Πέμπτη, 22 Ιουνίου 2017

How to become a data scientist – Complete Guide

How to become a data scientist – Complete Guide





How to become a data scientist – Complete Guide





5 Things to learn to becom data scientist
5 Things to learn to become data scientist Infographic

If you are looking for a High Paying Quality job profile in IT
Industry  . Data Scientist comes up in your wish list . When I started
searching about How to become a data Scientist. I got so many
informative article over internet. These article were full of
information but quite massive and distributed.

So ,In this article I have mentioned best way to learn data science
in very compact and serial way .If you follow all these tips , You will
be a Data scientist in very less efforts.   Actually, Data science is
combination of so many interrelated fields like Machine Learning, Data
visualization and Programming. It also includes Mathematics and
Statistics principles. This combination and its complexity create
massiveness and confusion in reader’s mind.

There are so many varieties in sub skills under Data Science like Programming language (python vs r), Machine learning algorithms
(Supervised Machine Learning Vs Unsupervised Machine learning). Which
skill should get higher priority for learning is the major pain area for
data science learner. If you are experiencing the the same, this is the
right place for solving all your problem. We have created a straight
road map to assist you in your confusion of How to become a data
scientist.



How to become a data scientist image
How to become a data scientist image



How to become a data scientist –  Complete Guide

As per the data scientist job description available across the
industry, we can divide the skill set in five major classes. I have
arranged these five skills in the order of priority. We have also
created a road map in the form of info graphics for your better
understanding. These five steps are best way to learn data science.

1.Brush up your Basic Mathematics and Statistics for Data Science –

Probability and statistics are fundamental tool required for every
predictive analytics. It is badly used in every corner of data science.
Especially Machine Learning Data Mining is the field where you cannot
take any step without having dirty hand in Mathematics and Statistics.

You may find Free eBook on statistics for data science by just
clicking the below link. Probability and statistics interview question
for data science is must covered topic in this section as well for
better performance in your job interview. Every Data Scientist job
description contains separate column of Probability understanding as
required skill.

2. Learn r Programming | python for data science-

You have to opt one programming language in every data science project. The possible combination for learning are –

  1. Learn r Programming
  2.  python for data science
  3.  java for data science.
This is the second step in the series of best way to learn data
science. If you are doing any data science project, you need data. Data
Analyst can use or produce data  by external file source like excel or
you have to fetch via some API call using some programming language.
Finally , You have to use at least any programming language to
accomplish these task .I will recommend you to refer our article Why Python for Data Analysis . This article is focusing over python but after reading it you can relate it with other programming language.

If you want to make your hand dirty with python and you are looking for

a short overview type article , Python essentials in 5 minutes will be the best article for you .

3. Learn Applied Machine Learning Algorithms for Data Science

Machine Learning algorithms and Trained tool are essential for data
science. There are so many tools available where you can train your
machine learning model. This Model you be integrated in your existing
Data science project. Data science project. Lets understand it with a
example , Suppose we have to create a price prediction algorithms for
any financial firm and we have 10 year past data only. We will build the
model using some market logic for the prediction of next year . If we
some how able to make automatic feed back system in our existing system
to add the current real outcome as experience .So next time we will have
11 year training data . In the same way as the time passes our system
will be more  precise in predictive analytics . This approach is called
 machine learning where machine start learning it self with its past
experiences .

I will suggest you to take over view of Machine Learning Library . This will improve your understanding .



4. Learn Data visualization Tool for Data Science

Data Scientist mine the data and extract some meaningful result out
of it .These result could be any pattern , any indicator or something
else . To understand the hidden information out of the huge raw data ,
You have to use some data visualization tool. In fact , We have so many
data visualization toll available all around us . Companies from
different industries are using these tools very frequent . Some of them
are very popular and frequent like-

  1. Qlik Sense and QlikView
  2. D3.js
  3. Tablea

5. Learn Big Data Technologies for Data science-

this comes last but quite effective.Specially If you want to become
full stack data scientist . There are so many big data tool and
technologies  . Hadoop is open source framework for Big data . Spark
with java and Scala is also quite frequent use framework . There is a
complete list of required Big Data Tool in Data science .For Beginner , I
will suggest to learn Hadoop first .

Finally , If you learn all these technologies , You can start your
career as a Data Scientist .I mean these all skill are essentials for a
Data Scientist  . Along with this , If you are dealing with text
analytics You may use Natural Language Processing . Natural language
Processing is NLP in short . NLP as a short and trendy word in field of
technology. All big and innovative companies are working on NLP .
Facebook and Google also come in these list .

Lets Zoom in Machine Learning Data Mining  . Machine Leaning is
itself a branch of  Artificial Intelligence .Programmers and application
Designer are using machine learning data mining , Data science , AI in
their existing Application .This Integration are migration their
Technology  into new era.There are so many tools like Amazon Machine
Leaning , Azure ML Studio , Apache singa are in trend.

Anyways , Lets conclude all. Data Scientist is some one who is good
at Maths , Programming and Analytics .These three are major branches in
their itself . Their combination creates a meaningful data .
Unstructured data is majorly available around us . Most Of the time we
create a unstructured data unknowingly.For Example the video of  our
activity is a unstructured data in itself.To handle this a major pain
area in field of Data Science . So If  you learn unstructured data
technologies with data science , You are future ready product.

End notes

I think , We have have done enough discussion over the topic How to
become a Data Scientist . If you want to explore more on Machine
Learning , You can refer our article What is Machine Learning ? .

Παρασκευή, 26 Μαΐου 2017

Playing the numbers game: 21st Century law will be based on math and data analytics

Playing the numbers game: 21st Century law will be based on math and data analytics



Drew Hasselback
Tuesday, May 23, 2017

Lawyering in the 21st century will have a lot to do with math and data analytics Files
As Zev Eigen explains it, the future of law will have a lot to do with math.



Eigen is a lawyer in Los Angeles with Littler Global, a worldwide
firm that focuses on management-side employment law. He’s also a data
scientist with a PhD from the Massachusetts Institute of Technology. He
has combined his expertise to become global director of data analytics
at the law firm.


So Eigen seems to know a thing or two about the kind of math that
sprawls across black boards in a fog of letters and symbols. This is
precisely the scary kind of math that a good number of lawyers fear.


But Eigen says that’s okay. The future of law won’t demand that
lawyers know how to build those equations themselves, he explains. The
future will be about knowing how to benefit from the information such
math can provide.


Eigen is an advocate for the use of data to discover things like
how people collaborate, to predict how a regulator might respond to a
case, or to streamline decision making so lawyers — or perhaps even
machines — can make snap decisions on certain matters.



Lawyers can benefit from this information without having to know how the math behind it works, he explains.


It’s like driving. A car may be a complicated machine, but you
don’t need to be an engineer to know how to drive a car. Eigen wants
lawyers to learn how to “drive” data analytics.


“I’m a big proponent and a big advocate of making sure we’re all good consumers of the information,” Eigen said.


Eigen was a keynote speaker at Lawyering in the 21st Century, a
forum hosted by LexisNexis and Ryerson University’s Legal Innovation
Zone. The event, which took place at Ryerson in Toronto on last week,
was based on the premise that change is coming quickly, and lawyers need
to be ready.


Postmedia Network
Postmedia Network
Lawyers have been comfortable with incremental change, but technology
is empowering consumers to disrupt the profession faster than lawyers
have been used to, said Chris Bentley, executive director of the Legal
Innovation Zone.


“Consumers want what they want, and the way they want it. And
they want it at the price they are prepared to pay,” Bentley said.
“Amazon and Google got big not by serving the richest, but by serving
the rest.”


Change will not come solely in the form of technology, the forum was told.


Ben Heineman, Jr., who was the top in-house lawyer at General
Electric Corp. for about 18 years until he retired in 2005, said a
corporate general counsel must do more than rubber-stamp a CEO’s actions
or a board’s decisions.


He said a GC must also be an outstanding expert, a wise
counsellor and an accountable leader — someone willing to make not just
legal decisions, but also moral judgments.


Heineman said the skill of the future will be knowing where to
land on the decision-making continuum. Some general counsel are
inveterate naysayers, and they risk being excluded from future decision
making, he said. Others are inveterate yeasayers, and they risk being
indicted, he added. “Somewhere between those two extremes is where we
have to operate.”



The forum involved more than sitting and listening. In a design
workshop, participants had to brainstorm ideas for a system or app that
might get people to actually read legal documents before agreeing to
them. In a “Lean Six Sigma” workshop, lawyers had to build a flow chart
of a typical legal task so they could pin point the root cause of a
specific problem or streamline ways to complete the task faster.


But a lot of the future involves math, and some Canadian firms are already diving in.


McMillan LLP, for example, is working with a data scientist to
analyze the firm’s dockets, personnel assignments, timing, and document
flow. The idea is to see if there are ways the firm can more accurately
determine its own costs and margins. “Most law firms have this
information. What they’re not doing is analyzing it systematically,”
says Tim Murphy, a partner with McMillan in Toronto.


Other firms are pursing other forms of innovation. The Toronto
office of Baker & McKenzie LLP has just been chosen to serve as the
firm’s global Innovation Lab for Multidisciplinary Collaboration.


For some, the math underpinning a lot of these developments is
complex, but Eigen insists that lawyers should never be afraid to admit
that they can’t understand it.


Eigen said that before hiring any data scientists, lawyers should
ask them to explain their methods and their math. This could well be an
efficient way to determine just how well they know their stuff.


“They should be able to explain it to you in the same way you are tasked with explaining things to clients,” Eigen said.


Financial Post


dhasselback@nationalpost.com

twitter.com/vonhasselbach

(10) Data science Training in Hyderabad - Δημοσιεύσεις

(10) Data science Training in Hyderabad - Δημοσιεύσεις



Φωτογραφία του χρήστη Data science Training in Hyderabad.