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  • Subject area(s): Marketing
  • Price: Free download
  • Published on: 14th September 2019
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  • Number of pages: 2

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Data science is a rapidly growing profession that is generally speaking, dreadfully misunderstood. This dissertation titled, “Defining Data Science and Data Scientist” moves to explain exactly what those two are. This dissertation firstly defines what these are in where data science is the methodology in where data scientists take portions of non-specified statistics. This also covers how persons generally will define what a data science is and how it performs technical analysis with robot-like precision.  The dissertation will also cover related skills and topics that pertain to Data science, it will also cover interviews from professionals as to see what their beliefs were in regard to data science and data scientists.

There are several important topics that are covered by this dissertation. To begin with, this dissertation discusses five key areas in where data scientists must have skills. This list includes, Data Analysis Functions, which are the capabilities that are types of math and analytical methods and modeling skills. Another is programming tools and machine learning techniques. These two covers the ability of the scientists to write code to customize a tool, and to be able to renew data and build machine learning proficiency. Then there is Interdisciplinary knowledge and critical abilities. These two skills represent the functions that must exist across academic disciplines and the softer skills that are required for data results to be communicated. An interesting item within this dissertation is that there isn't really any documentation for tools like, Tableau, Tensorflow and radiant in academic literature. It is simply possible that colleges and universities have yet to properly incorporate training of these tools into their curriculums. Currently Big data is defined as being techniques for managing and analyzing results that let several industries consider the results in their decision-making process.

Another big topic that is covered in this dissertation is machine learning. Machine learning is explained as the ability for a machine to discover and learn new things. The interviewees had responded that the machine learning has the capability to use decision trees, and logistics regression. They also stated that the leading techniques are Apache and Spark. The dissertation also covers strategic decisions. The interviewees had responded to strategic decisions being made from data results. This led to many of them answering that they “did not know”, or “not much”. This leads us to think that a good portion of companies and organizations are novice to data science. These interviewees were also requested to answer multiple choice questions. They had been asked what sort of questions they had answered based on data. Typically, the responses were about risk, marketing, promotion, and fraud prevention.

Here are five data science jobs that I found online:

1. Data Scientist

a. This is currently one of the most popular jobs currently going on. People qualified for these jobs are rare and they are also required to be skilled in a wide range of things. They must know SQL, SAS, Spark and so forth. Data Scientists must also be able to handle raw data and analyze it with statistics.

b. Source: https://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html

2. Business Analyst

a. This role requires better knowledge of the business practice and needs to be able to link data insights to business goals. They must also be able to communicate these goals to the rest of the organization. Business analysts are the bridge between IT and business.

b. Source: https://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html

3. Data Architect

a. This person creates the data management blueprints so that the systems in order to integrate, centralize and protect data sources. Knowledge of Hive, Pig, and Spark is also required.

b. Source: https://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html

4. Data Engineer

a. This job is in where the person builds, tests and maintains the architecture of the database. They must have knowledge of SQL, Hive, R, C++ and Python. A data engineer should have a little of skill in everything.

b. Source: https://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html

5. Database Administrator

a. This job entails being sure that the database is available to all persons who are privy to it. They must also be sure that backups are made and that the system is secure. They must also keep track of the technologies used in order to be able to support them. This role must also know SQL, XML, C# and Python.

b. Source: https://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html

Conclusion:

What I have learned is that a data scientist is a career that is currently one of the most sought in the job marketplace. Data scientists use business skills in combination with technical skills to study and learn from Big Data. The discoveries that are made are to help improve a company's business analytics practices I also found that basic math and excel seemed to be the most popular themes during the multiple-choice surveys in where the participants were stating their opinion on which skills, they thought a data scientist should have. Personally, I would have thought that SQL would have been the preferred programming skill. I also found it interesting that one of the participants had suggested that data scientists should not “...recreate the wheel by writing code, and instead fetch the code from a community, then drag and drop it into your tool...” this I presume is to save time.

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