Category: 3. Why Learn Data Science?

Data Science is the career of the future. Industries are becoming data-driven and new innovations are being made every day. The field of technology has become dynamic and with more and more people interacting with the internet, more data is being generated.

Industries require data-scientists to assist them in making smarter decisions and creating better products. Data perceives as the electricity of modern gadgets and applications. It makes products smart and empowers them with autonomy.

In today’s world, it has become a necessity to possess data-literacy. We must learn how crude data can transform into meaningful products. We must learn the techniques and understand the requirements to analyze and draw insights from the data.

Data holds an untapped potential that must be realized in order to develop useful products. With the advent of machine learning technologies, it is now possible to predict and intelligently classify information. Big Data and Data Science hold the key to the future.

Together, they form the bigger picture of Artificial Intelligence that is giving us products of the future like Self Driving Cars, Autonomous Robots, etc. In the classic Sci-Fi film 2001: A Space Odyssey, HAL is an intelligent conversational platform that can take decisions without human interference.

These things are no longer works of fiction anymore. With the emergence of Natural Language Processing and Reinforcement Learning, it is now possible to build such platforms in the contemporary world.

While it is true that the field of Data Science is immense, its rewards, however, are even greater. As technologies are rapidly evolving and changing, new technologies are replacing the older ones.

As a result, we need to be dynamic, keep up with technology and keep moving forward. Therefore, it is the need of the hour to learn Data Science and build a successful career in the future.

Big Data & Data Science is beyond being a tool of Business Intelligence. Various philanthropic and social organizations are using data to create products for social good. Also, various health-care organizations are using data for helping doctors to have better insights about their patient’s health.

In this section, we will go through various examples where companies are using data for social good. This will help you to develop inspiration to learn Data Science as a tool for enriching the lives of people.

4.1 Data Science for Analyzing Refugee Crisis

The global refugee crisis has become a problem that has resulted in deaths and the displacement of many people. In order to manage and regulate the information of refugees, the United Nations and World Bank have established a center for this purpose.

Using data based on gender, age, income, skills and health, it will analyze and take decisions to help and improve the lives of displaced people. It will use data to help displaced refugees and asylum seekers through real-time access on the refugees.

Therefore, Data Science is playing an important role in assisting governments and policymakers to make better decisions.

4.2 Data Science in Healthcare

Another major usage of data is in the field of medicine and health-care. Various health-care industries use historical records of data to predict diseases and help in early diagnosis. With the advent of deep learning algorithms in data science, it is possible to detect tumors and other defects at an early stage of diagnosis.

Data Science is also helping genomic industries to analyze the effect of drugs on genetic issues, analyzing genetic sequences and developing new drugs to combat diseases. In these ways, Data Science is helping people in various socioeconomic and health sectors.

Therefore, we realize the need for data and data scientists to help the world become a better place. We need to learn Data Science in order to create better solutions for real-world problems that people face today. There are problems all around you.

You need to recognize problems and develop solutions using the existing data. This will inspire you to learn data science as you will have a goal towards solving the problem.

According to Glassdoor, the average salary for a Data Scientist is $117,345/yr. This is above the national average of $44,564. Therefore, a Data Scientist makes 163% more than the national average salary.

This makes Data Science a highly lucrative career choice. It is mainly due to the dearth in Data Scientists resulting in a huge income bubble.

Since Data Science requires a person to be proficient and knowledgeable in several fields like Statistics, Mathematics and Computer Science, the learning curve is quite steep. Therefore, the value of a Data Scientist is very high in the market.

A Data Scientist enjoys the position of prestige in the company. The company relies on his expertise to make data-driven decisions and enable them to navigate in the right direction.

Furthermore, the role of a Data Scientist depends on the specialization of his employer company. For example – A commercial industry will require a data scientist to analyze their sales.

A health-care company will require data scientists to help them analyze genomic sequences. The salary of a Data Scientist depends on his role and type of work he has to perform. It also depends on the size of the company which is based on the amount of data they utilize.

Still, the pay scale of Data Scientist is way above other IT and management sectors. However, the salary observed by Data Scientists is proportional to the amount of work that they must put in. Data Science needs hard work and requires a person to be thorough with his/her skills.

Due to several lucrative perks, Data Science is an attractive field. This, combined with the number of vacancies in Data Science makes it an untouched gold mine. Therefore, you should learn Data Science in order to enjoy a fruitful career.

As discussed above, there is a huge abundance of data. However, there aren’t enough resources to convert this data into useful products.

That is, there aren’t enough people who possess the required skills to help companies utilize the potential that data holds. Due to this reason, there is a dearth in the supply of Data Scientists.

Much of this is contributed by the infancy of Data Science as a field. There is a lack of ‘data-literacy’ in the market. In order to fill this vacuum in supply, you need to learn Data Science and its underlying fields.

Data Science is not a standalone field. It is comprised of several sub-fields. These subfields are Statistics, Mathematics, Computer Science and Core Knowledge. Data Science offers a steep learning curve and is difficult to master.

However, with the right resources and direction, one can undertake the journey of mastering Data Science.

A great data science product is like a meal composed of data as its raw ingredient, tools like programming languages used to cook the meal and the foundational knowledge of statistics & math as its recipe

2.1 How to Cover the Skill-Gap?

To assuage the high demand, people should direct their attention towards learning the necessary skills that will help them to take up Data Science as a prospective career. While there are many resources and books on the internet, it is impossible to digest everything all together.

Therefore, people must curate a path and do away with all the necessary clutter to have practical insights about Data Science.

To create a refined data product, we need refined and polished skills. This comes with a combination of knowledge and experience. Since Data Science is a recent field and therefore experience can take a back seat. However, it is built upon existing knowledge of math and statistics that one must know about.

Data Science is about the implementation of this knowledge through several tools and programming languages. Therefore, one must also possess the skills of a computer scientist. Data Science in simple words can be termed as applied statistics without computer science.

Proficiency of these tools is a must since you need to express your knowledge in the right manner. It is, therefore, an utmost necessity to learn all the subfields of Data Science in order to grasp the trends hiding in the data.

There is a pressing need to fill the skill gap in order to churn out Data Scientists required by industries for their versatile applications. Therefore, we can best conclude that learning Data Science is not just about one topic but a collection of various topics ranging from Statistics to Computer Science.

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In the last century, oil was considered as the ‘black gold’. But, with the industrial revolution and the emergence of the automotive industry, oil became the main driving source of human civilization.

However, with time, its value dwindled due to the gradual exhaustion and resorting to alternative renewable sources of energy.

In the 21st century, the new driving force behind industries is Data. As a matter of fact, even automobile industries are using data to impart autonomy and improve the safety of their vehicles. The idea is to create powerful machines that think in the form of data.

Data Science is also the electricity that powers the industries of today. Industries need data to improve their performance, make their business grow and provide better products to their customers.

In the scenario of data science section, we took an example of a commercial industry that wants to maximize its sales.

In order to do so, it requires a thorough analysis of data behind sales, understanding of the purchasing patterns of the clients and using their suggestions to improve the product. To perform all these tasks, a Data Scientist is required.

Similarly, take an example of a Business Intelligence company is required to analyze its potential customers base. It requires a Data Scientist to utilize the data they breathe on the internet to track their daily trends and analyze their behavioral patterns.

1.1 How Does a Data Scientist Make Sense of Data?

A Data Scientist will use his tools to sculpt through all this data and chisel out meaningful observations that will help companies to make profound decisions.

Similarly, a health-care company specializing in building conversational platforms for patients of mental health will need data to analyze the trends and patterns. Automobile industries need data to develop self-driving cars.

Data is being generated since the dawn of human civilization. However, only recently we have been able to tap its true potential and draw insights from it. Only in the past decade, we have started to depict data as a fuel for industries. The main contributor to this latest revolution is the rise in computational power.

1.2 High-Performance Computing – An Answer to Complex Data

With the advent of high-performance computing platforms like

• GPUs
• FPGAs
• TPAs

We have been able to process such a voluminous amount of data. We are able to analyze and draw insights from this data owing to these advanced computational systems.

However, despite all these advancements, data remains a vast ocean that is growing every second. While the huge abundance of data can prove useful for the industries, the problem lies in the ability to use this data.

As mentioned above, data is fuel but it is a raw fuel that needs to be converted into useful fuel for the industries. In order to make this raw fuel useful, industries require Data Scientists. Therefore, knowledge of data science is a must if you wish to use this data to help companies make powerful decisions.