Data Science – Terminology, Scope, Pros and Cons

Data Science

Find out what is Data Science and learn about the different terms associated with it. Explore the Pros, Cons and the scope of being a Data Scientist.

Data science has become a revolutionary technology in the 21st century, where everyone is talking about it. Harvard University has even declared it as the sexiest job of the 21st century. Many famous personalities, including the Turning Award Winner, Microsoft’s Jim Gray has claimed data science as the fourth paradigm of science. The job of a data scientist is one of the most sought after jobs in the world. There is abundance requirement of the role of a data scientist and people in this profession earn lucrative pay.

So the question arises, what is data science? And what is the role of a data scientist? What skills are required to become a data scientist? And is data science good feel for you or not? Let us find answers to all these questions in this article. Will start with discussing what data science is and why it is becoming so popular nowadays. So in this article, you will learn –

What is Data Science?

Wikipedia defines data science as “an interdisciplinary field which uses scientific methods and processes standard algorithms and systems to extract knowledge and insight from many structural and nonstructural data”. Data mining, deep learning, as well as big data, are all related to the field of data science. This particular field unites different areas like statistics, data analysis and machine learning as well as knowledge of a specific domain like healthcare care or business management.

What Is Data Science

The underlying meaning of data science is the study of data. The whole field is about extracting analysing, visualising, managing and storing data to create data-driven insights. These insights help the company to make data-driven decisions which can help the company to grow and earn more profits as well as the customers trust. It is worth noting that data science requires the use of both structured and unstructured data.

Pros and Cons of Data Science

Data science is a vast field which is gaining popularity is now a day with an increase in the demand for a data scientist. This field has many substantial advantages, but we cannot neglect the significant disadvantages. So before deciding to switch tracks and become a data scientist, it is essential for, or you do an in-depth analysis of all the advantages and disadvantages of being a data scientist.

Advantages of being a Data Scientist

  1. Data scientists are in demand– The job of a data scientist is the fastest-growing job according to LinkedIn. This job networking company states that by 2026, there will be around 11.5 million job vacancies exclusively for data scientists. This makes data science a highly employable job sector.
  2. An abundance of positions – Very few people in the Information Technology Field have the required skills set to become a good data scientist. This makes the field of data science much less saturated as compared to other areas in the Information Technology sectors. This field is vastly abundant. The demand for a data scientist in today’s world is very high but with a meagre supply of data scientist with required skills set.
  3. The job of a data scientist is highly paid – The pay scale of a data scientist is one of the highest in the world. An average data scientist, according to Glassdoor, earns around $116,110 per annum.
  4. Data science is a versatile field – With numerous application of data science, this field has managed to generate employment in various factors like health care, consultancy services, banking as well as e-commerce websites. These sectors widely use data science. So the data scientist is likely to get jobs in any of these sectors according to choice and vacancy.
  5. The job of a data scientist is highly prestigious– Now imagine yourself to be the owner of a particular company, and you rely entirely on your employed data scientist to deliver you exact data so that you can take decisions accordingly. These decisions will definitely influence your business as well as your customer ratings. So you will always want these particular employees to give their best. This will help you to improve the performance of your own company. And, this is one of the best things being a data scientist. Since the company makes decisions according to the data delivered by the data scientist and their expertise, the position of a data scientist is considered to be very important in a company.
  6. Data science can save lives – Well, you must be wondering how field related to the Information Technology sector has the ability to save lives. As we discussed earlier, we have seen that data science has full applications in areas like healthcare sector. And you will be surprised to know that the field of data science has improved health care sector significantly. Now you can detect many fatal diseases like tumour at an early stage, and you can try to save lives by treating it on time.
  7. Data science helps you to grow personally – Another peculiar advantage of this particular field is that it will enhance your problem-solving aptitude, which will help you in each and every phase of life. Apart from that, data science works as a bridge between the IT and the management fields, and data scientists can enjoy working on both areas.

Disadvantages of being a Data Scientist

  1. Data science is a blurry term– Data science has become such a buzzword that it is challenging to pin. A particular and exact definition of this term. Hence it arouses confusion between people of the IT field.
  2. Mastering data science is almost an impossible task– This is because data science is a mixture of many different fields like statistics, computer science mathematics etc. It is complicated to master each of these fields and have equal expertise in them. Although it is safe to say that there are many excellent online courses which help you to develop knowledge in these particular fields, still they are not enough.
  3. The requirement of domain knowledge– The most significant disadvantage of data science is that the field of data science is exclusively dependent on a particular domain on which it is working. The data scientist is also required to have a good knowledge of these domains or fields. Let us take an example, a person with a background of statistics and computer science cannot do very well in some other area like the healthcare sector. The industry of healthcare requires the data scientist to have an excellent knowledge of biology, more specifically on human anatomy, which is quite tricky for a guy with a computer science background.
  4. Issues related to data privacy – Nowadays, a privacy breach is one of the biggest problems in the digital world. And data science has a huge role to play in it. For many industries, data works as their fuel. Data science helps the companies to take decisions given by its data. The data used in decision making and data science is generally accepted by breaching customer privacy, like taking information about their behaviour as well as their contact. These data are used by the parent companies and their data scientist to make decisions accordingly. And using these data can even lead to leaks due to security failure, which is a massive attack on the privacy of the clients. This also raises many ethical questions, and it is a significant concern for the field of data science.

Different components in data science

Data science acts as an umbrella to many various fields. Some of these components of data science are –

  1. Big data
  2. Data mining
  3. Data analytics
  4. Data analysis
  5. Data science and
  6. Machine learning

So these terminologies come under data science. Individually, data science is a subject with different stages within itself. Let us take an example of a big retailer who wants to use data to improve his business. He does the following things to do this, or makes the data scientist do for him –

  1. Collecting data
  2. Preprocessing data
  3. Analysing data
  4. Driving insights and generating BI report
  5. Taking decisions based on insights

Generally, all the decisions based on data are taken following some steps in order. Let just have a look at these stages-

Collecting data

To solve any problem or take any decision regarding your business, the first thing you will need is to have data. Having data is the essential thing required to analyse anything. There is rarest of rare chances of you getting ready to consume data, and we should not always be dependent on this particular thing.

Collecting Data

Generally, the data we receive is in the form of big data. Big data is nothing but data which is very big or complicated and is difficult to handle by the data scientist. And it needs proper processing. Let us take an example of a retail business where a lot of transactions happen in a day. So maintaining data of every customer, bank details come employee staff and others are very difficult, and here we need some big data technologies like Hadoop and Kafka to simplify our work.

Cleaning data

Cleaning the data involves removing unnecessary information from the big data like some missing fields, improper values, or even setting the right format of the data. It also includes works like structuring the data from raw files to the required format and making corrections if any.

Cleaning Data

All the processes at this stage until generating insights for the business fall under the category of data analysis. Here, data is extracted, cleansed, transformed, modelled and visualised with an intention to uncover all the required meaningful as well as useful information which can help improve decision making. The data used in such cases can be structured as well as unstructured.

Analysing data

Here, the category of data analytics comes handy. Different kind of analytics like predictive analytics, descriptive analytics and perspective analytics, are used in the process. All is started by determining which type of analytics is needed to perform. After all this, we present a data mining operation which is to identify hidden patterns and information in the broad set of data.

Analysing Data

Driving insights and BI report

After the analysis of data, the data scientists gather insight from data which enables them to take decisions accordingly. These insights can also come through the data mining process or through some predictions made by the data scientist. The predictions made by the data scientist are included in a mathematical model, and here machine learning is applied mostly. The technique of machine learning is used to obtain a mathematical model by learning from the patterns present in the data while doing data mining.

Taking actions

Based on all the insights and information gathered through the analysis of data and data mining, measures are made by the company find the main objective to take these actions is to improve the future sales and profits in the business.

Different roles in the data science industry

Different Roles In The Data Science Industry

  1. The data scientist – This job requires the person to have all the skills sets and talents, from being able to handle the raw data to analyse the data and take the proper decision. This job role has a massive demand for big companies like Google and Microsoft.
  2. Data analyst – This person has mastered the languages of C, C++, R, Python, SQL, etc. The main work of this person is to collect and process and perform statistical data analysis on the given data.
  3. Data engineer – generally from the background of a software engineer, the data engineer is more concerned about large-scale processing systems and important or large databases.
  4. Machine-learning engineer – There are more involved in Artificial Intelligence which is becoming a global trend nowadays. The goal of a machine learning engineer is to create software and program it in such a way in which the machine/program is not required to be directed to perform a specific task and that the applications can do it automatically. They help provide the intelligence to the work done by data analyst, for example, predicting sales of product and segregating different types of customers based on their habits, trees and preference.
  5. Business analyst – This field is less technically oriented, but it is imperative to the field of data science. The role of a business analyst is to use in-depth knowledge about different business processes and the data insights to take proper decisions to help the company grow and gain more profit.

Pillars of data science

To be successful as a data scientist or to be successful in the field of data, science requires the individual to have a particular skill set. These four fundamental skills are –

  1. Written and verbal communication – The job roles like data scientist or data engineer or data analyst involve the individual to deal with clients. You should be able to tell stories about their data to their clients and convince them about their work. Here, written and verbal communication is crucial since you need to communicate with the clients as well as the people who are working with you to analyse your data. You should also be able to present the data in an attractive way.
  2. Statistics and probability – Statistics has played a significant role in analysing the data and making the data science field better. Generally, data scientist are exposed to data which have numbers and words, and sometimes different signs which need to be organised by the data scientist in a readable manner. The data scientist should be able to present data in a way, which can be visualised by their clients as well as other team members. At the same time come, a probability is used to predict the uncertainty regarding data.
  3. Business domain – A data scientist must have the domain knowledge of the business in which he is working. This holds true for the data analyst or the financial analyst as well as a data engineer and other job roles in the field of data science. This knowledge will help the analyst to analyse different possible outcomes of a particular decision. It enables the data scientist to take this is accordingly.
  4. Computer science and software programming – It is one of the basic requirements of being a data scientist. The data scientist needs a solid background in computer science commerce statistics as well as in programming. Having expertise in these three departments help you to become a better data scientist as well as do better in any field related to information technology. Additionally, the individual should also be able to think test and analyse quickly and make reliable decisions.

I hope you found this guide useful. If so, do share it with others who are willing to learn about the different topics that we publish here on our blog. If you have any questions related to this article, feel free to ask us in the comments section.

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