Explained: Data Science

Posted by Teksands on April 27th, 2021

Today, Data Science is an essential part of every industry, be it Healthcare, Gaming, Recommendation system or logistics. The popularity of Data Science has grown immensely in recent years, and businesses have started implementing Data Science techniques to increase customer satisfaction. Before understanding how making a career in Data Science will benefit you, we must first know what Data Science is.

 Data Science is a field that uses algorithms and systems to extract knowledge and valuable insights from unstructured data. This valuable information can be used while making business decisions. In Data Science, predictive models are built by using complex Machine Learning algorithms. The data can be procured from multiple sources and presented in various formats.

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 How Can Data Science Help In Business

 Data science helps in Predictive Analysis, Data Modeling through various algorithms and communicate the results through graphs. Data Scientist has prerequisite skills through which they can identify data that stands out in some way. One of the most important benefits of Data Science is that one can understand their target audience on a very granular level. Data Scientists helps in extracting valuable insights through which one can predict customer behaviour and use it from a sales and marketing point of view. A Data Scientist can add value to any business by empowering management to make better decisions, challenge the staff to adopt better practices while focusing on issues that matter. Data Science helps to identify the target audience and turn the company’s action based on market trends so as to identify better growth opportunities. 

 Basic Machine Learning Algorithms That A Data Scientist Must Know About

1. Decision Tree - The main advantage of a decision tree is that it is easy to understand. The method is used for classifications. The system classifies different inputs according to specific parameters.

2. Regression - Regression is an algorithm whose output is always actual or continuous value. The algorithm is based on supervised learning techniques. 

 3. Support Vector Machines - SVMs are supervised learning method and can perform both linear and non-linear classifications.

 4. Clustering - Clustering works in the form of data that is unlabeled and then places each data point into a cluster. This ML algorithm is based on unsupervised learning techniques.

 The cycle of a Data Science project includes different stages like concept study, data preparation (data integration, data transformation, data reduction, data cleaning), model planning, model building, communication, and operationalisation. 

 Data Science As A Career

 Since there is so much demand for a Data Scientist but less supply, the job vacancies for Data Science has increased tremendously. There are several jobs that you can take up after making a career in Data Science like Data Scientist, Machine Learning Engineer, Data Consultant, and Data Analyst. 

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