data science by technology moon

Posted by ashwin jain on July 5th, 2019

Data Science

Introduction

Data science has been evolving as one of the most promising and in-demand career paths for skilled professionals. Now-a-days successful data professionals get to know that they must upgrade the traditional skills of analyzing huge amounts of data, data mining, and programming skills. In order to get useful intelligence for their organizations, data scientists must practise the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each and every phase of the process.

The term “data scientist” was coined as recently as 2008 when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data. In a 2009 McKinsey&Company article, Hal Varian, Google's chief economist and UC Berkeley professor of information sciences, business, and economics, predicted the importance of adapting to technology’s influence and reconfiguration of different industries.

Skilled data scientists are capable of identifying appropriate questions, collect data from different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in all industries, resulting skilled data scientists to be increasingly important to many companies.

Work of a Data Scientist

Data scientists have become important and necessary assets and are present in all organizations. These professionals are well-rounded, data-driven individuals with high-level technical skills who are capable of building complex quantitative algorithms to organize and synthesize large amounts of information used to answer questions and drive strategy in their organization. This is coupled with the experience in communication and leadership needed to deliver tangible results to various stakeholders across an organization or business.

Data scientists need to be creative,innovative,always questioning and result-oriented, with industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical officials. They possess a powerful background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

Scope of becoming Data Scientist

Glassdoor ranked data scientist as the Best Job in America in 2018 for the third year in a row. As increasing amounts of data become more easily available to everyone, large tech companies are now not the only ones in need of data scientists.

The need for data scientists shows no sign of slowing down in the coming years.

Data is everywhere and expansive. A variety of terms related to mining, cleaning, analyzing, and interpreting data are often used interchangeably, but they can actually involve different skill sets and complexity of data.

Data Scientist

Data scientists examines that which questions need answering and where to get the relevant data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data. Results are then synthesized and communicated to key stakeholders to drive strategic decision-making in the organization.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, storytelling and data visualization, Hadoop, SQL, machine learning

Data Analyst

Data analysts bridge the gap between data scientists and business analysts. They are provided the questions that needs to be  answered from any company and then analyze data to find results relevant to the high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Skills needed: Programming skills (SAS, R, Python), statistical and mathematical skills, data wrangling, data visualization

Data Engineer

Data engineers manage exponential amounts of rapidly changing data. They focus on the development and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

Skills needed: Programming languages (Java, Scala), NoSQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop)

Data Science Career Outlook and Salary Opportunities

Data science professionals are paid for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in almost all industries. 

For example, machine learning experts utilize high-level programming skills to create algorithms that continuously gather data and automatically adjust their function to be more effective.

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