8 Nov 2019
Data science has recently risen as the most ponderable and discussed subject due to its growing application across industries and massive importance in one's career. More of an umbrella, Data Science domain contains within many more subcategories of disciplines, making learning a little bit of a challenge especially for novices.
While the domain still lacks the required number of skilled data scientist professionals despite surprisingly huge demand internationally, the other parallel question continues to perturb many: Is data science relatively difficult to learn?
It is true that the data analysis market hasn't met its demand of specialized data scientists, leaving the sea of industry data unprocessed and unhandled. Even though the field of data science poses several challenges, there is also a welcoming change of career for data scientist aspirants. So what all difficulties contribute to creating data science skills gap across the globe?
This blog stands for the answer to how difficult it is for data science aspirants to gain data science expertise.
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What challenges make data science difficult to learn?
Fancying the idea of learning data science sounds easier than actually done, as many young millennials are interested in being a Data scientist. However, in reality, the data science domain poses a good amount of challenges especially if it is the first time you hear about it or you have no background close to the field. The potential challenges that may make learning data science difficult for you are:
Dive into Multiple disciplines
Data science fundamentally emerges as a multidiscipline umbrella form of profession involving combined aspects of statistics, analytics, mathematics and programming (like Python and SQL) and soft skills like business communication. To be a data scientist, you must master all of it -- which is not easy at all.
You may find it easy to target and master each discipline individually, but being an expert in all disciplines will leave you facing great challenges. Even learning about each discipline thoroughly requires a lot of focus and dedication. For once programming itself is a vast field that takes years for a candidate to master.
Proficiency in data science and statistics thus can be achieved by putting in a lot of effort and hard work. Data science professionals often have past history of exposure in analytics, mathematics or finance. Even programming skills are a common attribute of data science professionals. Hence, one challenge involved in learning data science is to first be aware of related disciplines.
Problem solving aptitude
Data science focuses on tackling ongoing business-related challenges such as business analysis, increasing ROI and marketing goals. Data scientists will attempt to develop effective models to address the toughest business problems. A good data master will be a great problem solver with an aptitude for mathematics, who will read and discover unique data patterns important to deliver insights. Dealing with data also means handling various complexities of business problems which often demands previous experience in a similar industry. Conclusively, a solution-driven analytical approach is what makes data scientist able to understand business problems and find robust solutions.
Ability to tackle Ocean of Data
Being a data scientist, you will also be presented with a huge flood of data that needs to be arranged and analyzed properly for its special business-oriented pattern. The data is unlimited and keeps on expanding which is not an ordinary job for a data scientist. To read the bulk of data for a meaningful insight and business information, a proficient data scientist needs a lot of focus, skills and aptitude.
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Tackling an ocean of data essentially means rummaging through loads of data that is not always organized or structured in desired forms. For an individual to size up large volume of information in its most unkempt format, it takes organized skills of using data management tools. In the end, it comes down to specializing in more than one roles for a true data scientist.
Data processing Knowledge
The profession of data science isn't simply limited to working on projects or gathering knowledge from open online resources. Although research skills are a foremost quality of a data scientist, they need to go beyond the fundamentals to acquire domain knowledge. The job has a lot to do with the amount of experience and qualifications. For instance, a typical engineer would find it arduous to dive into data science role that requires tracking and predicting customer sales.
This is mainly because the profession focuses on both analysis and technical knack along with the challenges of identifying relevant variables in customer behavior and designing models pertaining to business dynamics. One needs to make sure the data science model syncs with business objective depending on the industry type such as banking, finance, health, retail, pharmaceuticals, manufacturing, etc. Therefore, careful analysis and reading of given data in a given industry business is what a data scientist professional needs to develop viable customer-centric products for their customers.
Constant learning and practical implementation
While data science presents bounty of opportunities for learners, startups excited to explore the niche may feel overwhelmed by the amount of knowledge it demands. Data Science deals with numbers at stretch and heavy scrutiny around business activities. People who wish to master the field would feel like developing thorough practical skills around core mathematical concepts before ever jumping into the sea of data.
This implies the implementation of what one has learned in mathematical statistics. Data science is all about practical tactics since the concepts are dynamic and continue to evolve as time elapses. It is therefore important to be ready and interested in constant learning of new methods for practicing theoretical knowledge. Data Scientist professional therefore should believe in practices, right approach, continuous learning and implementation.
Learning Data Science is a promising investment
From a career perspective, it is undeniable that data science industry is worth attention. According to LinkedIn Workforce Report for the US (August 2018) there is a rise in the demand for data scientists but acute shortages of data science skills in almost every US city. The shortage is measured in thousands, of course.
On the other hand, data science nonetheless is regarded as the best promising jobs with high returns and long-term investment prospects. Due to its high demand and scarcity of skills, data scientist aspirants have a lot to gain such as:
Data science offers you a rich profile at an organization where you will be an important, well-respected professional whose ideas, insight and opinions will matter to the business and be heard by project managers and peers.
For those who find dealing with business statistics, coding and numbers fascinating can enter the handsomely paying, young domain of data science. The data processing niche is for you and will bring the best perks and future in competitive job market if you are willing to put in what it takes to be a data scientist professional.
Read Also: Why Should You Learn Python For Data Science
Conclusion: Should you go for it?
In conclusion, it is fair to say that learning data science is not absolutely easy and requires you to dedicate a lot of effort to master, however it is a very promising investment. The field is also marked with ongoing innovations as technology evolves.
However, due to high demand and skills shortage in data science in many industries, there is a great opportunity for you to be a successful professional data scientist. You may go ahead and participate in specialized bootcamps and the domain-specific university courses or online tutorials on the subject that help you consume well-structured knowledge.
8 Nov 2019
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