as well.

In a recent article written by Petersons “The cost of tuition and fees of graduate

degree programs varies greatly depending on the type of school and academic

program. On average, annual tuition at public colleges and universities totals

nearly $30,000 and at private schools nearly $40,00027.” Considering that the

average graduate degree requires around 15-20 months, the costs add up quickly.

2. In-Class vs. Online The majority of results you will find are for online courses

offered by colleges/universities. Be careful of these. While it may beneficial to go for

a degree in Data Science on a part-time online basis, the degree you would acquire

post-graduation may not actually be from the school to which you’ve applied. Some


colleges/universities only offer degrees through their online programs, meaning you’d

get a degree from XYZ University Online and not from the school (officially) itself.

3. Note the Curriculum Data Science has become a “buzzword”, and like many

buzzwords they tend to be used freely. Some colleges/universities have Data

Science as a focus in their Computer Science degree path. Make sure you sign up

for exactly what you want stated on your final transcript. Take note of opportunities to

work towards a Doctorate as well (if feasible financially and it’s something you’d like

to do). Some colleges/universities offer a Data Science Master’s degree program

that is the first step toward a PhD.

A Single Step...

Data Science is a discipline built on the foundation of discovery through the implementation of

advanced mathematical principals, scientific processing, data visualization, and cross-discipline

collaboration. Data Scientists are unique individuals who are naturally curious, have a passion

for discovery, and a yearning to dissect large volumes of data. While an educational background

in advanced mathematics or computer science is important, it is more important to have a

curiosity for the unknown, an appetite for discovery, and a desire to analyze data.

At the beginning of this writing journey I introduced the term Data Science and described what it

is and how it has embodied the framework of Big Data and the Internet of Things (IoT). I then

took a brief look at what I believe makes up the personas of Data Scientists and what skill sets

they use to confront the complex challenges faced in the data we generate today. We’ve gotten

cozy with two tools of the trade, took a short detour with a quick example using R, and learned a

bit about select online Data Science communities. We ended our excursion by exploring and

highlighting key aspects of the numerous available educational avenues that focus in the Data

Science discipline.

An aspiring Data Scientist will take the lessons learned throughout this article and begin their

journey. So if you’re ready to don the white coat, ask yourself again: Do you yearn to discover?

Do you want to decipher intricacies in unstructured data? Take seemingly disjointed files and

construct a cohesive masterpiece? Challenge complex hypotheses? Develop data-driven

models? If you’ve answered yes to any of the above, then becoming a Data Scientist is the

career path for you...enjoy the journey!


Appendix

1) Dhar, Vasant. Copyright 2013. Communications of the ACM, Vol. 56 No. 12, Pages 64-73

http://cacm.acm.org/magazines/2013/12/169933-data-science-and-prediction/abstract

2) Tukey, John (1977), Exploratory Data Analysis, Addison-Wesley.

3) O’Neil, Cathy and Schutt, Rachel. Copyright 2013. “Doing Data Science: Straight Talk from

the Frontline”.

4) Provost, Foster and Fawcett, Tom. Copyright 2013. “Data Science for Business: What you

need to know about data mining and data-analytic thinking.”

5) BBC Corporation. June 2014. Big Data: Are you ready for blast-off?

http://www.bbc.com/news/business-26383058

6) META Group. February 2001. "3D Data Management: Controlling Data Volume, Velocity, and

Variety."

7) Techopedia. “Internet of Things (IoT)”. http://www.techopedia.com/definition/28247/internet-

of-things-iot

8) Singh, Tarry. June 2014. Digital Transformation.

9) Dutra, Anthony. 2015. The Three Personas of a Data Scientist- The Nerd, the Artist and the

Business Professional

10) The Apache Software Foundation. http://lucene.apache.org/core/

11) Jeffrey Dean and Sanjay Ghemawat. December 2004. “MapReduce: Simplified data

Processing on Large Clusters.” http://labs.google.com/papers/mapreduce.html

12) White, Tom. Copyright 2011. “Hadoop: The Definitive Guide, 3rd Edition.”

13) EMC eLearning. “Big Data Solutions: Get Started with Hadoop”

14) Dutra, Anthony. 2015. “The Dutra Simple MapReduce Word Count Process”

15) About the R Language. http://www.r-project.org/about.html

http://cacm.acm.org/magazines/2013/12/169933-data-science-and-prediction/abstract

http://www.techopedia.com/definition/28247/internet-of-things-iot

http://www.techopedia.com/definition/28247/internet-of-things-iot

http://labs.google.com/papers/mapreduce.html

http://www.r-project.org/about.html


16) Downloading R: http://www.r-project.org/ and RStudio:

http://www.rstudio.com/products/rstudio/download/

17) University of Northern Texas. Mining Twitter and Sentiment Analysis w/R.

iralab.unt.edu/sentiment-analysis

18) Image created by utilizing Tagxedo. www.tagxedo.com

19) Kaggle. http://www.kaggle.com/

20)Cormier, Dave. http://davecormier.com/edblog/2008/10/02/the-cck08-mooc-connectivism-

course-14-way/

21) Udemy, ALISON and Coursera websites: https://www.udemy.com/ ; http://alison.com/ ;

https://www.coursera.org/

22) Cloudera University. http://www.cloudera.com/content/cloudera/en/training.html

23) EMC Proven Professional.

https://education.emc.com/guest/certification/framework/stf/data_science.aspx

24) Hortonworks. http://hortonworks.com/

25) Data Scientist, IT Salary.

http://www.payscale.com/research/US/Job=Data_Scientist,_IT/Salary

26) Google search for Data Science Classes

https://www.google.com/#q=Data+Science+Classes

27) Petersons. “Is the Cost of a Graduate Degree Worth It?”

http://www.petersons.com/graduate-schools/cost-graduate-degree-worth.aspx

http://www.r-project.org/

http://iralab.unt.edu/sentiment-analysis

https://www.udemy.com/

http://alison.com/

http://hortonworks.com/

http://www.payscale.com/research/US/Job=Data_Scientist,_IT/Salary

http://www.petersons.com/graduate-schools/cost-graduate-degree-worth.aspx


Code for Sentiment Analysis17

Credit to the University of Northern Texas for their blog and videos on Mining Twitter and

Sentiment Analysis w/R which inspired the provided example.

Feel free to copy and paste the code into your R or RStudio environment. Fill in the bold items

with your own input. Be sure to note the commented out portions (the R language comments out

lines with the # symbol) as some portions require extra input!

Scoring Tweets

Function: This code allows users to compare overall positive or negative sentiment of tweets.

Note: Anything in bold needs to be updated by user.

#Install packages

install.packages("ROAuth")

install.packages("twitteR")

install.packages("plyr")

install.packages("stingr")

library("ROAuth")

library("twitteR")

library("plyr")

library("stingr")

#Download Certifications-necessary step for Windows

download.file(url="http://curl.haxx.se/ca/cacert.pem", destfile="cacert.pem")


#Get your consumerKey and consumerSecret see the twitteR documentation for instructions

cred <- OAuthFactory$new(consumerKey='YOUR CONSUMER KEY',

consumerSecret='YOUR CONSUMER SECRET',

requestURL='https://api.twitter.com/oauth/request_token',

accessURL='https://api.twitter.com/oauth/access_token',

authURL='https://api.twitter.com/oauth/authorize')

#Generate PIN to complete the authorization process-necessary step for Windows

cred$handshake(cainfo="cacert.pem")

#save for later use for Windows

save(cred, file="twitter_authentication.Rdata")

#once saved, next time all you have to do is

#load("twitter_authentication.Rdata")

#registerTwitterOAuth(cred)

#the cainfo parameter is necessary on Windows

DATA.list<- searchTwitter("#INPUT HASHTAG HERE", n=INPUT AN INTEGER VALUE,

cainfo="cacert.pem")

DATA.df <- twListToDF(DATA.list)

write.csv(DATA.df, file=’INPUT FILE PATH HERE’, row.names=F)

#Create the Sentiment Function

score.sentiment <- function(sentences, pos.words, neg.words,.progress = 'none') {

require (plyr)


require (stringr)

scores<- laply(sentences, function(sentence, pos.words, neg.words){

#Clean up the data

sentence <-gsub('[[:punct:]]', '', sentence) #removes punctuation

sentence <-gsub('[[:cntrl:]]', '', sentence) #removes correctors

sentence <-gsub('\\d+', '', sentence) #removes digits

sentence<-tolower(sentence) #puts all characters in lowercase

#Split words

word.list<- str_split(sentence, '\\s+')

words<-unlist(word.list)

#compare our words to the dictionaries of positive and negative terms

pos.matches<-match(words,pos.words)

neg.matches<- match(words,neg.words)

#Return boolean positive or negative word matches

pos.matches<- !is.na(pos.matches)

neg.matches<- !is.na(neg.matches)

score<-sum(pos.matches)-sum(neg.matches)

return(score)

}, pos.words, neg.words, .progress=.progress)

scores.df<- data.frame(scores=scores, text=sentences)

return(scores.df)


}

#Read in positive and negative words

hu.liu.pos<- scan(‘INPUT FILE PATH', what='character',comment.char=';')

hu.liu.neg<- scan(‘INPUT FILE PATH', what='character',comment.char=';')

#Import the csv file

DATA_dataset<-read.csv(‘INPUT FILE PATH')

DATA _dataset$text<-as.factor(DATA _dataset$text)

#Score all Tweets

DATA.scores<-score.sentiment(RobinWilliams_dataset$text, pos.words, neg.words,

.progress='text')

#Visualization

hist(DATA.scores$score)

Word Frequency in Tweets

Function: This code allows users to pull tweets from a designated hashtag, and provide a table

of the most frequently used words

#Set the search parameter- necessary on Windows

DATA.list<- searchTwitter("#INPUT HASHTAG HERE ", n=1500, cainfo="cacert.pem")

DATA _text <- sapply(DATA.list, function(x) x$getText())

#create a corpus

DATA _corpus = Corpus(VectorSource(DATA _text))

#clean up the data

tdm <- TermDocumentMatrix(


DATA _corpus,

control <- list(

removePunctuation = TRUE,

stopwords <- c("INPUT WORDS THAT YOU WANT TO EXCLUDE”, stopwords("english")),

removeNumbers <- TRUE, tolower = TRUE)

)

#create a matrix

m <- as.matrix(tdm)

# get word counts in decreasing order

word_freqs <- sort(rowSums(m), decreasing = TRUE)

# create a data frame with words and their frequencies

dm <- data.frame(word = names(word_freqs), freq = word_freqs)

#Write to .csv

write.csv(dm, file='INPUT FILE PATH’, row.names=F)

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information is subject to change without notice.

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MAKES NO RESPRESENTATIONS OR WARRANTIES OF ANY KIND WITH RESPECT TO

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