Is Data Science Hard to Learn?

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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 the 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 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 hard 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:

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Dive into Multiple disciplines

Data science fundamentally emerges as a multidiscipline umbrella form of profession involving combined aspects of statistics, analytics, mathematics and programming languages(i.e. 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. However, it would be great to have a clear understanding of what exactly data scientists do so you can learn effectively.

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. However, there are also people who switch careers to data science field without any relevant experience.

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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 a data scientist able to understand business problems and find robust solutions.

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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 bulk of data for 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 a large volume of information in its most unkempt format, it takes organized skills of using data management tools and programming language Python. In the end, it comes down to specializing in more than one role for a true data scientist.

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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 a 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.

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Constant learning and practical implementation

While data science presents a 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 a 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 professionals therefore should believe in practices, right approach, continuous learning and implementation. If you are new and looking for opportunities, you can begin by working as data scientist in startups as there will be lot of more liberation in terms of how the company can leverage the data for growth.

Learning Data Science is a promising investment

From a career perspective, it is undeniable that the 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 one 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:

• High salary with great perks
• Increased job security
• Better Career growth prospects
• Respectable industry
• Flexible work conditions

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.

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.

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Conclusion

In conclusion, to answer the question ”Is data science hard to learn” depends upon how and where you learn. 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.

Related Data Science & Machine Learning Courses in Hong Kong

• Data Science & Machine Learning Immersive

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