Companies like Microsoft, and Amazon have made number crunching cheap with R and Python through their cloud infrastructure by eliminating the need for a data warehouse.There are several datasets and easy-to-use machine learning algorithms available that companies can leverage free of cost.However, many companies have not been able to take complete advantage of all the possibilities as there are not enough qualified professionals to help organizations work with all the data they have. Organizations are a victim of lack of skills and not lack of resources. Without existing expertise to analyse data internally, it is difficult to make intelligent choices on what skills are required and how to hire people with those skills.
(Source - https://qz.com/1296930/hal-varian-googles-chief-economist-thinks-the-world-needs-more-data-scientists/ )
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Data scientist is a highly paid job role but poorly understood which is the sole reason why good data scientists keep quitting organizations.Many companies start hiring data scientists without even having a suitable infrastructure in place to get the most out of AI which leads to cold start problem.Moreover, many companies tend to hire experienced and senior data science professionals rather than hiring juniors leading to unhappy relationship between them and the employer.The job role of a data scientist is to write smart machine learning algorithms to derive valuable insights but he/she cannot do so because they are stuck up in sorting out the data infrastructure and creating analytic reports. This leads to frustration for the employers and disappointment to the data scientist making them quit the job.
(Source - https://www.techrepublic.com/article/why-its-your-fault-your-data-scientists-keep-quitting/ )
According to HBR, data scientist is the most sexiest job of 21st century with high demand . IBM forecasts suggest that the number of data scientists required will increase by 28% by 2020 with 2.7 million open job roles for data science professionals in US alone. Still, these job roles are the most difficult to fill due to the skills gap and require on average minimum of 45 days to fill. Kristin Rahn, Director of Product Management for Data Science and Analytics says that there is a shortage of people for all STEM roles and students find analytics classes most disinteresting to attend. Another reason that Krisitn highlights for the skills gap is that data science is performed by business for the benefit of others which means that data science professionals are required to have communication and consulting skills. There are professionals who have the technical knowledge but they lack in communication and consulting which are required to become valuable in a commercial environment.
(Source - https://www.computing.co.uk/ctg/opinion/3034263/data-scientists-are-in-demand-and-well-paid-so-why-is-there-a-skills-gap )
Here are the steps that Suneel Chakravorty, Co-Founder and Partner at Simple Fractal wants you to follow to begin your way into data science-
i) Choose the right Dataset - Regardless of the nature of your organization (whether you are into insurance or manufacturing or any other business domain) , you must first identify the dataset that will help you advance the business. Doing so will give a healthy focus to your data science mission.
ii) Set an initial goal - Decide what is the first thing you want to do with data. Set a goal that has achievable milestone and can render business value.
iii) Skill up your Team with the Required Tools (SQL, Python, R, and more) based on the Requirements-Your team should learn to use the tools and also have basic intuition behind models to ensure that they are proficient enough to build useful data products.
iv) Hack on the Data - Play with the data to derive initial results and keep iterating the process.
(Source - https://www.forbes.com/sites/forbesnycouncil/2018/06/27/four-simple-steps-to-get-your-organization-started-with-data-science/#3ae312546d15 )
For a patient struggling with life in ICU, every second counts and the decision a healthcare professional makes can have a life or death consequence.Healthcare organizations are leveraging analytics to transform clinical and epidemiological data into actionable insights that can save and enhance a patient’s life which would otherwise not have been possible. One such startup is CLEW Medical that uses big data and machine learning for predictive analytics which guide patient care in the Intensive Care Unit.With the use of CLEW’s platform, physicians can now use real-world clinical data to find out what might work best for a patient in ICU rather than depending on any gut instinct , defensive medication or personal experience.
(Source - https://www.hcanews.com/news/understanding-healthcares-new-life-savers-data-scientists )
MNC’s and Indian companies are on the verge of hiring data science experts who can help them garner insights from big data.A survey was conducted on 3 groups of people -job seekers, students and recruiting managers to find out the variations in skill sets, work experience and educational qualifications to get a complete idea of the hiring scenario in the data science industry. Here are some interesting insights that are real eye-openers for people looking for a data science job -
i) 33% of the respondents to the survey mentioned that formal education is essential to get a data science job considering the current trends in the industry.
ii) 48% of the respondents mentioned that some kind of a prior programming experience is important to get a data science job.
iii) 35.8% of respondents said that some kind of a job experience is important to land a top gig in the data science sector.
iv) 23.2% of the respondents said that it is difficult but not impossible to transition from a non-datascience background to a new tech like data science.
v) 42.7% consider internship as the best way to land an entry-level data science job.
vi) 47% said that knowing Python programming language is necessary to get a data science job with 39% respondents considering R programming language as the next best favorite for data science.
vii) The three important skills necessary to flourish in data-science are -Statistical Modelling (35%), Machine Learning (28%) and Business Intelligence (17%).
(Source- https://analyticsindiamag.com/annual-survey-on-data-science-recruitment-in-india-2018/ )