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Data_Scientist.php
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Data_Scientist.php
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<?php
// Initialize the session
session_start();
?>
<!DOCTYPE html>
<html lang="en">
<?php include 'header.php'?>
<!-- Hero-area -->
<div class="hero-area section">
<!-- Backgound Image -->
<div class="bg-image bg-parallax overlay" style="background-image:url(./img/bgc2.jpg); " ></div>
<!-- /Backgound Image -->
<div class="container">
<div class="row">
<div class="col-md-10 col-md-offset-1 text-center">
<ul class="hero-area-tree">
<li><a href="main.php">Home</a></li>
<li><a href="blog.php">Knowledge Network</a></li>
<li>Data Scientist</li>
</ul>
<h1 class="white-text">Data Scientist</h1>
</div>
</div>
</div>
</div>
<!-- /Hero-area -->
<!-- Blog -->
<div id="blog" class="section">
<!-- container -->
<div class="container">
<!-- row -->
<div class="row">
<!-- main blog -->
<div id="main" class="col-md-9">
<!-- blog post -->
<div class="blog-post" style= "text-align:justify; ">
<h2>Job Description</h2>
<!-- row -->
<div class="row">
<p style="font-size:16px;" >A data scientist is someone who makes value out of data. Such a person proactively fetches information from various sources and analyzes it for better understanding about how the business performs, and to build AI tools that automate certain processes within the company.</p>
<p style="font-size:16px;" >Data scientist duties typically include creating various machine learning-based tools or processes within the company, such as recommendation engines or automated lead scoring systems. People within this role should also be able to perform statistical analysis.</p>
</div>
<!-- /row -->
<!-- row -->
<div class="row">
<h2>Skills required</h2>
<p style="font-size:16px;">A strong background in math, science, and computer science is a must for aspiring hardware engineers. They also, however, should be adept communicators capable of conveying instructions in verbal and written forms. Other critical skills include</p>
<ol style="font-size:16px;">
<li><b>1. </b>excellent analytical and problem-solving skills</li>
<li><b>2. </b>experience in database interrogation and analysis tools, such as Hadoop, SQL and SAS</li>
<li><b>3. </b>exceptional communication and presentation skills in order to explain your work to people who don't understand the mechanics behind data analysis</li>
<li><b>4. </b>planning, time management and organisational skills</li>
<li><b>5. </b>the ability to deliver under pressure and to tight deadlines</li>
<li><b>6. </b>teamworking skills and a collaborative approach to sharing ideas and finding solutions.</li>
</ol>
</div>
<!-- /row -->
<!-- row -->
<div class="row">
<h2>Educational Requirements</h2>
<ol style="font-size:16px;">
<li><b>1. </b>BSc/BA in Computer Science, Engineering or relevant field; graduate degree in Data Science or other quantitative field is preferred.</li>
<li><b>2. </b>You'll be expected to know some programming languages such as R, Python, SQL, C or Java and have strong database design and coding skills.</li>
<li><b>3. </b>A postgraduate qualification, such as a Masters or PhD, can be useful and many data scientists have one. It is especially helpful if you're considering a change of career or are interested in learning analysis skills.</li>
<li><b>4. </b>You'll typically need a mathematical, engineering, computer science or scientific-related degree to get a place on a course, although subjects such as business, economics, psychology or health may also be relevant if you have mathematical aptitude and basic programming experience.</li>
<li><b>5. </b>Understanding of machine-learning and operations research</li>
<li><b>6. </b>Strong math skills (e.g. statistics, algebra)</li>
</ol>
</div>
<!-- /row -->
<!-- row -->
<div class="row">
<h2>Duties and Responsibilities</h2>
<ol style="font-size:16px;">
<li><b>1. </b>Identify valuable data sources and automate collection processes</li>
<li><b>2. </b>Undertake preprocessing of structured and unstructured data</li>
<li><b>3. </b>Analyze large amounts of information to discover trends and patterns</li>
<li><b>4. </b>Build predictive models and machine-learning algorithms</li>
<li><b>5. </b>Combine models through ensemble modeling</li>
<li><b>6. </b>Present information using data visualization techniques</li>
<li><b>7. </b>Propose solutions and strategies to business challenges</li>
<li><b>8. </b>Collaborate with engineering and product development teams</li>
</ol>
</div>
<!-- /row -->
<!-- row -->
<div class="row">
<h2>Salary</h2>
<p style="font-size:16px;">An entry-level Data Scientist with less than 1 year experience can expect to earn an average total compensation (includes tips, bonus, and overtime pay) of ₹536,700 based on 539 salaries. An early career Data Scientist with 1-4 years of experience earns an average total compensation of ₹786,214 based on 2,351 salaries. A mid-career Data Scientist with 5-9 years of experience earns an average total compensation of ₹1,393,592 based on 814 salaries. An experienced Data Scientist with 10-19 years of experience earns an average total compensation of ₹1,747,802 based on 212 salaries. In their late career (20 years and higher), employees earn an average total compensation of ₹1,100,000.</p>
</div>
<!-- /row -->
<!-- row -->
<div class="row">
<h2>Companies offering Data Scientist role</h2>
<ol style="font-size:16px;">
<li><b>1. Pinterest</b> </li>
<li><b>2. Microsoft</b></li>
<li><b>3. Accenture</b></li>
<li><b>4. Intel</b></li>
<li><b>5. Oracle</b></li>
<li><b>6. Uber</b></li>
</ol>
</div>
<!-- /row -->
</div>
<!-- /blog post -->
</div>
<!-- /main blog -->
</div>
<!-- row -->
</div>
<!-- container -->
</div>
<!-- /Blog -->
<?php include 'footer.php'?>
</html>