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A Detailed Overview Of Data Science Process Lifecycle

Data Science has now become one among the hottest fields in the analytics industry. The continuous advancements in the computational performance have now made it possible to analyze enormous levels of Big Data. Also, uncovering hidden patterns & extracting hidden patterns behavior so as to uncover the trends has also become a lot easier. 

The Data Science lifecycle process is having many interpretations. Data science is a quickly evolving field, and its terminology is rapidly evolving with it.

Data Science Process Lifecycle-

  • Business Understanding

Data Scientists are the experts who will constantly questioning & reasoning. Having become a crucial part in the decision making process of any enterprise, Data Scientists are now working towards supporting the business managers & strategists to make informed decisions for ideal results. 

Before beginning with the Data Science project lifecycle process, it is very much crucial for the Data Scientists to have a clear understanding of the problem you they are given to solve. Data Scientists will be working towards identifying the business objectives by identifying the variables that are to be predicted.

  • Data Acquisition

Data Acquisition or Data Collection is the is the process of gathering the data that could present your with the relevant answers to the questions which the Data Scientist have defined earlier. In some projects, Data Scientists will be presented with predefined data set to work while in some projects they have to gather all the data right from the scratch..

This process of Data acquisition may involve different aspects like web scraping, database queries, and scripting emails requesting data, creating labeled features by hand & even it involves setting up infrastructure that will help in collecting data.

The extent of success in the existing Data Science model is largely dependent on the accuracy in the data collected and how Data Scientist process it. 

  • Data Preparation

After having acquired the needed data, the very next step involving preparing the data for the analytics process. No matter, how the data is collected, Data Scientists need to clean and prepare the data for analysis. This is a crucial process because the data which is collected from multiple sources will mostly be in unstructured & unorganized format. This simply means that the process of analysis cannot be directly applied on this format of data.

As a part of the Data preparation process, Data Scientists may sometime come to know that they are needed to go back and gather more data.  The process of data collection is very crucial because it helps the Data Scientists towards getting a better understanding of the data. Data may come from whatever source, but it is the duty of a Data Scientist to gather the right data relevant to the problem in hand & prepare it by adopting the right steps.

Usually the process of Data preparation takes a lot of time & if the collected data is really huge then there’s no doubt that this would seem to be a tiresome process. As per expert Data Scientists, more the 80% of their time gets consumed by data preparation and cleansing process.

  • Hypothesis And Modelling-

This is one among the crucial process in the Data Science life cycle process. As a part of this process, Data Scientists are needed to carry extensive writing, running and refining the programs so as to extract accurate insights from the data. The most commonly used programming languages for this process include Python, R, MATLAB or Perl. Among these languages, most of the Data Scientists prefer Python because of its ease of programming nature & also due to the presence of excessive libraries.

Also, as a part of this process, Data Scientists will be working on various machine learning techniques are applied so as to determine which Machine Learning algorithms  can be sued that would best fit the business needs.

  • Evaluation and Interpretation

This process involves evaluation various metrics for different performance metrics. In order to accurately the Machine Learning model performances the right process would be to measure and compare it using validation and test sets to identify the best model based on model accuracy and over-fitting.

  • Deployment

In most of the cases, Machine Learning models are needed to be recorded before deployment. In the proceeding step, the Machine Learning models are deployed in a test environment & are then deployed into production upon satisfactory test results.

Data Visualization & Communication-

The insights that are extracted are then communicated to others through the application of attractive visuals through interactive dashboards that are supported by Data Visualization tools like Tableau. The findings from the data extracted by the Data Scientists will be playing a crucial role helping the businesses to make accurate data driven decisions. This helps them in achieving their business objectives. Build expertise towards working on the Data Science life cycle process with experts training by enrolling for Data Science Training In Hyderabad by Kelly Technologies.

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