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all about data scientist jobs.

If you like the challenge of finding meaning in the seemingly incomprehensible, you might find a career as a data scientist rewarding. In this career, you will be tasked with interpreting large amounts of data from a wide range of sources, presenting your findings so that action can be taken.

what do data scientist jobs entail?

As a data analyst, you will be responsible for interpreting large volumes of data from a variety of sources. You will use many tools in your efforts, such as algorithmic, data mining, artificial intelligence, statistical, and machine learning tools. Once you have the desired information to show for your work, you will then be required to present that information in a clear and understandable way.

what is a typical work schedule for data scientists

While any given data scientist role can be different, it is uncommon for this position to involve working outside of the typical Monday-to-Friday workweek. You will likely be working 37 to 40 hours a week, though larger projects often require longer hours in order to meet any special deadlines. Depending on the specifics of your role, some travel may be required, which can also affect your typical working schedule.

what are the pros of data scientist jobs?

• Excellent future prospects: New data sources are emerging every day, from smart home devices to wearable technology. The myriad of new ways available to collect data has caused the field of data science to grow tremendously in recent years, and it looks set to keep growing for the foreseeable future. As more techniques and more data come to be, the demand for data scientists will only increase.
• Challenging and rewarding: Data science involves a wide range of skills used to their full potential. In short, it's not a role for someone who likes to coast through the day. If you like solving puzzles and challenging yourself, this career could be an extremely good fit for you.
• Versatility of industry: Data science is not limited to any single field. And, as more and more industries start to see the benefits of data science, more positions will open up.

what are the cons of data scientist jobs?

• Oftenly changing landscape: Data science in any meaningful form is still in its infancy, and the breakneck pace at which technology changes means that it can be hard to keep up. However, keeping up is something you must do as a data scientist.
• Generalization: While data scientists may find a wide variety of industries open to them, they will never really get to specialize in any of these industries because data science is primarily about the data. The disciplines used are largely industry-agnostic, so if you like to get to grips with a specialty, you may find this work frustrating at times.
• Controversial nature: The discussions around our privacy in the modern technological world are heated and ongoing. As a data scientist, you will essentially be at the centre of these discussions, and that can be difficult for some people.

what are common data scientist interview questions?

When you attend an interview for a data scientist position, you may be asked some questions that are specific to the job you are applying for. Nevertheless, you will very likely be asked some or all of the following regardless of who your potential employer is or what field they are in:

• What are your strengths and weaknesses? When asked this, the strengths side of the question should be straightforward—just remember to focus on strengths that are relevant to the job. With weaknesses, it is important to give your employer a clear sense that you are aware of your weaknesses but also that you have a plan to deal with them.
• Why did you choose us? Employers are unlikely to waste time asking you why you want to become a data scientist since you will have already had many years of education to get to this stage. Instead, they might ask you why you chose to apply for a job with their organization. Don't just say nice things about the company, show that you've done your homework, and explain how you feel you will fit in.

data scientist resume and cover letter requirements

In your resume when applying for a data scientist role, you should include the following where applicable;

• Name and contact information.
• Brief summary of experience/skills.
• Employment history.
• Education/qualifications.
• Awards/certifications.
• Examples of accomplishments.
• Social media accounts (if work-related).
• Work-related skills.

When writing your cover letter, remember that this is often the first impression your potential employer will have of you. Write professionally, and be sure to include the following elements:

• Firstly, expand on your education and experience in the field.
• Secondly, talk about why you want the job and why you want to work with this organization specifically.
• Finally, outline how joining the company will benefit them as well as you.

what is the job outlook for data scientists?

The outlook for data scientists is very promising, as the demand for data scientists is on a decidedly upward trajectory. However, one caveat to this is that a data scientist who wants to remain in demand will need to keep themselves well-versed in all the latest developments from this field, and it is a constantly moving target. As long as you take the time to research and study new techniques, methods, laws, and other relevant aspects of the job, you will likely find the outlook for data scientists to be very promising indeed.

what are the different types of data scientist jobs?

• Data scientist: The type for which the role is named, a data scientist will be tasked with modelling processes and creating algorithms and predictive models for the purposes of analyzing data.
• Data analyst: A data analyst manipulates large sets of data for the purposes of identifying trends and forming meaningful conclusions from those trends. These conclusions then go on to inform things like business decisions.
• Data engineer: Data engineers can be thought of as being responsible for preparing the data. They will clean, aggregate, and organize data from different sources before handing it off for analysis.
• Data architect: As a data architect, you would be responsible for creating and managing the data architecture of your organization, finding the most efficient ways to handle the data, and determining what data is worth keeping.

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what is the average salary for a data scientist?

The average salary of a data scientist in Canada is around $95,000, with an hourly rate of $48 per hour. This is largely in the middle of the salary range for this role, with entry-level positions typically commanding a $77,000 salary and more experienced data scientists receiving as much as $136,000 per year. The importance of data analysis in the organization you work for will also affect your salary. An organization that does not expect many benefits will be reluctant to pay more, but this can be a great opportunity to prove your worth.

average data scientist salaries in canada

• annual: $95,000
• monthly: $7,600
• weekly: $1,900
• hourly: $48

Top paying areas for data scientists in Canada include: Ontario, Alberta, and British Columbia.

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what are the duties and responsibilities of data scientists?

The specific responsibilities you will have as a data scientist will depend on what kind of data scientist you choose to become, but there are some duties that you should expect to have regardless.

• Work closely with your organization: When you boil it down, the core role of a data scientist is to facilitate the meaningful use of data to better the organization. That being said, data has no inherent meaning—that's why there are data scientists—but in order to find the relevant trends and patterns in the data, you need to know what your company needs.
• Use a variety of tools: The amount of data that a typical data scientist will deal with these days would be overwhelming without the right tools. You will be expected to make full use of things like machine learning, data mining models, statistical analysis applications, and even build custom algorithms.
• Assess data sources: While some organizations are often depicted as data-vacuums looking to hoover up any data they can find, the reality is that not all data is useful. One of your roles as a data scientist will be to assess how effective your data sources are, as well as the techniques used to gather them.
• Communicate findings: Perhaps the most important aspect of your role as a data scientist after the data itself is the proper communication of your findings to the relevant parties. You will be hired to do what executives and department heads cannot, but they still need to understand your findings before they can implement changes based on them.
• Stay up to date: As mentioned above, data science is a rapidly evolving field, and you will need to stay up to date with the latest news, techniques, and software in order to be fully effective as the landscape continues to shift.

where do data scientists typically work?

For the most part, your time as a data scientist will be spent sitting at a desk working on a computer. This is an almost-entirely computer-based profession, so you will need to be comfortable with that. The exception to this is those times where you will be required to present your conclusions to colleagues or bosses. This may just require a report, but it could also mean giving a presentation to several members of staff.

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what skills and competencies do data scientists need?

As with your duties and responsibilities, the skills and competencies you need will vary slightly with the specific type of data scientist you choose to become. That being said, you should expect to need to be proficient in all of the following:

• Problem-solving: A good deal of a data scientist’s work will involve having to solve problems, be it issues with data collection, figuring out how best to analyze data, or working out how to interpret your findings in a way that is meaningful to your organization.
• Communication: Being able to communicate your findings to the relevant parties with your organization is a critical skill as, without it, your work could be rendered essentially meaningless. You should be able to articulate what your analysis has shown, how certain changes can improve things, and why those changes would work.
• Time management: Data scientists often work to strict deadlines. This is not a problem for someone who can effectively manage their time but could quickly become an issue for someone who struggles.
• Pressure handling: Whether it be a deadline that is fast approaching or an impending presentation in front of the organization's executives, being a data scientist can carry a lot of pressure.
• Teamwork: In addition to being able to properly communicate your findings to your coworkers, you will also need to be able to work with them in your pursuit of data collection, as well as determine what data and trends will be useful.

what training and certifications do data scientists need?

To break into a career as a data analyst, you will need to have at least a bachelor's degree in a relevant subject, though a master's or doctorate will help give you an edge over a lot of the competition. Computer science and data science are, of course, two of the most related subjects for this occupation. That being said, there are other subjects that can be turned to a career as a data scientist, such as:

• Computer Science
• Data Science
• Engineering
• Mathematics
• Physics
• Statistics

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