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Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For most organizations, it is employed to transform data into value in the form of improved revenue, reduced costs, business agility, improved customer experience, the development of new products, and the like. Data science gives the data collected by an organization a purpose.
While closely related, data analytics is a component of data science, used to understand what an organization’s data looks like. Data science takes the output of analytics to solve problems. Data scientists say that investigating something with data is simply analysis. Data science takes analysis another step to explain and solve problems. The difference between data analytics and data science is also one of timescale. Data analytics describes the current state of reality, whereas data science uses that data to predict and/or understand the future.
The business value of data science depends on organizational needs. Data science could help an organization build tools to predict hardware failures, enabling the organization to perform maintenance and prevent unplanned downtime. It could help predict what to put on supermarket shelves, or how popular a product will be based on its attributes.
For further insight into the business value of data science, see “The unexpected benefits of data analytics” and “Demystifying the dark science of data analytics.”
While the number of data science degree programs are increasing at a rapid clip, they aren’t necessarily what organizations look for when seeking data scientists. Candidates with a statistics background are popular, especially if they can demonstrate they know whether they are looking at real results; have domain knowledge to put results in context; and communication skills that allow them to convey results to business users.
Many organizations look for candidates with PhDs, especially in physics, math, computer science, economics, or even social science. A PhD proves a candidate is capable of doing deep research on a topic and disseminating information to others.
Some of the best data scientists or leaders in data science groups have non-traditional backgrounds, even ones with very little formal computer training. In many cases, the key ability is being able to look at something from a non-traditional perspective and understand it.
For further information about data scientist skills, see “What is a data scientist? A key data analytics role and a lucrative career,” and “Essential skills and traits of elite data scientists.”
Here are some of the most popular job titles related to data science and the average salary for each position, according to data from PayScale:
According to Fortune, these are the top graduate degree programs in data science:
Given the current shortage of data science talent, many organizations are building out programs to develop internal data science talent.
Bootcamps are another fast-growing avenue for training workers to take on data science roles. For more details on data science bootcamps, see “15 best data science bootcamps for boosting your career.”
Organizations need data scientists and analysts with expertise in techniques for analyzing data. They also need big data architects to translate requirements into systems, data engineers to build and maintain data pipelines, developers who know their way around Hadoop clusters and other technologies, and system administrators and managers to tie everything together. Certifications are one way for candidates to show they have the right skillset.
Some of the top big data and data analytics certifications include:
For more information about big data and data analytics certifications, see “The top 11 big data and data analytics certifications,” and “12 data science certifications that will pay off.”
Data science is generally a team discipline. Data scientists are the core of most data science teams, but moving from data to analysis to production value requires a range of skills and roles. For example, data analysts should be on board to investigate the data before presenting it to the team and to maintain data models. Data engineers are necessary to build data pipelines to enrich data sets and make the data available to the rest of the company.
For further insight into building data science teams, see “How to assemble a highly effective analytics team” and “The secrets of highly successful data analytics teams.”
The goal of data science is to construct the means for extracting business-focused insights from data. This requires an understanding of how value and information flows in a business, and the ability to use that understanding to identify business opportunities. While that may involve one-off projects, more typically data science teams seek to identify key data assets that can be turned into data pipelines that feed maintainable tools and solutions. Examples include credit card fraud monitoring solutions used by banks, or tools used to optimize the placement of wind turbines in wind farms.
Incrementally, presentations that communicate what the team is up to are also important deliverables.
Production engineering teams work on sprint cycles, with projected timelines. That’s often difficult for data science teams to do because a lot of time upfront can be spent just determining whether a project is feasible. Data must be collected and cleaned. Then the team must determine whether it can answer the question efficiently.
Data science ideally should follow the scientific method, though that is not always the case, or even feasible. Real science takes time. You spend a little bit of time confirming your hypothesis and then a lot of time trying to disprove yourself. In business, time-to-answer is important. As a result, data science can often mean going with the “good enough” answer rather than the best answer. The danger, though, is results can fall victim to confirmation bias or overfitting.
Data science teams make use of a wide range of tools, including SQL, Python, R, Java, and a cornucopia of open source projects such as Hive, oozie, and TensorFlow. These tools are used for a variety of data-related tasks, ranging from extracting and cleaning data, to subjecting data to algorithmic analysis via statistical methods or machine learning. Some common tools include:
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