Data Science and Analytics: The present day business environment is witnessing the growing implementation of analytical forecasting tools in a big way. This may be attributed to the increase in complexity of the exhaustive data collection points, competitiveness and the rate of change in the business environment. The objective of the Data Science is to present a comprehensive view of the various tools and techniques used in forecasting for managerial decision making including the problem of demand estimation, market size determination, sales projections, analysing and predicting stock prices.

AI and ML in Data Science

Machine learning algorithms are part of artificial intelligence (AI)/Data Science that imitates the human learning process. Machines are more powerful than the humans at analysing vast amounts of data and gain insights about the business complexity. Machine Learning (ML) algorithms have applications across various industries and different functional areas. The primary objective of ML is to assist in decision making. Today ML is used for driving innovation and as competitive strategies by several organizations. With machine learning algorithms, one can develop models in various domains from which expert knowledge can be deduced.

Methodology and Tools

The methodology, covering various time series analysis techniques, as well as regression methods, is presented with appropriate mix of case analysis and numerical demonstration with the aid of software packages so as to enable the required forecasting needs.

There are myriads of data analytics tools that help us get important information from the given data. We can use some of these free and open source tools even without any coding knowledge. Some of the most popular tools are SAS, Microsoft Excel, R, Python, Tableau, RapidMiner etc. Among the above tools, Tableau Public is free software developed by the public company “Tableau Software” that allows users to connect to any spreadsheet or file and create interactive data visualizations.

Simplifying the complexity

Decision making and problem solving have become complex due to competition and scale of operations of the business organisations. The traditional ERP systems running in organisations today has ensured availability of data, however the ERP systems lacks data analysis capabilities that can assist the management in decision making. The theory of bounded rationality proposed by Nobel Laureate Herbert Simon is evermore significant today with increasing complexity of the business problems; limited ability of human mind to analyse alternative solutions and the limited time available for decision making.

Machine learning algorithms are a set of techniques and heuristics (problem solving hacks for faster solution) that can be used to analyse data to improve business performance through fact-based decision-making. In the recent past, automation and innovation across several industries has been driven by machine learning algorithms.

The path ahead

Several reports claim that AI and machine learning specialists in Silicon Valley with few years of experience are paid $300,000 – $500,000 a year. Bernard Marr in his article published in the Forbes magazine claimed that 74 per cent of the customers will be happy to receive computer generated insurance advice.

GITAM is striving to excel in academics while integrating the above courses in the curriculum of most of the relevant programmes. A one day Workshop on ‘Data Visualisation Using Tableau’ was facilitated for the Students of M.Sc (Data Science) of GITAM, Visakhapatnam recently.

There are a number of specialised institutions along with universities and colleges offering certificate/diploma/advanced diploma/degree courses to prepare the students industry ready.