All industries in the world (without exception) today are dominated by data science. Data Science has become the fuel for industries to track various aspects of their business. From getting to know their target audience to predict sales figures, enterprises are enhancing all spheres of their marketing, innovation, and growth using data science. With machine learning and big data, data science has revolutionized the business world by interpreting data and helping make accurate decisions.
The Power of Data And Data Science
Can you believe that around 1.7 megabytes of data are constantly generated every second for every person in this world? This myriad of data needs to be waded through, organized, and interpreted so that one can derive meaning from the unstructured and raw bits of data. This amount makes it inevitable for businesses to hire data science experts to handle the enormous amount of data. By applying data science and big data to everyday activities, companies can bring immense value to consumers. For example, banking institutions can enhance their fraud detection mechanisms, and asset management firms are applying data science to predict whether a security’s price will increase or decrease in a given time frame.
Data Science History
The term “data science” has been circulating in the industry since as early as the 1960s. Earlier, data science was used as a synonym for “Computer Science.” In 2001, William S. Cleveland (computer scientist from the United States who taught Statistics and Computer Science at Purdue University) first used “data science” to depict an independent discipline. Gradually, they refined the term to denote data processing using analytics for a range of applications.
What is Data Science and what do Data Scientists do?
Data science is an amalgamation of many disciplines like data engineering, data mining, data preparation, predictive analytics, data visualization, and machine learning. Data scientists are also adept in mathematics, statistics, and software programming.
- Advancements in data collection techniques and technology have made it possible to have improved access to data.
- Data is collected from many different channels, sources, and platforms. For instance, social media, mobile phones, internet searches, and healthcare surveys.
- Big Data refers to the vast data sets created from collated data. Big data contributes to creating enhanced operational tools in all sectors.
- This ever-increasing unstructured data needs techniques for parsing to make sense of it. This challenge is what led to the emergence of data science and the growing need for data scientists.
- Data scientists utilize tools from various disciplines to gather, process, and gain insights from data sets. They use data mining and apply algorithms to complex data sets to reveal patterns. With these algorithms, data scientists can learn about an individual’s buying patterns and behavior to monitor for making decisions.
- Predictive analytics (statistical measures) is used to gauge plausible events in the future from the extracted data. The data scientists draw a parallel from what happened in the past to predict the likelihood of a specific event in the future.
- One can think of data scientists as storytellers who present data insights to decision-makers in a simplistic manner that they can understand and then apply to solve their problems.
Application of Data Science in the Real World
According to a survey by Fortune, AI professional hiring and jobs have grown by 74% in the last four years. According to the US Bureau of Labor Statistics, the employment and need for data scientists is expected to grow by 15% by 2029, much larger than the average predicted growth of other occupations (which is four percent).
This growth pretty much entices any job-seeking individual to avail themselves of the data science certificate online for a guaranteed job that would pay well. To understand how you can apply data science to any sphere of business, we will look at some of its applications in real life.
- Banking – The most crucial sector of data science application is the banking sector. With the use of big data and data science, banks can keep up with the competition around them. Data science helps banks manage their resources more efficiently and make more intelligent decisions by detecting fraud, improving customer data management, customer segmentation, etc. Banks can also assess their overall performance by risk modeling through data science. Banks use real-time analytics to dig into underlying problems that affect their performance.
- Transport – The transportation industry is another important application of data science. In the transport division, data science makes its mark by creating safer driving environments for drivers and optimizing the performance of vehicles. This approach, in turn, gives greater autonomy to the drivers. Data Science has reached another level by introducing self-driving cars using Artificial Intelligence.
- Spam Filtering in Emails – Spams are unwanted emails that have become a nuisance with their upsurge. This challenge has given rise to the need for anti-spam filters that are robust and highly reliable. Machine learning has made it possible to detect and filter spam emails successfully. By training the machine learning model on a dataset of emails (some spam and some not spam), one can test the model with an email that is not part of the data set on which they trained the machine learning model. The model can correctly identify whether the email is spam by detecting patterns of fake words, typically present in emails that promote products with over-the-top discounts.
- Predictive text (Autocomplete) – Word completion is a feature of many apps where the app can predict which word or phrase the user wants to write by analyzing only the first few letters of the word that the user types. In the app’s GUI (Graphical User Interface), users can see the terms suggested by the app and select the correct word by either pressing the tab key or the down arrow key. These predictive searches work on AI concepts such as NLP (Natural Language Processing), deep learning, and machine learning.
- Streamline Manufacturing – You can utilize many data science concepts to identify areas of manufacturing processes that are inefficient. Machines used for manufacturing gather vast volumes of data from production processes. This data could be too high at times to be accurately analyzed by humans. In such cases, data science is of immense use as it enables the quick gathering, cleaning, and sorting of data. One such tool, called Golden Run, was developed by Oden Technologies, an industrial automation company. This machine-learning tool can collect manufacturing data and identify times of the highest efficiency. The Golden Run tool then recommends how you can replicate the high-efficiency state.
Wrapping Up
Data science has impacted all business segments and society at large. With major industries like banking, manufacturing, eCommerce, transport, healthcare, etc., utilizing data science to improve their products and processes, it is no wonder that the need for data scientists has been growing exponentially in the past few years. So, whether you are a fresh graduate or an experienced professional, a data science and engineering course will boost your career as data scientists are one of the most highly paid professionals. It would be advised to brush up on your basics of data science first, in order to excel in your data science career.