A Guide to Data Science

Ever wondered what the weather will be like tomorrow? Maybe you want to know how busy the roads are, or if your favourite sports team has a chance of winning? Data Science has the answer to it.

Data science is a rapidly expanding field that combines the power of computers and statistics with a scientific methodology to produce powerful insights from data. It's the crossroads of the hard sciences and computer programming, and it allows you to make your voice heard in a variety of areas.

How it all began –

The term Data Science emerged recently but making sense of the data has been around for a long time and has been discussed by scientists, statisticians, computer scientists, and various others over the year. The following timeline traces the evolution of the term “Data Science”

In 1962, John W Tukey – An American Statistician stated that “Data Analysis, and the part of statistics which adhere to it, must… take on the characteristics of science rather than mathematics… data analysis is intrinsically an empirical science”. Later in 1977, he published a paper called “Exploratory Data Analysis”, emphasizing the need to be placed on using data to suggest a hypothesis to evaluate and that Exploratory Data Analysis and confirmatory Data Analysis.

In 1996, in a conference held by ‘International Federation of Classification Societies’ Chikio Hayashi – A Japanese Statistician stated that it includes three phases “Design for data, Collection of Data and the Analysis of Data”

Later in 2001, Willan S. Cleveland, An American computer scientist outlined in his article “Data Science: An Action Plan for Expanding the Technical Areas of Statistics” – that Data Science is a fully analytical discipline.

The Importance of Data Science –

Data Science has made great strides, emerging as a multidisciplinary response to the expanding extent of data. It is now an essential aspect of understanding data – it has evolved into a vital aspect for businesses, as it assists business leaders and pioneers in making decisions based on facts, statistics, and patterns. It also enables businesses to carry out critical tasks handling data and delivering solutions for specific problems, managing financial risks, identifying fraudulent transactions, blocking cyberattacks, and other security concerns in the IT system.

Data Science is also important in areas other than the typical business operations, such as in Academic Institutions, where it can aid in the monitoring of student performances. It can be utilized in diagnosing medical disorders, image analysis, treatment planning, and even in medical research in the health sector. Even sports teams can benefit from Data Science; It can be used to analyze players’ performance, and even assist in-game strategy planning.

The Process and Lifecycle of Data Science –

• Business Understanding:
  Understanding of business is vital in any project, regardless of whether it is data science or not. This is where you establish a business challenge, as well as the essential metrics and success criteria for the project. Everything that happens after that is aimed at addressing and resolving this issue.
• Data Acquisition:
  After understanding the business and its objectives, it is necessary to understand the data sources, their quality, relevance to the problem statement, and other needs.
• Exploratory Data Analysis and Visualization:
  In this stage, we strive to find the relationship between variables, detect outliers, apply statistical methods to identify patterns, and finally visualize the findings with a storyline. It is extremely analytical and at the same time requires creativity in the visualization of complex data insights.
• Feature Engineering and Extraction:
  It involves the application of domain knowledge and techniques to transform the data into a form where models’ predictive ability is increased. Ex: Categorical encoding, log transformation, scaling and power functions.
• Model Building:
  Once the data pre-processing is complete, create a baseline model. Then gradually increase the complexity, apply algorithms, tune the parameters to find the right mix, and make sure the model is not overfitted.
• Model Deployment:
  Model is deployed to use it for practical purposes. Generally, models are deployment on the cloud and in some cases on-premises based on business requirements.
• Model Monitoring: 
  Keep track of the data and the models’ performance to ensure there is no data/model drift. It is also important to retrain the models at a regular interval based on business impact.

The Benefits of Data Science –

The biggest benefit of data science is the empowerment and facilitation of better decision-making. The business will be able to factor quantifiable, data-based evidence into their business decisions, which can lead to stronger business performances, cost savings, and smoother business processes and workflows.

The potential business benefits include but are not limited to increased or higher Return on investments, Sales growth, better and efficient operations, faster time to market, and increased engagement and satisfaction. It enables real-time analysis of data as it is generated.

Other benefits of using Data Science include reducing fraud, risk management, profitable financial trading, better manufacturing uptime, increased supply chain performance, robust cybersecurity protections, and improved patient outcomes.

The Application of Data Science in different Industries:

Here are the examples of how different industries can use data science –

• Financial Services:
  The banks and credit card companies mines and analyze data to detect fraudulent transactions, evaluate customer portfolios to identify upselling opportunities, and manage financial risks on loans and credit lines.
• Transportation:
  Freight carriers, logistics services, and delivery companies’ providers use data science to optimize their delivery routes, schedules, and their best modes of transport for shipments.
• Retail: 
  Retailers can analyze customer behaviors, buying patterns that will help them drive personalized product recommendations and targeted advertising, marketing, and promotions to their customers. It also helps them in managing product inventories and their supply chains to keep items in stock.
• Manufacturing:
  Data science helps in the optimization of supply chain management and distribution and can give predictive maintenance to detect potential equipment failures in plants before they occur.

The common uses of data science are in the areas such as cybersecurity, customer services, and business process management are common across different industries. An example of common use is assistance in employee recruitment and talent acquisition: data science can analyze and identify common characteristics of top performers, measure how effective the job postings are, and provide other information to help in the hiring process.

Future of Data Science –

Data Science is a vast collection of various data activities. Machine learning and statistics are also included in these data operations. Citizen data scientists are projected to play a larger part in the analytical process as data science becomes more common in enterprises. In a Magic Quadrant report from 2020, Gartner stated that "the need to support a broad set of data science users is increasingly the norm" – one likely result of this is an increase in the use of automated machine learning, which includes skilled data scientists looking to streamline and accelerate their work.

Other trends that will impact data scientists' work in the coming years include a growing push for explainable AI, which will provide information that will help people understand how AI and Machine learning models work and how much to trust their findings in decision-making, as well as a relatable focus on responsible AI principles, which are designed to ensure that AI technologies are fair, unbiased, and transparent.

Wondering how to take advantage of Data Science for your Organization? Get in touch with us.