# Data Science is Difficult to Learn! A Myth or a Truth?

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**Want to learn Data Science?**

If yes, you might want to know the answer to the question – *is data science difficult to learn? So, read the complete blog and you will find the answer. Also, at the end of this blog, I am providing you the best guide to learn Data Science quickly. *

Data Science is a recent field. It still lacks a proper development base and is more of an umbrella form. There are various challenges that exist in data science. While there is a massive explosion in data, there is no availability of specialized data scientists who can handle data the right way.

This is because of the massive skill gap that is contributed by the major difficulties that plague the field of data science.

So, let’s discuss how data science is difficult and some of the problems that are faced by data scientists as well as data science aspirants alike.** **

## What makes Data Science Difficult?

Almost everyone wants to * become a Data Scientist* these days without knowing the difficulty that lies ahead in learning data science as well as implementing it. Some of the issues that make Data Science difficult are –

### 1. Hard Problems

Data Scientists need to tackle hard problems. These problems are focused on developing models that tackle some of the hardest business problems. This requires a keen sense of problem-solving and high sense of mathematical aptitude.

A Data Scientist is required to find patterns within the data and generate insights by taking conclusions from the data.

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A Data Scientist must be seasoned with solving problems of great complexity. It requires people who are inquisitive enough to persevere through the toughest of problems. Data Science is a complicated field, especially for those who have no prior experience in this field.

Since, data science is a recent field, finding experienced candidates is one of the toughest problems faced by several companies. Furthermore, the problems that exist in the massive ocean of data science have several variations.

This further makes data science a difficult challenge for many industries. Therefore, in order for the companies to develop data science solutions, they must thoroughly understand the problems and apply an analytical approach to solve them.

### 2. Large Scale Data

Data is the lifeline of a Data Scientist. However, there is a large amount of data that is present in the world today. This data is expanding at an exponential rate and often becomes a burden for the data scientist.

In order to derive meaningful information from the data, a data scientist is required to analyze the given big data and generate insights. However, managing such bulky data often becomes a challenge for many data science professionals.

Do you know – White House has already spent a huge bunch of almost $200 million in different data projects.

Furthermore, the data that is present is not always organized, that is, the data is not structured in the form of rows and columns. This appends an additional challenge to the data scientists.

In order to handle such a large volume of data, a data scientist is required to have knowledge of big data tools like Hadoop and Spark. This distributes the expertise of a data scientist whose primary job is to analyze data. Therefore, it becomes a challenge for the data scientist to be specialized in multiple roles.

### 3. Technical Expertise

Data Science roots from multiple disciplines. Fields like mathematics, statistics, programming are some of the key disciplines that make up data science. There are then several sub-constituents of these disciplines that a data scientist must master.

While it is relatively easier to have knowledge and expertise in individual fields, it often becomes difficult to master all the three disciplines. Furthermore, it takes years for an individual to become an expert in a single field.

For example, in order to become proficient in programming, a programmer spends years to master his domain. For becoming a proficient master in data science, he will have to spend almost an equal amount of effort in mastering statistics.

This is one of the main reasons as to why most proficient data science professionals hold a PhD in quantitative fields like finance, natural sciences, and statistics. In these days, programming has become an auxiliary skill that every professional is required to learn.

For example, a person pursuing a PhD in biostatistics is required to hold command over a programming language like R to implement statistical models for generating findings. Therefore, it is concluded that in order to master data science, you must first master its underlying disciplines.

### 4. Domain Knowledge

One cannot become a proficient data scientist only through solving projects, participating in boot camps and acquiring knowledge from various online resources. While these skills are necessary for building the fundamentals, it is the domain knowledge that brings data science into the picture.

The domain knowledge comes from experience. For an engineering and IT professional, transitioning into a data science role that deals with a forecast of customer sales might prove difficult.

This is because data science requires domain knowledge to identify useful variables, develop models in the context of business problems as well as fine-tune models to eliminate bias that can only be identified through an understanding of the domain knowledge.

Various * industries make use of data science*. Fields like health, finance, banking, pharmaceuticals, sales, manufacturing make the use of data science in their own way.

Furthermore, data scientists need data to make better products for their customers through careful analysis and assertion. These customers can be the end user for several business domains. Therefore, in-depth domain knowledge of the customer is required for a data scientist to gain better results.

### 5. Learning Everything at Once

For startups who are venturing into the field of data science, the presence of a sea of knowledge can often prove to be daunting.

Data Science is math heavy, and many people who are data science aspirants would want to have a grasp over the core mathematical concepts before venturing in the field of data science. However, this approach is not right.

Data Science is a practical field. It requires the practical implementation of various underlying topics. The concepts that are used in data science are also highly vaporable. This means that if you only grasp the theoretical knowledge and do not practice it, it will be easily forgotten.

Data Science, therefore, is practice-heavy and requires the right approach to solve its problems.

## Conclusion

In the end, we conclude that data science is a highly difficult field that has a steep learning curve. This is one of the main contributing factors behind the lack of professional data scientists.

There are many new university degrees and boot camps for data science that have started to address this problem through imparting structured knowledge to the students. Without any university degree, you can learn all the A-Z of data science through visiting Data Science DataFlair Tutorials Home.

There you will find** 370+ FREE Data Science tutorials** that can help you to become a master of it.

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