What does it mean to be a Full Stack Data Scientist in Value Base Academy?

As Data Science becomes prevalent and more industries are aligning to build their data scientist teams as a new concept has been emerging - the “Full Stack Data Scientist”. What is this new role? What should an organization do about it?

Simply, a full-stack data scientist is one who takes a data-driven concept from identification with execution that results in some substantive, quantitative and impactful improvement. In the technical sense “full-stack” in data science has a lot of parallels with descriptions of full-stack developers. For example, one could have a full-stack of technical skill sets and produce an unstructured group of data in a data science model for a business process.

Value Base Academy is building full-stack data scientists who understand that success means developing businesses by implementing their domain expertise by creating end-to-end solutions and analytical products. It is transformative and provides expertise that is industry-specific and customised with real life projects and portfolio worthy capstone projects.

Stages in the Data Science Lifecycle of Full-Stack application

  1. Business Understanding: This is the problem one can solve with the help of data, then the business will generate profit.

  2. Data Understanding: With data understanding, one can play with an analytics platform to refine, define, slice and dice data.

  3. Data Preparation: Development of a hyper-parameter for the model that one will be trained.

  4. Modeling: This is where one applies supervised, unsupervised, and deep learning algorithms.

  5. Evaluation: To track model performance and consistently improve the algorithm.

  6. Deployment: Model is deployed and data is flowed to keep the consistent business values.

Skills of a Full-stack Data Scientist

To achieve the scope of “executing on a data science business capability from beginning to end” a full-stack data scientist should be able to comfortably grip the following aspects of such an advantage, including:

  1. Identifying and understanding the business problem

  2. Understanding the business or operation

  3. Identifying data sources

  4. Data Science (AI, ML. BI, Data Analysis)

  5. Extract, Transform, Load (ETL)

  6. Analysis and Modeling

  7. Communication building

  8. Productionizing output

  9. Having an impact

Value Base Academy provides hands on experience and encapsulates a holistic knowledge of full stack data science with it's 12 week certified program. To give you clear insight, McKinsey Global Institute study states that the US will face a shortage of about 190,000 data scientists and 1.5 million managers and analysts who can understand and make decisions using Big Data by 2018. As a result making full-stack data science a highly in demand role in the current job market with the automation of complex processes becoming the new way of carrying out efficient operations. A full stack data scientist should be given the ability to select and apply the right tools such as access to data lab and analytical platforms for attaining significant applied knowledge. And Value Base Academy offers this exclusive access to the participants of this program. The training organisation is spearheading by fulfilling the global need by empowering technology enthusiasts to become powerful applied data scientists.

This blog article has been compiled by researching these articles given below.








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