It is a noted fact that Data scientists would have to deal with a large quantity of data to crack the required information and therefore to secure it with adequate mathematical strategies. The classification of structured and unstructured data is an important task of a data scientist. When it comes to the life cycle of a data scientist, it is directly proportional to the varied stages concerned within the study and advancement of data science.
Major deciding factors that would increase the life cycle of a data scientist
Data wrangling capacity shall be quoted in the first place. It holds the most challenging part in Data science where the collection, segregation, analyses, classification, visualization and implementation of various data sets plays an integral part. Besides proving to be an ideal first quality of an efficient data scientist, it additionally strengthens the career prospects at a significant level. Infrastructure maintenance inculcates some great qualities required to continue working as a data scientist. Once a data scientist reaches a specific state with a set of skills that can prove his flawless data handling, data mentorship opportunities pour in enhancing the life cycle to a different extent.
Software engineering skills build a data scientist to ascertain a robust background in varied phases including data construction, separation, dissection, monitoring, and production. The job description for such type of data experts is either data engineer of data scientist.
Basic understanding of concepts involved in the design, development and deployment phases leading to a successful data science project is the core knowledge required from a data scientist in order to survive for a long period. There are some false speculations relating the life cycle of data science with that of a soft engineering project.
It is essential to bisect the basic differences between these two criteria. A detailed study of a data scientist project can be taken by an aspiring data scientist for a consistent prospect. The study includes seven important phases of the life cycle of a data scientist project.
Data acquisition is the primary and initial step in the process of the life cycle. People with extremely qualified skills are allocated to perform this operation which involves some serious data acquiring processes. Once a particular data scientist is allotted for the job, he will be assigned to handle a series of hard-hitting tasks to track the source of data. Data may be received from both internal and external sources. Therefore, the data scientist bisects various data sets for the purpose of tracking down the exact source. Sometimes data might be reacquired which would also be sorted accordingly.
The second most significant phase is data preparation where the data scientists should clean, organize and process the acquired data within the approach that answers the meaningful insights such as the purpose of having obtained the particular data sets. To discover the relevant business insights of a data, a data scientist should be able to perform hypothesis and modeling with the use of languages such as Python, R, MATLAB or Perl.
Evaluation and interpretation challenge the data scientist to look out for different performance metrics i.e. the data is exposed to testing its nature to identify the type of learning it shall deliver. The data scientist would have to reiterate the above four phases to check the credibility of a business understanding making it clearer.
The final three phases are almost like the post-processing steps. It includes deployment, operations and maintenance and optimization. These phases perform activities such as testing, monitoring and maintenance and product assurance.
Data scientists are generally advised to stay within the circle bounded with a constant update in order to realize their career’s maximum life expectancy as there is no definite boundary for learning in the ever-growing field.