10 Benefits why Data Science is a boon for every Industry

 

(Photo by Matthew Henry from Burst)

Data science (DS) is the need of the hour in almost every industry. As data plays an important role in making business-critical decisions, DS has become a must-have function in every organization.

Data science (DS) is no more a new term, even for a layperson. Nowadays, every industry is benefiting from various applications of DS, which were unimaginable a few decades ago – but now possible due to artificial intelligence (AI). AI is not a new concept and has been there for at least 60-70 years now. However, it only became possible for scientific and technology communities to make the best use of AI after the advancements in the field of computers and similar technologies.

DS works on historical data to predict classes, numerical values or clusters/groups of a given sample. Some of the examples include flight departure time, employee churn, loan defaulters, fraud detection, weather forecast, online product recommendations, etc.    

DS cannot exist without machine learning (ML), which is based on AI. ML can be divided into two main categories – Supervised ML and Unsupervised ML. Although both rely on historical data, the former requires data with response variable (that a model predicts), also called tagged or labeled data; while the latter doesn’t need data with response variable, i.e. data without label or tag. There is a third kind of ML, known as Semi-supervised learning, requiring some portion of data to be tagged or labeled. (Read on to know more on Machine Learning)

Supervised ML mainly consists of classification, regression and forecast models. Classification models predict the class of an object, situation, people, etc. (for example: male or female, rain or no rain). Regression models estimate the numerical values such as age, salary, etc. Forecast models are mainly time series models estimating values just like regression models (example: share price).

Unsupervised ML builds models based on algorithms, such as Clustering and Principal Component Analysis (PCA). Clustering divides data into various clusters depending on similarities withing a cluster and dissimilarities between two clusters. PCA uses principal components to group together similar data points.

Some of the famous models are mentioned below:

Classification: Random Forest, Deep Learning (Deep Neural Networks, LSTM, RNN), Decision Tree, K-NN, Naive Bayse, Logistic Regression, Ensemble Methods (boosting and bagging), Support Vector Machines (SVM), Recommender System, etc.

Regression: Linear Regression and some of the algorithms mentioned under classification

Forecast: ARIMA, Exponential Smoothing Models, etc.

Clustering: K-means, Hierarchical Clustering, mixture models, etc.    

Applications and Benefits of using data science:

There are numerous advantages of DS applications in every industry. 10 of them are mentioned below:

1. Predicting future possibilities to come up with successful strategies

2. Increased securities through fraud and anomaly detection

3. Reduced errors through anomaly detection

4. Increased efficiency through automation using DS

5. Reduced Turn-Around-Time through automation using DS

6. Enhanced customer experience by learning about their behavior through DS applications

7. Streamline business processes through automation of data collection and analysis using DS

8. Risk Analysis and Management

9. Personalized services to customers using DS based recommender system

10. Share-market Trading using DS Algorithm

The above list is not exhaustive and only indicative. 

However, DS works excellent when there is useful data, else it can be a complete waste of effort and time. There is a famous saying in data science community, "Garbage in, garbage out." If you feed useless data to your algorithm, you can only expect a useless model that may not help you make the right decision. (More on this...)

Following are some of the main industries that have benefited a great deal from DS applications:

Airlines, e-commerce, banking, financial sectors, insurance, healthcare, cybercrime security systems, social media, etc.