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Introduction to Data Science

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Data Science is an interdisciplinary field making use of scientific methods, processes, algorithms and systems for extracting knowledge and insights from structured and unstructured data, and applies knowledge and actionable insight from data across a broad range of application domains. – PowerPoint PPT presentation

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Title: Introduction to Data Science


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SynergisticIT
The best programmers in the bay area Period!
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Introduction to Data Science
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What is Data Science?
As per Harvard Business Review Data scientists is
considered as the sexiest job of the 21st
Century. Data Science is an interdisciplinary
field making use of scientific methods,
processes, algorithms and systems for extracting
knowledge and insights from structured and
unstructured data, and applies knowledge and
actionable insight from data across a broad range
of application domains. Considering Data science
training to carve a career in the field can be
rewarding.
4
Data Science Definition
Data science is the practice of mining large data
sets of raw data, structured and unstructured for
identifying patterns and extract actionable
insight from it. It is an interdisciplinary field
and the foundation of data science includes
statistics, inference, computer science,
predictive analytics, machine learning algorithm
development, and new technologies for gaining
insights from big data. Data science life cycle
includes acquiring data, extracting and entering
it in the system. Next stage includes
maintenance, including data warehousing, data
cleaning, data processing, data staging, and data
architecture.
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Stages of Data Science Lifecycle
  • Data science has five stages in the lifecycle
  •  
  • Capture Data acquisition, data entry, signal
    reception, data extraction
  • Maintain Data warehousing, data cleansing, data
    staging, data processing, data architecture
  • Process Data mining, clustering/classification,
    data modeling, data summarization
  • Communicate Data reporting, data visualization,
    business intelligence, decision making
  • Analyze Exploratory/confirmatory, predictive
    analysis, regression, text mining, qualitative
    analysis
  • Data Science training is the right platform for
    learning various segments and aspects of
    lifecycle before venturing into the field.

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Why Businesses need Data Science?
The amount of data created every day has resulted
in need for professionals to tackle and make
sense of it. There is a huge mine of unstructured
and semi-structure data coming from various
sources and the traditional business intelligence
tools are just not sufficient to make sense of
it. Hence, Data science offers advanced tools
for working on large volumes of data coming from
various types of sources such as financial logs,
marketing forms, sensors, instruments, text
files, and multimedia files. Therefore, it is
wise to consider data science bootcamps in case
you wish to diversify in the field.
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Application of Data Science
  • Anomaly detection (fraud, disease, crime, etc.)
  • Automation and decision-making (background
    checks, credit worthiness, etc.)
  • Classifications (in an email server, this could
    mean classifying emails as important or junk)
  • Forecasting (sales, revenue and customer
    retention)
  • Pattern detection (weather patterns, financial
    market patterns, etc.)
  • Recognition (facial, voice, text, etc.)
  • Recommendations (based on learned preferences,
    recommendation engines can refer you to movies,
    restaurants and books you may like)

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Why consider a career in Data Science?
Data science is considered a great career path
and one of the in-demand career paths. There is
not even a single industry that cannot benefit
from data science. Apart from high-demand there
are high salaries to expect in the career. As per
Glassdoor, data scientist makes an average of
116,100 per year. SynergisticIT, Is the best
data science bootcamp to consider in case you
wish to be part of a high paying career.
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Job Roles in Data Science
  • Here are few roles to consider in Data Science
  • Data Analyst
  • Data Engineers
  • Database Administrator
  • Machine Learning Engineer
  • Data Scientist
  • Data Architect
  • Statistician
  • Business Analyst
  • Data and Analytics Manager
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