Data reigns supreme in this era of the digital economy. As such, mining and interpreting data with cutting-edge tools and frameworks has been accorded top priority by leading industries today. In the 21st century, Data Scientist’s position is considered as one of the most rewarding ones as lucrative career prospects line up to benefit those who have expertise in the data science domain.
Python is at the base of a successful career in Data Science and Machine Learning. It facilitates analysis and visualization of data from all possible perspectives and also allows the building of intelligent machine learning models to optimize on insights drawn. Data Science with Python Foundation training would build your fundamental awareness in a strong manner so that you can demonstrate your understanding of Python in solving real-life business challenges.
Why Python is Worth Learning from Data Science Perspective?
Facts readily available from numerous researches and market surveys commissioned by brands like IBM have shed interesting light on the potential that Data Science commands in a competitive employment landscape. As Python is the most popular language for Data Analytics, it definitely stands to benefit aspirants the most.
A number of compelling reasons can serve as motivators for learning Python to make a position for oneself in the booming Data Science (DS) domain.
1. Huge Demand for Data Science Professionals
Way back in 2015, almost 2.35 million job listings were present collectively in all DS categories. Every year, the demand for trained DS professionals is surging up by 15% and the pace would continue to sustain itself. By 2020, it is expected that 0.36 million new vacancies would be available across the USA only. (Source)
2. Fastest Growing Roles
In the DS employment market, the roles of Data Scientists and Advanced Analysts are experiencing the fastest growth rate. The demand would soar up to 28% by 2020. (Source). Also, the maximum number of openings in the DS market is related to the position of Analytics Manager.
3. Longer Vacancy Opening Period
When jobs are posted in the DS segment, the vacancies remain open for about forty-five days on an average. This is 5 days more than the average for all types of job listings. (Source). It also highlights the shortage of qualified professionals in the market and the urgency companies show to hire data scientists.
4. Salaries Are Consistently Increasing
Shortage of well-trained and discerning DS professionals is inspiring employers to enhance the salaries even at entry positions. Organizations are doing this to subliminally motivate aspirants to go for DS training and bridge the demand to supply gap.
The annual salary being offered on an average to DS professionals is $80,265, which is higher by $8,736 compared to graduate level employment opportunities available. (Source). The salaries exponentially go up with experience and increasing complexity levels of analytics’ related responsibilities.
Top Analytics/Data Science/ML Software in 2018 KDnuggets Poll
2018 % share
% change 2018 vs 2017
5. Hybrid Nature of Job
The skill scarcity in the DS domain would continue for some time because most DS jobs are hybrid in nature. Analytical skills have to be supplemented with domain-specific market expertise for leveraging the full potential of DS tools and underlying Python framework. To prepare yourself for the challenges, you have to undertake focused and comprehensive Data Science With Python Foundation course wherein you can develop necessary skill-sets under the mentorship of industry professionals.
6. More Strategic Investment by Companies
For mitigating the talent scarcity in the DS domain, organizations are strategically investing in the development of talent pipeline and bench strength across DS ecosystem. Talent requirements are being analyzed in depth and based on the outcomes, fleets of trained DS professionals are being maintained by companies. These professionals can be delegated to various analytics jobs and they are allowed to observe their seniors at work for gaining valuable insights. After a foundation training, you can easily be absorbed in the rolls of any renowned company which would then further invest in nurturing of your advanced skills.
Enroll Now for a Python Foundation Course in Data Science
Enrolling in a stellar course which is steered by experts would help you build your practical skills in Python through hands-on learning with live projects. Working with real datasets and accessing advanced analytics tools would make you conversant with all popular Python toolkits and libraries like pandas, numpy, matplotlib, seaborn, etc.
If you are already in the IT domain, it is time to upskill yourself and build on your analytical skills with focused training modules.