Data Science: The Hottest Field of 21st Century
In the past few years, the world of data has gathered steam. More so in the post-pandemic times. You might recall having read projections regarding the number of COVID-19 cases in a particular country over a period. And you might have wondered, how do these people predict a number that is mostly accurate? The answer to that question lies in today’s article.
(If you haven’t yet read our previous article Big Data: Powering the Age of Information, I highly recommend you do so before continuing with this one)
Data: The New Oil
It is often said that Data is the New Oil of 21st century. The premise behind this statement indicates that as the discovery of oil and its applications revolutionized the 20th century, so will the Data for the current one. There certainly is some truth to this assumption as mining of data and gathering useful insights from it has certainly caused a silent revolution in almost all the fields such as Healthcare, Entertainment, Marketing and many more.
In the previous article, we understood why Data is important. But all the Big Data gleaned from multiple sources is of little value by itself. This is where Data Scientists step in. They are responsible for extracting useful information from humongous data. This information is then used to make decisions regarding the business or a field. For example, for a company to decide whether to launch a product in a particular geography, Data Scientists make use of multiple factors such as – previous sales figures, competition, user preferences, etc. – to analyse and predict the sales of the product. This information shall give the company a better idea regarding outreach strategy, target consumer, packaging, discount to be offered, etc. Now imagine shooting the arrow in the dark without all this data to guide. That would be a less-than-efficient way of doing business, right?
Thus, to summarize, Data Science is used to draw conclusions from a large amount of data which would help the concerned make informed decisions.
Steps in Analysis of Data –
Though the job of a Data Scientist seems straightforward and simple, it is not so. Working with Big data is challenging due to multiple factors like huge volume, need for verification and constant influx of new data, just to name a few. Hence there are a few steps involved before the insights from data can be used –
1. Data Mining – This is the first step where data is gathered from relevant sources. For example, an advertisement agency shall collect data from internet and social media while a healthcare company shall focus on gaining patient records, medical history, etc.
2. Data Pre-processing – This step is necessary to convert the collected data into a form that can be analysed easily. Hence it involves removing inconsistencies and irrelevant bits, checking for veracity, etc.
3. Data Analysis – This is the most important step. Analysing the data essentially means separating the wheat from chaff. Hence it includes searching the data for information that could be useful and then drawing conclusions from it. Statistical methods and machine learning techniques are most commonly used in this step (read more about Machine Learning here).
4. Data Visualization – It is difficult to use mere data analysis to make decisions as the insights could be in a haphazard form. Hence, visualization of the analysis in the form of easy-to-understand graphs, charts and tables is important. This visual representation ensures that the mined data can now be used by the decision makers to reach a pertinent conclusion.
5. Decision Making – After we have all the necessary insights gleaned from data in the suitable form, it is time to make informed decisions that would prove beneficial for the corporation.
Importance of Data Science –
1. Informed Decision-Making –
As mentioned before, the more factually correct, relevant and up-to-date data we have, better are our decisions and resulting outcomes. Further, it contributes to resiliency of businesses through improved risk and crisis management.
2. Improve productivity and efficiency of corporations –
Through Data Science, it is possible to craft target strategies that would maximise output and drive higher impact. Consequently, it can also help in saving costs by driving efficiency and efficacy.
3.Building better products and developing effective services–
Consumer data is especially relevant in the FMCG (Fast Moving Consumer Goods) and retail sector. Through analysis of consumer reviews over shopping websites, manufacturers or service providers can get the much-needed feedback which shall help in meeting customers’ expectations. Data Science additionally contributes towards identifying market gaps and expediting product launch timelines.
Applications of Data Science –
The field of Data Science, though narrowly focussed on analysis of data, can be applied to a wide cohort of fields. Let’s look at some of the major ones –
1. Finance & Economics – Data Science is employed in finance for fraud detection, risk and portfolio management. It is also used for stock price predictions and macroeconomic analysis, in conjunction with machine learning models.
2. Entertainment – Content recommendation, analysis of audience preferences and content profiling are some of the areas that use Data Science within the entertainment industry. Additionally, content creation makes use of Data Science and the related analytics to spot upcoming trends.
3. Advertising – Advertisers utilize Data Science insights for targeted and/or segmented marketing, consumer behavior analyses and finally to determine the success or failure of a campaign.
Data Science and Privacy Concerns –
Big Data and its analysis form an integral part of the Data Science domain. But this data is sourced from various points, including social media, shopping and financial websites. Hence there arises a concern regarding data privacy and security.
Due to the large amounts of data that Multinational Corporations (MNCs) possess and store at their headquarters located in foreign countries, discussions regarding data safety have begun. Passing of legislations to safeguard citizens’ privacy and nation’s integrity is the route being taken by multiple governments. Passing of the General Data Protection Regulation (GDPR) by European Union is one such example.
Alongwith the skepticism regarding data collection and storage, there are concerns regarding data sharing as well as the ethical conundrum regarding mining personal data. In case of personal data of individuals being used for training artificially intelligent models or for the purpose of data analysis, anonymization of the mined data is of particular importance. Identifying elements such as name, gender, age, citizenship and others need to be nulled during the Data Pre-processing stage in order to protect the privacy and identity of individuals. This step is especially important when data is shared either publicly or to corporations that need it.
Further, as the fields of Data Science and Artificial Intelligence (AI) work closely, the ethical dilemmas that AI systems face can relate to Data Science too. Hence Data Scientists and Analysts need to be aware of the ethical implications of their work and strive to reduce the impact inherent data bias on their analyses. It is also significant to remember that systems do not reinforce existing biases. For example – in doing a Twitter sentiment analysis, it is imperative to identify and filter our xenophobic, racist and misogynistic content. Otherwise, we risk programming our machines with all that is ugly in the world, rather than enabling them to help us create a better global society.
The reality of the Sexiest Job –
Harvard Business Review has termed the job of Data Scientist as the century’s sexiest. From our detailed discussion, you might have understood why Data Science is being hailed as ‘the’ job right now. Being an interdisciplinary field that utilizes concepts from Statistics, Computer Science, Business and Management, Data Science is currently offering multiple job roles that promise an exceptional career trajectory. The market is overflowing with ads of classes that offer courses in Data Science and Data Analytics with a promise of eye-popping salaries. Though it is true that Data Scientists are paid handsomely, it is not as direct as these marketers make it sound. Being a Data Scientists is a responsible position that has high stakes as huge business decisions are made based on their input. Hence, it is important that Data Scientists have a good grasp over data fundamentals, programming as well as Business and domain knowledge. Such profound knowledge is necessary to succeed as a Data Scientist/Analyst/Engineer. Accompanying technical knowledge, a growth mindset and a readiness for lifelong learning shall help you become successful in the century’s most sought-after job.
Thus, though humans have been using data since the dawn of civilization, Data Science as a field emerged only in recent decades. Proliferation of internet and availability of sophisticated tools for Big Data analysis ushered in the Age of Information that we currently are living in. Hence, there is no doubt that the onward march of Data Science and Big Data Analytics shall continue for the coming decades and the job of Data Scientist shall keep getter hotter!
Vishvali Deo is an E&TC (Electronics and Telecommunication) Engineer by education and Software Engineer by Profession. She believes that 'Technology is a Great Democratising and Equalising Force' and hence is on a mission to make the general public understand seemingly complex technologies in a simple manner.
She is convinced that the root of today's world problems lie in the past, hence she has also pursued post-graduation in History. She has a keen interest and a good grip over Economics, Political Science and Environmental Engineering. She has a penchant for working with Women and spreading Digital Literacy amongst them, with the aim of their empowerment. She also strives to provide Free Quality Education to children and counsels young adults. Besides, she is also skilled at Public Speaking, having won many awards in Elocution & Debate Competitions and Technical Paper Presentations.