Is Data Science good or bad?

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.