Data science is one of those broad fields in which data is collected, processed, analyzed and theories, tools, concepts, techniques, and technologies are obtained. This valuable knowledge is extracted from the information obtained simply from raw data.
This is done to help individuals and organizations make efficient decisions, consume, manage, and store data. Theoretical, computational, mathematical, and practical techniques and methods are used to obtain, analyze and evaluate the available data. This data can be used for multiple purposes like the development of products, forecasting, decision-making and trend analysis.
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What is the lifecycle of data science?
Phase1- Discovery- before starting any project, it is crucial to learn more about the priorities, specifications, required budget and other types of requirements. You need to be certain about the questions you ask and use all the available resources like people, technology, data and time in a very optimum way. This phase also includes framing of the business problem and formulating the initial hypothesis to test.
Phase2- Data preparation- This is the phase wherein an analytic sandbox is required to perform analytics for the project and to get the available data into the sandbox, ETLT (extract, transform, load and transform) is performed.
Phase3- Model planning- This phase is where all the techniques and methods are used so that relationships can be drawn between variables and this will set a base for all the algorithms to be implemented in the upcoming phase. R, SQL analysis services, SAL/ACCESS are some of the tools used for model planning.
Phase4-Model building- Datasets will be developed in this phase for the purpose of training and testing. Here whether the tools will be able to run the models or will require an environment which is more robust will be kept under consideration. Numerous techniques for learning, classification, clustering and association will be analyzed.
Phase5-Operationalise- Technical documents, final reports, codes and briefings will be delivered and, also a pilot project will get implemented in a production environment which will be real-time.
Phase6- Communicate results- If the goal has been achieved, it is extremely important to evaluate all the planning done in phase 1. This last phase is for identifying the key findings and interacting with the stakeholders to determine whether the criteria decided in the first phase has been successful or has failed.
Read More: Data Science Course With Python
What job prospects are available in data science?
One of the most common jobs available after completing the course of data science is a data scientist. Data scientists are responsible for designing and implementing some of the processes used to mine data for modeling and research related purposes. Data architect is also one of the most common jobs in the data science field. Data architects help in the development of the underlying architecture so that it can be processed and analyzed and the extracted data can help organizations in several ways like creation of a blueprint, etc. Data analyst has also become one of the most well-known professions. Data analysts analyze the data and look out for meaningful insights like numerical information, charts, graphs, reports, etc. Some of the other common job prospects of data science are data engineers, statisticians and database engineers.
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Data science is a field which is growing day-by-day. More and new job prospects are emerging daily and data science has a great scope in the future. In order to make a prosperous career in data science and to become a successful data scientist, I joined a data science course in bangalore.
