Pune Data Science Corporate Workshop
Mode of Delivery – The classes are held both online and in physical classrooms.
Audience – We have a global audience that logs in to work hand in hand with our world-class instructors.
Certification – Available in 25+ Countries NobleProg Certification is accepted globally
What is the potential of Data Science?
Companies are now realising the potential of leveraging data and analytics to help in business decision-making. Whether it is about identifying the information needs relevant to a business context or to fetch and organise data to address such needs, data professionals are increasingly playing a valuable role. Since India has always been seen as a technology outsourcing hub, so is true, when it comes to analysing the data using analytics and big data technologies. Companies all around the globe are looking to hire specialists who can do the task. Hence, the scope for data analysts and data scientists is huge.
Why should you go for Data Science?
“Data scientist” seems to be the IT job of the moment. But how much of what you’ve heard is hype and conjecture, and how much of it is based on facts? Usually, when something sounds too good to be true, it probably is. However, the demand for data science is taking the world by storm, and companies – large and small – are clamoring to find employees who can understand and synthesize data, and then communicate these findings in a way that proves beneficial to the company.
Below are the top 10 reasons to consider pursuing a career in Data Science.
#1 The Job Outlook
Don’t expect this bubble to burst anytime soon. According to a report by McKinsey & Company, by 2018, the U.S. will have anywhere from 140,000 to 180,000 fewer data scientists than it needs. And the shortage of data science managers is even greater. Roughly 1.5 million data decision-making managers will be needed by 2018. At some point, the frenetic pace at which employers pursue data scientists will slow down, but it won’t happen anytime soon.
#2 The Salaries
According to an O’Reilly data science salary survey, the annual base salary of U.S.-based survey respondents was $104,000. Robert Half’s tech guide places the range between $109,000 and $153,750. And in the Burtch Works data science salary survey, the median base salary ranges from $97,000 for Level 1 contributors to $152,000 for Level 3 contributors.
In addition, median bonuses start at $10,000 for Level 1 contributors. As a point of comparison, the U.S. Bureau of Labor Statistics (BLS) reports that lawyers earn a median annual wage of $115,820.
#3 The Management Salaries
Data science managers can earn almost as much – and sometimes more – than doctors.
Burtch Works reveals that Level 1 managers earn a median annual base salary of $140,000. Level 2 managers make $190,000, and Level 3 Managers earn $250,000. And that puts them in pretty good company. According to the BLS, pediatricians, psychiatrists, and internal medicine doctors earn a median annual wage between $226,408 and $245,673. So without years of med school, residencies, and medical debt, you might earn more than the person who holds your life in his/her hands on the operating table. Cool. Scary, but cool.
And when you factor in median annual bonuses, data science managers out-earn many surgeons. Median annual bonuses for Level 1, 2 and 3 managers are $15,000; $39,900; and $80,000, respectively.
#4 The Work Options
When you become a data scientist, you can work practically anywhere your heart desires. While 43% of these professionals work on the West Coast, and 28% are in the Northeast, they’re being employed in every region in the country – and abroad. However, you might be interested in knowing that the highest salaries in the U.S. are on the West Coast.
And you’re probably not surprised that the technology industry employs the most data scientists, but they also work in other industries ranging from healthcare/pharma to marketing and financial services to consulting firms to retail and CPG industries.
In fact, data scientists even work for gaming industries, and 1% work for the government.
#5 The Sex Appeal
The prestigious Harvard Business Review hailed data scientist as the sexiest job of the 21st Century. How on earth is that possible? Are data scientists suggestively dangling the data in front of their employers? Are they whispering sweet algorithms in their employer’s ear? No (at least I don’t think so), but some of them work with cool startups, and also mammoth companies like Google, LinkedIn, FaceBook, Amazon, and Twitter. In essence, their sex appeal lies in the fact that everyone wants them, but they’re hard to acquire.
#6 The Experience Factor
“Experience” is probably one of the most common words found in a job description, and frankly, companies usually want employees with a ton of it.
However, data science is such a relatively new field that Burtch Works reports 40% of data scientists have less than 5 years of experience, and 69% have less than 10 years of experience. So scroll back up to Reason #2: Salaries to match up the wages with the experience levels. Level 1 individual contributors typically have 0-3 years of experience. Level 2 individual contributors usually have 4 to 8 years of experience, and level 3 individual contributors have 9+ years of experience.
#7 The Variety of Undergraduate Majors
Since data science is such a new major, many colleges are scrambling to create undergraduate degree programs. In the meantime, data scientists hail from an assortment of academic backgrounds, including mathematics/statistics, computer science, engineering, and natural science. Also, some data scientists have degrees in economics, social science, business, and even medical science.
#8 The Variety of Education Options
If you pursue an online Master’s Degree in Data Science, you don’t have to sit in a classroom all day. You can take courses online from anywhere in the world, with the luxury of studying at your own pace.
#9 The Lack of Competition
Not only is there a shortage of data scientists, but professionals in other fields don’t necessarily want to step up to the plate. According to a recent joint report by Robert Half and the Institute of Management Accountants, employers are looking for accounting and finance candidates who can mine and extract data, identify key data trends, and are adept at statistical modeling and data analysis.
But the report reveals that most accounting and finance candidates don’t have any of these skills – in fact, many colleges don’t even teach this level of analytics to students majoring in a financial discipline.
#10 The Ease of Job Hunting
Because data scientists are in such high demand and the supply is so limited, organizations have recruiters solely dedicated to finding these professionals. While candidates in other fields are harassing recruiters and pestering hiring managers, as a data scientist, you merely need to let it be known that you’re looking for a job . . . or maybe, you’re just thinking about looking for a job. In fact, the need is so dire that even if you already have a job, recruiters will try to lure you away with a better compensation/benefits package.
Why Is This Program Different:
The Instructors – Our instructors are industry experts, people who have been there and done that. They not only encourage questioning but also give solutions that are practical and applicable at an enterprise level.
The Practice – We provide an actual cluster for hands-on practicing. It removes the need to install virtual machines and makes learning easier and fun.
The Curriculum – Created by industry experts to equip attendees to hit the ground running. Our interactive sessions along with the curated curriculum make starting a project at work or attending an interview or just upscaling your career a cake walk.
Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
Introduction to Data Science for Big Data Analytics
Introduction to Data Analytics lifecycle
Getting started with R
Getting started with Hadoop
Integrating R and Hadoop with RHadoop
Pre-processing and preparing data
Exploratory data analytic methods in R
Regression (Estimating future values)
Assessing model performance and selection
Support vector machines for classification and regression
Identifying unknown groupings within a data set
Discovering connections with Link Analysis
Association Pattern Mining
Constructing recommendation engines
Who should take this program?
Anyone having zeal to learn new technology can go for it. Students and professionals aspiring to make a career in Data Science should opt for the program.
Corporate Executives looking to connect corproate strategy to technology
Government Executives looking to better understand opportunities
High school & college students
Supply Chain Managers
CEO’s, Boards, and Senior VP’s
Entrepreneurs looking for something new
Consultants and Professional Service Providers
Anyone looking to better prepare for long term career potential in the future
For any enquiries you can always reach us at dHJhaW5pbmcgfCBub2JsZXByb2cgISBpbg== or call us at +91 88 001 555 18, +91 98 18 063 614