A Guide to Data Science for an Organizational evolution

Data Science deals with data and data only. Quest goes! Is there anything that is not data? Ponder a little. Skip the question. We all know the atrocious attrition rate in an organization can have a deadly consequence on financial and non-financial performance. The quest goes towards knowing how many employees are there, what are they doing? What are they contributing? What are they getting? What others are keen to offer him? Is he like to leave? What can influence him to stay? At a given time point as well as on the progressive order of time on a real-time basis, under essential similar conditions, the organization is interested to know the answers for each employee. So many employees, every moment, for all the employees, answers to each of the employees are data and maximally are unstructured data. These data are to be sourced, collected, stored, processed, linked, sequenced, arranged in the progressive order of time. Job is enormous with huge Big Data using Data Engineering. It involves computer science, Information Technology in HR Domain. Is it possible to collect store such data with constant human intervention? Of course not. Automation with Artificial Intelligence is used for a data engineering job.

Next, the data to be used to understand the pattern of attrition, influencers of attrition, models for predicting an individual to attrite with estimated accuracy. This is Data Analytics. Data Preparation, Data Visualisations, Business Intelligence, Exploratory Data Analysis, Modelling, Descriptive analytics, and lastly predictive analytics perspective is brought to predict. However, even this is not possible through manual interventions or limited and offsite automation support. All these models are improvised with automation as a machine and the machine will keep learning the way we learn through Machine Learning algorithms. The phenomenon of machine learning is more pragmatic and may not penetrate the deeper insight latently availably in data. The need is to deep dive to unearth the information through Deep Learning. One can do not afford to use machine learning and deep learning for pieces of information. These must be made available on a real-time basis, intervened as needed, and change the model if needed by itself, and here comes Artificial Intelligence again in Data Analytics.

Tools such as R, Python, Orange, Tableau, Power BI to say a very few, are used for the purpose. This Data Science must be viewed as Data Engineering plus Data Analytics using Tools in any domain involving mathematics, statistics, computer science, and Information Technology.

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