Skills Required

Technical Proficiency: The skills of the code programming languages like Python, R, and SQL for data manipulation and analysis are the primary prerequisites for the mastery.

Data Handling: The ability to deal with the big data, to prepare and preprocess the data and to conduct exploratory data analysis is a key in the student’s arsenal.

Statistical Analysis: The basic proficiency in the statistical methods and machine learning algorithms that are the basis for the predictive modeling and inference is the key to the good knowledge of a matter.

Big Data Technologies: The students should have the knowledge of the tools such as Hadoop, Spark, and the frameworks for distributed computing for processing and analyzing the big data to acquire.

Data Visualization: Mastery of data visualization libraries and tools such as matplotlib, seaborn, Tableau or Power BI which are utilized to present the data in an efficient manner is the meaning of proficiency.

Domain Knowledge: The domain knowledge that is part of the brain of Finance, Healthcare, or marketing, which helps in making data analysis more useful in the current situation and also the actionable insights that are derived from it.

Problem-Solving Skills: The data processing of the complex issues, interpreting the patterns and coming up with the new solutions to the problems are some of the activities in which the researchers practice.

Communication Skills: The results of the study should be communicated in an unambiguous way and the main points of the study should be put in a simple manner. The investigator should likewise talk to the people from different functional areas so that they can work together, and be a source of information for the decision-making.

Leadership Abilities: The skills of the Capability to lead and mentor junior team members, leading the project in the desired direction, and pushing the strategic initiatives are the main features of the Able to train and guide the juniors.

Business Acumen: The data science projects which are in harmony with the organizational goals and the values which the company is based on, are the ones that help the comprehension of the business goals and priorities to the data science projects.

In the changing business world, data science architects are the key people who lead the way in combining the new techniques of data analysis with the organization’s plans. They are the persons who move in the area where the data analysis meets the solutions that can be executed, thus the technological competence is brought into line with the business goals. Nowadays, organizations are becoming more and more data-driven, and thus, data science architects are becoming more and more important in this process. This introduction part is for the exploration of the pathways, skills, certifications and training which are the steps that the person should take to start the career in a data science architect, it will guide the people to master the art of converting the data into strategic assets.