Data Science - Conducive to a Good Career

17-Oct-2020

Advances in the field of computer science and information technology have enabled Data Science to advance from cleaning datasets and applying statistical methods to becoming a field that contains Predictive analytics, Data mining, Data examination, Business intelligence, Deep learning, AI, and more. Now, there may be many who still think that Data Science is only a fad and the hype around it will drain away in time. But nothing could be further from the truth. The truth is that Data Science is picking up steam as businesses and organizations obtain tremendous volumes of data to bring changes and improvements to their modus operandi and processes. As the amount of data grows more and more voluminous organizations attempt to process the data generated, skilled Data scientists will become more and more engrossed by data associations of every kind imaginable. For instance, a glance at the job board in highly-rated job portals shows top associations duelling with each other to obtain and secure Data scientists. The associations include big names like Facebook, Twitter, Airbnb, Apple, Linkedin, IBM, and Paypal among many others. It is a very fruitful time for anyone who wishes to improve and increase their abilities in Data Science and Big Data analytics to exploit the Data Science job openings.

Are Data Science Courses in demand?

The following is some information about the Data scientists/Data analytics demand in the industry -

• By 2020 the number of Data Science and Analytics job openings is expected to go up from 364,000 postings to around 2,720,000.
• The most rapidly developing jobs are Data Scientists and Advanced Analysts, who are anticipated to see job spikes as large as 28% by 2020.
• Jobs requiring AI skills are paying a dividend of $114,000. Advertised Data science job openings pay $105,000 on average and promoted information designing occupations pay a dividend of $117,000.
• Annual requirements for the fast-developing new jobs of Data analysts, Data developers, and information specialists will rise to almost 700,000 openings by 2020.
• Around 80% of organizations are doubling down on Data analytics groups or experts inside their organizations to take apart and dissect the information successfully so that better techniques can be invented in the future.
• The demand is on the rise and the supply is not nearly sufficient for the organizations.
• Data analytics assist businesses and companies make better choices by evaluating the historical data and help them in reducing the expense. Companies can also make systems more favorable for advancement and growth in the future.

Which jobs can you get with a Data Science degree?

Here are some of the top data science careers you can enter into with a postgraduate degree -

Business Intelligence (BI) Developer

Average Salary: $89,333

Typical Job Requirements: BI developers design and create strategies to assist business clients in rapidly finding the data they have to utilize to reach better business decisions. Highly data-savvy, they use BI tools or create custom BI analytic applications to promote the end-clients' comprehension of their frameworks.

Data Architect

Average Salary: $137,630

Typical Job Requirements: Make sure data solutions are constructed keeping performance in mind and design analytics solutions for numerous platforms.

Applications Architect

Average Salary: $134,520

Typical Job Requirements: Track and monitor the behavior of applications running within a business and note how they interact and function with each other and with other users.

Infrastructure Architect

Average Salary: $126,353

Typical Job Requirements: To make sure that all business frameworks are functioning optimally and can bolster and assist in the improvement of new technologies and secure system prerequisites. A comparable job title is Cloud Infrastructure Architect, which directs an organization's distributed computing procedure and operations.

Enterprise Architect

Average Salary: $161,272

Typical Job Requirements: An enterprise architect has to operate closely with stakeholders, particularly management and subject matter experts (SME), to gain a wider perspective of an organization’s strategy, information, IT assets, and processes.

Data Scientist

Average Salary: $139,840

Typical Job Requirements: Locate, cleanup, and organize data for organizations. Data scientists should have the option to evaluate a lot of raw, complete, and processed data to unearth patterns that will benefit an organization and enable the managers to reach vital business decisions. Compared with data examiners, data researchers are much more specialized.

Data Analyst

Average Salary: $83,878

Typical Job Requirements: Manipulate and transform large data sets to match the desired analysis techniques for companies. For quite a few companies, this role can also include tracking and monitoring web analytics sometimes and analyzing A/B testing too.

Data Engineer

Average Salary: $151,307

Typical Job Requirements: Carry out batch processing or real-time processing of data that has been gathered, stored, and processed. Making data readable, analytics-friendly and accessible to data scientists.

Machine Learning Scientist

Average Salary: $139,840

Typical Job Requirements: Research new data approaches and algorithms.

Machine Learning Engineer

Average Salary: $114,826

Typical Job Requirements: Deliver software solutions and create data funnels.

Statistician

Average Salary: $93,589

Typical Job Requirements: Interpret, analyze, evaluate, and report statistical information, such as formulas for business purposes.

How do I start a career in data science without any experience?

If you have the energy for Data analysis, your presentation skills are serviceable, and you are keenly interested in this field it will be easy for you to give a kick start to your Data Science profession without much prior knowledge.

In addition, if you have completed a Bachelor’s qualification and your subjects included Mathematics, Statistics, and Economics it will be an added advantage for your understanding. There is still a very high demand for individuals possessing a degree in enterprises.

Things that are required for starting your career as a data scientist are -

Technical abilities -

• Specialized skills incorporating fundamental and core programming aptitudes and insights.
• Python and R are the most important programming languages you should focus on in the initial phase.
• Become acquainted with the fundamentals of statistics as they are employed to break down and discern the patterns in data.
• In the same way, as a data scientist, you should be aware of how to produce insights comprised of valuable knowledge from data.

Business skills -

• Learning and actionable skills are important. However, to be able to use them effectively one needs to practice with real-life and real-time problem statements.
• By starting to accumulate domain knowledge one can start gleaning insights about a specific industry.
• Connect with and accept recommendations from pioneers or the leaders of the area. You can participate in data science conferences or connect with them through professional websites.

Communication and Visualization -

• Build your network using social media or professional websites like Linkedin, Meet up, and so forth. Team up with the individuals or professional networks from whom you can derive knowledge and learning.
• Develop your storytelling abilities – The job of a data researcher doesn’t just entail creating bits of knowledge from data. It is just as important to understand how well you can convey it to impact the end-users.

What is the salary of a data scientist?

Today, we have entered a world where your fridge would text you saying, 'Hello! I am coming up short on Apple Juice. If you don't mind bring a bit while coming back home'. Just imagine what an easy life our not-so-distant future holds. Furthermore, it would all be made achievable through our own data and connected gadgets! Organizations have correctly comprehended the gigantic business potential which can be realized utilizing Data. Google, Amazon, Facebook, and Baidu are only a few of the organizations which have made big investments in information technology and data science. For example, self-driving vehicles, voice/picture recognition, wearable gadgets, and so forth.

A candidate proficient in the Python programming language procures Rs. 10.20L yearly.

Predictive modeling merged with Big Data brings about a considerable mixture of a range of abilities. A candidate with considerable Big Data and Data Science abilities earns Rs. 13.10L yearly when compared to an applicant with only big Data abilities who earn only Rs. 9.80 L yearly. An entry-level Data Scientist of IT with under 1-year experience can hope to acquire a normal all-out pay of ₹550,000. An early vocation Data Scientist of IT with 1-4 years of experience gains a normal all-out pay of ₹605,552. A mid-career Data Scientist of IT possessing 5-9 years of experience is given a normal complete remuneration of ₹993,293. An accomplished Data Scientist of IT possessing 10-19 years of experience acquires a normal absolute remuneration of ₹1,747,282.

How much time will it take to learn data science?

It is generally helpful to learn data science one requires at least 6 months of dedicated time with around 6 to 7 hours daily. Furthermore, it is also contingent on your grasping power and previous knowledge of programming/mathematical skills that can speed up or slow down your learning. Another thing to note is that one may become well acquainted with many aspects of Data Science after 6 months of learning but to really master it and obtain a dream job requires much more knowledge than that surface learning. Even though, you can start working on the fresher level and work in different projects scaled to your mastery as it grows with time and participation. Another way to learn Data Science is to opt for the many Data Science online certification courses available on Careerera such as Post Graduate Program in Data Science, Masters in Data Science Engineering, Data Science Analytics Professionals, Masters in Data Science and Analytics.

Below are a few of the most important things you must note for making a career change to data science:

1. Practice Hands-on: Complete a lot of hands-on projects in the form of assignments and start-to-finish projects individually, in teams, and in groups.
2. Work on Projects: Complete multiple guided and unguided projects end to end from start to finish; not just in piece meals or baby steps.
3. Work in Teams: The ability to work in a team is extremely important and you must scout other practitioners for solving interesting problems together.
4. Build a GitHub Portfolio: Over a period of 6 to 8 months construct a highly impressive Github portfolio.
5. Blog Actively: Blog about the various data science projects you worked on social channels such as LinkedIn, Facebook, etc.
6. Participate in Hackathons: Participate in start to finish mentor-led hackathons where students get the chance to work in teams to solve a problem statement in the allotted time constraint.
7. Network Systematically: It is important to engage in structured networking and build a strong and attractive LinkedIn profile.
8. Attend Meetups: Meetups are the places where you come across a lot of people who would be willing to offer you internships, jobs, training, course learning opportunities, etc.
9. Master a Domain: Machine Learning is vast. Delve deep into at least one topic and learn it comprehensively and properly.
10. Get an internship: Getting an internship is a best and smoothest way to transition from the learning stage to the experienced stage. It will provide you the best exposure to real industry problems and how they are tackled impromptu. And if you do well, you might bag a job too!
11. Be Patient: Allow yourself some time. You should realize that the transition cannot be rushed. There is a need to dedicate at least 6 to 8 months to make a significant leap in your career.
12. Practice Multiple Interviews: Before bagging your dream job, you should practice with a series of interviews.

Does Data Science have a future?

According to Tim Berners Lee, the inventor of the World Wide Web, Data is a Precious Thing and will Last Longer than the Systems themselves. Vinod Khosla, an American Billionaire Businessman and Co-founder of Sun Microsystems declared that in the next 10 years, Data Science and Software will benefit Medicines much more than all of the various Biological Sciences together. By the above two statements, it can be clearly concluded that data proliferation will never end and the use of data related technologies like Data Science certification and Big Data will go up day by day. Different sectors are making use of Data Science for expanding their growth and deriving multifarious benefits. All the above points are supportive of the statement that the future of Data Science is extremely bright. In India alone, there shall be a massive and imminent shortage of data science professionals from 2020. According to IBM, there will be a predicted increment in the data science job openings by 364,000 to 2,720,000.

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4. Can You Become a Data Scientist Without any Experience?
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