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In this post, I take a look at several industry experts' opinions about the skills, abilities and temperament needed to be a good data scientist.
In addition to analyzing structured data, businesses are now trying to uncover insights from unstructured data from such sources as social media, emails, community message boards and even open-ended comments in surveys.
The final group of skills reflect the need to approach problems using critical thinking, creativity and open-mindedness. These steps are not meant to imply that science is only a series of activities. Instead of thinking about science as an area of knowledge, it is better to conceptualize it as a way to understand how the world really works.
I believe that the scientific method plays a critical role in understanding any data, irrespective of their size or speed or variety. Despite the idea that Big Data will kill the need for theory and the scientific method, the human element is necessarily involved in the generation, collection and interpretation of data. Additionally, I highlighted a few ways that the scientific method can help improve the veracity (validity) of data. Even though finding a single person who possesses these data scientist skills is akin to finding a unicorn, companies need to understand all the data scientist requirements as they look to build data science teams to address their data analytic needs.
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Business leaders are relying on data scientists to improve how they acquire data, determine its value, analyze it and build algorithms for the ultimate purpose of improving how they do business. With the advent of new ways to store, analyze and retrieve data, the idea data scientist needs skills in programming languages, distributed computing systems and open-source tools.
Skills in natural language processing help data scientists transform these unstructured data into structured data to allow for quantitative analysis. Machine learning skills help businesses identify generalized patterns in the data (training data) that allows for classification of future data (target data). Data scientists, to be successful, need a good understanding of the business, including its people, products and services, and how they all work together. This skill is closely linked to business acumen, as data scientists need to be able to convey complex quantitative, computational findings into terms that business executives, managers, and front line employees can understand. To be of real, long-term value to business, Big Data needs to be about understanding the causal links among the variables. To solve business problems, data scientists need a variety of skills, including quantitative, computer, business acumen and communication. This keyboard can be used in industrial environments, hospitals, libraries, marine and boating applications, or anywhere where dust and liquids are present.
Statistical skills can help businesses summarize their large data sets into smaller pieces of meaningful information. This pattern recognition helps drive recommendation engines that present customers with information that is relevant to them.
This knowledge of how business works helps data scientists direct their energies to data that are the most valuable to the business.

Data scientists often need to use visualization tools to help translate quantitative findings into images that are easily consumable by the masses.
Unfortunately, people introduce bias, intentional and unintentional, that weaken the quality of the data.
Hypothesis testing helps shed light on identifying the reasons why variables are related to each other and the underlying processes that drive the observed relationships. Hypothesis testing helps improve analytical models through trial and error to identify the causal variables and helps you generalize your findings across different situations.
The current review, however, uncovered an overlooked skill needed by data scientists: the scientific method.
The mini size is perfect for school and travel, fitting easily in a briefcase, backpack, or notebook carrying case. Predictive modeling skills help businesses create algorithms, both automatically and manually, to improve business processes.
Finally, data management skills help data scientists develop and integrate different data systems so businesses can utilize all their data in an integrated fashion.
With good communication skills and the use of tools to visualize the data, data scientists are able to provide the insights that business leaders need to operationalize changes to their current business processes.
Davenport only in  2008, it reached the status of "sexiest job of the 21st century" by 2012. As a whole,these quantitative skills allow businesses to apply mathematical rigor to to their large, quickly expanding data sets to help make sense of them.

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communication and quantitative skills test (cost)

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