What is data science
statistical tools to existing
data to generate new
Converting new data
insights into (often small)
changes to business
More efficient and effective use of staff and resources
Data Analysis Has Been Around for a While…
Introduction to Data Science
Most organizations are opening it’s door to Data Science.
Companies are focusing on Data Analytics for their growth.
Businesses are focusing on reaping major benefits from the data
they already possess.
Importance of Data Science
Data Science and Its Growing Importance.
An international field, data science deals with processes and
systems, that are used to extract knowledge or insights from large
amounts of data.
In 1940’s and 1950’s-data storage was a big issue.
Today we have ample data storage opportunities.
History of Data Science.
Before we see into the definition of Data science, let’s see the history of Data
It is nothing new that have been introduced today.
Data existing in 1940’s and 1950’s as well however it was not viewed the way
we see today.
Statisticians play an important role during this time period and they used to do
data analysis manually.
They lacked use of computer for this purpose as such it’s importance we less.
“Big Data” Sources
User Generated (Web & Mobile)
Education of a Data Scientist
The education requirements for data scientists are among
the steepest of all IT occupations.
Approximately 40% of data scientist positions require an
advanced degree, such as a Master's, MBA or PhD. ...
Research and compare data science training programs and
business intelligence degrees online.
Responsibility of a Data Scientist
Data scientist duties typically include creating various
machine learning-based tools or processes within the company,
such as recommendation engines or automated lead scoring
Usually, it's considered normal to bring people with different
sets of skills into the data science team.
Career Possibility of a Data Scientist
Some of the prominent Data Scientist job titles are:
Business Intelligence Manager
Skill’s of a Good Data Scientist
Here are some important attributes and skills, according to IT
leaders, industry analysts, data scientists, and others.
Critical thinking. ...
Machine learning, deep learning, AI. ...
Data architecture. ...
Risk analysis, process improvement, systems engineering. ...
Problem solving and good business intuition.
Technical skills of a Data Scientist
Other technical skills required to become a data scientist include:
Programming: You need to have
the knowledge of programming languages like Python, Perl,
C/C++, SQL and Java—with Python being the most
common coding language required in data science roles
Data scientist skill
Data scientists are skilled professionals who are responsible for
finding out insights from huge volumes of structured and
unstructured data. The importance of data scientists is spiking
up and businesses are heavily relying on data analytics to
boost decision-making capabilities and lean on machine
learning as an integral part of IT development.
Benefits of being a Data Scientist
The perks of being a Data Scientist
Sexiest job of the century. ...
Freedom to work. ...
A chance to work with big brands. ...
The payoff is handsome. ...
Proper training and certificate course. ...
Data science jobs in demand. ...
Different roles in the industry. ...
A safe career to pursue.
Top Programming Languages For Data Scientists
Python: It remains to one of the most popular languages, both in terms of pay it
offers and popularity amongst recruiters looking for Python skills. ...
All these eight field sets are different from each other.
People who practice Data Science are called Data Scientist.
They analyze complex data problems.
Notas do Editor
Be specific and direct in the title. Use the subtitle to give the specific context of the speech. -The goal should be to capture the audience’s attention which can be done with a quote, a startling statistic, or fact. It is not necessary to include this attention getter on the slide.
Fisher: ANOVA (analysis of variance: a collection of statistical models, used to analyze the differences among group means and their associated procedures (such as "variation" among and between groups)), Fisher’s exact test. Also credited with quote “correlation does not imply causation” – lifetime pipe smoker, he derided papers showing a link between smoking and cancer. Deming – quality control – statistical sampling Luhn – indexing, IR principles – use text and data to inform Business Decisions Tukey – exploratory data analsysis – led to S, S+ and R Howard Dresner – modern proponent of BI Tom Mitchell’s ML book – still a best-seller Fourth Paradigm – data driven scientific discovery – inspired by Jim Gray’s work at MSR. His primary research interests are in databases and transaction processing systems -- with particular focus on using computers to make scientists more productive. He and his group are working in the areas of astronomy, geography, hydrology, oceanography, biology, and health care. Other scientific discovery paradigms: empiricism, analysis and simulation Google Inc alphabet Inc. Google began in January 1996 as a research project by Larry Page and Sergey Brin when they were both PhD students at Stanford University in Stanford, California. Peter Norvig – simple models + lots of data > complex models Data Deluge – exponential growth in data volume, the Economist 2010, Businesses, governments and society are only starting to tap its vast potential
Parece que tem um bloqueador de anúncios ativo. Ao listar o SlideShare no seu bloqueador de anúncios, está a apoiar a nossa comunidade de criadores de conteúdo.
Atualizámos a nossa política de privacidade.
Atualizámos a nossa política de privacidade de modo a estarmos em conformidade com os regulamentos de privacidade em constante mutação a nível mundial e para lhe fornecer uma visão sobre as formas limitadas de utilização dos seus dados.
Pode ler os detalhes abaixo. Ao aceitar, está a concordar com a política de privacidade atualizada.