Data science mostly deals with downsizing chunks of data into pertinent and relevant information. Data scientists are in high demand as almost every organization has a large amount of data that needs to be processed and analyzed carefully. A data scientist is skilled in programming, mathematics, statistics, soft skills, communication and visualization. They have a good grasp on every domain.
Data science mostly deals with initial process of understanding the business and its operations. By posing various questions, they learn about the organizational goals and expected outcomes. Understanding the importance of data and how one can utilize the power to derive outcomes for one’s business by posing the right questions is an important agenda that only comes with a lot of experience.
Data Mining is the crucial step that involves collecting the data from various sources. Some data scientists tend to club data cleaning and retrieval together but it is necessary to realize that each process must be dealt separately and at atomic level. Once all the data is at your disposal one can query the data using SQL or data manipulating tools like Pandas.
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Data cleaning and preparing is done after collecting the data from the respective sources. It is a crucial step when companies deal with terabytes of data in big data projects. Although this is a time-consuming step, it is necessary to get rid of redundant data at each step. This helps to reduce the occurrence of anomalies such as an inconsistent data. Inconsistent data often involves misprint of string of digits or fault in the data types used or grammatical errors. There is a possibility that some of the data could be missing and must never rule this out. Missing data can lead to an inefficient Machine learning model. A common approach called ‘Average imputation’ can be used which is capable of replacing all the missing components with average of other instances. In some cases, it can reduce the variability of the data, but helps in other cases.
Once the set of data is accumulated, one can begin to analyze the data. The data exploration stage helps you to understand the pattern and provide insights to your data. By taking help of the distribution curve or plotting a histogram, one can analyze the general trend and create an interactive visualization, which helps you look into each and every data point. One can form hypothesis and derive conclusions regarding the data using this information.
In terms of Machine learning, a feature is an attribute or a measurable property of a phenomenon that is under observation. Features can be employed in complex tasks such as character recognition. Typically, two types of feature engineering are performed- feature selection and construction. Feature selection deals with the shortening of features that prove ineffective in adding more information. Whereas, feature construction mainly deals with the creation of new features from the existing ones. Finally, the accurate model is chosen and deployed that fits the data perfectly.
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Data science is one of the emerging fields and has gained a lot of popularity in IT sector. To excel your skills and learn about the evolving technology check out the Data science course in Pune. This course can help you gain knowledge and good grasp on your concepts.
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Data Science Course in Bangalore
