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Is Data Science good or bad?

  • July 07, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Because everything currently requires data and because Data Science is assisting a significant number of firms in predicting their future commercial possibilities, it is in high demand. Give us a detailed explanation of what data science is.

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Data Science

Data Science is a method of application of scientific methods to retrieve important information required for companies and industries. What all Data Science requires is just your data to create wonders that aren't possible by human intelligence.

Data science has a wide range of uses across many industries, although it is most frequently utilised in the domains of artificial intelligence, machine learning, deep learning, artificial neural networks, etc. Before discussing the benefits and drawbacks of data science, it is important to quickly review the many domains of artificial intelligence.

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Data Science

Different fields of Artificial Intelligence

  • Artificial Intelligence is a branch of Computer science that uses data to process and emulate human thinking mechanism. Machine Learning, Deep Learning, and Artificial Neural Networks are the subfields of Artificial Intelligence which are interconnected with each other. All of them aid Artificial Intelligence to achieve their goal.
  • As was already noted, machine learning is an application of artificial intelligence that involves teaching a computer with inputs and outputs such that, when given new inputs, the machine can anticipate the result on its own. Large volumes of structured data are used in machine learning to teach a machine to anticipate the desired result. To train a machine, we employ a variety of machine learning techniques. Depending on how they learn, these algorithms may be divided into four groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. To teach a machine to provide the desired results, any data scientist must select the ideal algorithm.
  • An Artificial Neural network is a subfield of Machine Learning where it emulates the mechanism of a neuron of the human brain. It consists of layers and neurons to process and train the data.
  • Deep Learning can be termed as a subfield of machine learning where it uses the technique of neural networks to process and predict large amounts of unstructured data. The only difference between Deep Learning and Artificial Neural Networks is the number of layers and neurons used in them. In Deep Learning, more number of layers are present when compared to Neural networks

Now that we have briefly explained how artificial intelligence operates. Now, let's examine its benefits and drawbacks.

Data is used in each of these fields to programme a machine to carry out the necessary activity. Thus, data is crucial to today's voice assistants like Google Assistant, Alexa from Amazon, Cortana from Windows, Google Self-Driving Cars, etc. We are seeing the wonders of data and its ability to transform our routine lives.

One can ask whether entering the area of data science is good or harmful because there are so many individuals who want to be data scientists. One may also ponder the advantages and disadvantages of data science. Let's talk about those elements in the following quick conversation.

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Pros of Data Science:

  • As mentioned earlier Data Science is reforming our mundane lives to smart lives. This is evident from the advent of voice recognition technologies and voice assistants which we depend on daily.
  • Well-known e-commerce enterprises like Flipkart, Amazon, etc. are benefited by data science. These eCommerce giants harvest data using machine learning techniques, and they use this data to influence online shoppers' thoughts and purchasing behaviours. As they base their products and services on consumer reviews and star ratings, this greatly bolsters their financial situation.
  • Also, we can use Data Science in the field of Astrophysics, which helps scientists to dig the details of a black hole and also, recently we had a breakthrough in that where our scientists could get the first image of a black hole. Data Science is widely used in the field of Medicine where doctors use Data Science to predict and identify the mutations which determine tumor cells. By this, doctors are able to stop it from spreading to other healthy parts of the body. In this way, Data Science is also proving its mettle by helping human beings. Who doesn’t praise this aspect of Data Science?
  • Sportspeople can benefit from data science by maintaining their energy and fitness levels. Players can avoid injuries this way and can instead concentrate on their game without worrying about them. Additionally, Data Science and Data Analytics assist many gamers in deciphering their opponents' playing strategies. They are better able to determine the ideal ways to defeat their rivals because they have a better understanding of the strategies and techniques used by their rivals.

We have known the Data Science pros. Now let us look at the other side of the coin-its cons.

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Cons of Data Science:

  • The important aspect of Data Science is Data Mining. We all know that Google uses the data to provide us search results. But it has come to know that there is a lot of gender bias involved in the classification of certain words. For instance, Google related men to most of the stereotypical male professions while many of the home-related words to women. This is something which we don’t want Data Science to do because this kind of bias might impact the mindset of future generations which can aggravate the already entrenched stereotypes in our society. We don’t want a machine to differentiate men and women like human beings. To avoid this, it is necessary to verify the credibility of the data before opting to train the machine.
  • The inability of data science to prevent false news is another significant adverse consequence. We are all aware of the harm fake news is doing to our tranquil lives. In fact, we turn to Google daily for answers to our questions about practically every subject. Whether it's our preferred dining establishment, fave sports team, top hospital, top mall, etc. This demonstrates how data affects our daily lives. Keeping this in mind, certain evil forces may use data as a weapon to spread rumours and false information in an effort to disrupt people's daily lives. If we don't use our judgement to determine the reliability of the source, this might be harmful. Facebook, Whatsapp, Twitter, Instagram, and other social media sites are the primary providers of fake news.
  • Handling large amounts of data is not a simple task. Data cleaning is a major task of a Data Scientist who has to segregate the required data from useless data. It is a huge and tedious task and it requires a lot of patience and perseverance to collect the required data. Any negligence in Data cleaning might affect the output.
  • Because the majority of the supplied data is arbitrary, it occasionally may provide surprising results. The poor handling of the accessible data may be to blame for this.
  • As data is the driving force for many companies and industries, it is used by them without proper data security features. It is possible that our personal data is being used by them and that our details are known to the unknown persons who aren’t concerned about our data privacy. This exposes the unethical use of our data which is at risk of being misused.

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