Essentials to Move Forward with Career Trajectory of Data Scientist

DataScience
Jun 16, 2015
Essentials to Move Forward with Career Trajectory of Data Scientist

Data Science is a growing industry and various organizations are looking to recruit experienced individuals from around the world. Data science is an emerging field and there are many branches of it, producing different job titles. Some of the job titles include Data Engineers, Data Scientists, Statisticians and Data Architects. However, many of the job seekers are not aware, what is the job description for any title and the relevant organisations or employers to apply for these jobs. They don’t know much about data science because it is a relatively new area, and they are also not aware of the different branches and diverse jobs that are related to each branch. Especially those who are new to this field, they assume that these job categories are similar, with different titles. Hence it’s important to investigate the job roles for the Data Engineer, Data Scientist and Statistician before you pursue any job opportunity.

Take Help from Discussion Forums

One of the ways is to look up discussions about the jobs and see what people are saying. Any more questions about these job areas can be posted in discussion forums. You can hear what people are saying in forums and decide from listening to their experiences if the job is for you.

Dont Confuse with Data Science & Data Engineering

Data Science is a totally evolving career, not only for the youngsters but also for those who are in the middle of their career path. Part of the job for data scientist involves asking questions, in order to gather information so it has an interviewing component to it. The answers to these questions will be found in knowledge of statistics, machine learning, and data mining tools. This knowledge includes CS fundamentals and programming, languages and database technologies such as Python and MySQL.

Analysis needs to be aimed to an audience, with visualised data or results, charts and graphs, and visualisation tools such as D3. These need to be presented in speech or writing. A Ph.D. degree is prerequisite to the Insight Data Science Program, as it symbolises five intense years in graduate training asking questions related to data, performing data analysis, creating statistical or mathematical models, and in the presentation of results. These are all requirements for the role of data scientist.

Data Engineer Assists Data Scientists

The data engineer collects and processes data as well as assists data scientists in effective job performance! Data engineering includes Data Infrastructure or Data Architecture. A good data engineer must also understand databases and best engineering practices, including software engineering. Skills include handling and logging errors, monitoring the system, building human-fault-tolerant pipelines, scaling up, integration methods, database administration, data cleaning and determining outcomes.

A data scientist must have years of software engineering experience, even without a Ph.D. degree. Remember that candidates with passion and potential are also highly valued for the new job openings.

Synchronicity

Synchronicity is a science both in psychology and in other sciences where there is a common message, or similarity between things. For example, having knowledge in one field may help you to understand another field or skill. Synchronicity involves a message in communication, which is understood by the links made between cues in the environment. Each cue does not mean anything until the messages are put together, and then the signs together form a message. In this way we can make discoveries which inform us about a new field which we may not have any knowledge of in our daily lives.

Use of Synchronicity to Learn New Things

We can use synchronicity to learn about fields which are new to us. An example of how it may relate to Data Science is when you compare two different careers or roles, and then you find the links between them. There may be similarity between the two roles. For example, a data scientist might use the Hadoop ecosystem for answers to data questions, and a data engineer might be using programming algorithms. These tracks are separate, but some companies prefer candidates who are knowledgeable of both data science and data engineering. With defined roles, there is ease in moving between two role descriptions. You can find linking information, similar technologies and methods in both of these roles. By making links between technologies you will make groundbreaking discoveries which can lead to advancements in both fields. This is how synchronicity as a science can be used in multiple fields. It also informs the candidates who, by linking skills, may make themselves better prepared and feeling that they have ability in both fields.

Bottom Line

So this is the type of learning which may be required by a potential candidate for this type of work. Also the candidate will have to seek out courses and learning methods which will assist him/her in this pursuit. An awareness of the cross section of technological skills and methods will assist the candidate in moving forward with a career trajectory.

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