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What is Data Science?

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process.

“The ability to take data — to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”

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## [1] "Quoted from: Hal Varian"
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## [1] "Occupation: chief economist at Google and UC Berkeley professor of information sciences, business, and economics"

The Data Science Life Cycle

The Data Science Life Cycle

The Data Science Life Cycle

Following the image above, effective data scientists are able to

  • Identify relevant questions
  • Collect data from a multitude of 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, causing skilled data scientists to be increasingly valuable to companies.

What Does a Data Scientist Do?

In the past decade, data scientists have become 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 curious and result-oriented, with exceptional industry-specific knowledge and communication skills that allow them to explain highly technical results to their non-technical counterparts. They possess a strong quantitative background in statistics and linear algebra as well as programming knowledge with focuses in data warehousing, mining, and modeling to build and analyze algorithms.

In reality, about 80% of what data scientists do is data cleaning.

Career Outlook and Salary Opportunites

Data Analyst

  • Average Salary: $65,470
  • Data analysts bridge the gap between data scientists and business analysts. They are provided with the questions that need answering from an organization and then organize and analyze data to find results that align with high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.

Data Scientist

  • Average Salary: $120,931
  • Data scientists examine which questions need answering and where to find the related 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.

Data Engineer

  • Average Salary: $137,776
  • Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.