What REALLY is Data Science? - Brainchecker

What REALLY is Data Science?

What profession did Harvard call the Sexiest Job of the 21st Century?

Ans: The data scientist.

This article by Brain Checker provides a Data Science definition and discussion meant to help define the data scientist role and its purpose, as well as typical skills, qualifications, education, experience, and responsibilities.

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they 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.

Data Science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning algorithms to numbers, text, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights that analysts and business users can translate into tangible business value. Data science combines multiple fields, including statistics, scientific methods, artificial intelligence (AI), and data analysis, to extract value from data. Those who practice data science are called data scientists, and they combine a range of skills to analyze data collected from the web, smartphones, customers, sensors, and other sources to derive actionable insights.

Data science encompasses preparing data for analysis, including cleansing, aggregating, and manipulating the data to perform advanced data analysis. Analytic applications and data scientists can then review the results to uncover patterns and enable business leaders to draw informed insights.

Why Data Science is Important?

More and more companies are coming to realize the importance of data science, AI, and machine learning. Regardless of industry or size, organizations that wish to remain competitive in the age of big data need to efficiently develop and implement data science capabilities or risk being left behind.

Data Science + DataRobot

Ramping up data science efforts is difficult even for companies with near-unlimited resources. The DataRobot AI Cloud Platform democratizes data science and AI, enabling analysts, business users, and other technical professionals to become Citizen Data Scientists and AI Engineers, in addition to making data scientists more productive. It automates repetitive modeling tasks that once occupied the vast majority of data scientists’ time and brainpower. DataRobot bridges the gap between data scientists and the rest of the organization, making enterprise machine learning more accessible than ever.

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 understand that they 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 Data Science Life Cycle

Data Science Life Cycle

The image represents the five stages of the data science life cycle: Capture, (data acquisition, data entry, signal reception, data extraction); Maintain (data warehousing, data cleansing, data staging, data processing, data architecture); Process (data mining, clustering/classification, data modeling, data summarization); Analyze (exploratory/confirmatory, predictive analysis, regression, text mining, qualitative analysis); Communicate (data reporting, data visualization, business intelligence, decision making).

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 Mc Kinsey & 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.

“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.”

– Hal Varian, chief economist at Google and UC Berkeley professor of information sciences, business, and economics

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 almost 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 necessary assets and are present in almost 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 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 on data warehousing, mining, and modeling to build and analyze algorithms.

Data Science Goals and Deliverables

In order to understand the importance of these pillars, one must first understand the typical goals and deliverables associated with data science initiatives, and also the data science process itself. Let’s first discuss some common data science goals and deliverables.

Here is a shortlist of common data science deliverables:

  • Prediction (predict a value based on inputs)
  • Classification (e.g., spam or not spam)
  • Recommendations (e.g., Amazon and Netflix recommendations)
  • Pattern detection and grouping (e.g., classification without known classes)
  • Anomaly detection (e.g., fraud detection)
  • Recognition (image, text, audio, video, facial, …)
  • Actionable insights (via dashboards, reports, visualizations, …)
  • Automated processes and decision-making (e.g., credit card approval)
  • Scoring and ranking (e.g., FICO score)
  • Segmentation (e.g., demographic-based marketing)
  • Optimization (e.g., risk management)
  • Forecasts (e.g., sales and revenue)

Each of these is intended to address a specific goal and/or solve a specific problem.

Where Do You Fit in Data Science?

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 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.

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 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.

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, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.

Skills needed: Programming languages (Java, Scala), No SQL databases (MongoDB, Cassandra DB), frameworks (Apache Hadoop) data scientists can have a major positive impact on a business’s success, and sometimes inadvertently cause financial loss, which is one of the many reasons why hiring a top-notch data scientist is critical.

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About the Author: Keya Raje

Senior Counselor ,M.A. Psychology.

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