Data Analytics

Frequently Asked Questions (FAQs)

• What is the difference between data analytics and data science?

  Data analytics is a specialty within data science, which is a general term that encompasses many different types of data roles.

  Data science takes raw data from various unconnected sources and creates algorithms, predictive modeling processes, and other custom analyses to shape raw data into insights. Data analytics uses processed, historical data to identify trends, generate actionable insights and answer questions to drive better business results.

  In other words, data analytics makes sense of existing information. Data science finds innovative ways to capture and analyze the data used by analysts.

  Data science is a more technical field, and typically requires more advanced education and programming expertise. Data analytics, on the other hand, may require more business training and context in order to translate insights – about consumer behaviour, seasonal demand trends, supply chain issues or product defects, for example – into strategies that help businesses and organizations achieve their objectives.

• Do I need a degree to get a job in data analytics?

  While the typical level of education needed for an entry-level data analytics role is a bachelor’s degree in computer science, IT, statistics, data science, or mathematics, additional education helps to be competitive in today’s job market.

  Having real-world project experience, and a portfolio of work that demonstrates a history of trial and error, especially in combination with the right training, can increase a candidate’s chances of securing a good job in the field.

  There are a number of coding bootcamps and college certificate and diploma programs that offer intensive, accelerated training that can help build necessary foundational and practical experience in the field while providing job support.

• What are common career paths within the data analytics field?

  Armed with data analytics skills, there are numerous career paths available to you across virtually every industry, from technology, media, life sciences and retail to financial services, government and academia.

  For example, business analysts extract business-specific data and use it to streamline processes or optimize organizational structures, how budgets are allocated and marketing dollars spent. Financial analysts leverage data to reduce financial risk for banks, or uncover potential revenue streams for startups.

  Healthcare data analysts interpret historical data in order to facilitate medical research and improve outcomes for patients, while data journalists use tools like Python and Tableau to communicate through charts, graphs and infographics that reveal important and perhaps counterintuitive insights within news stories.

  Marketing analysts, meanwhile, might use sales data to determine which products to prioritize across email, social media and featured positions on an eCommerce website.

  Finally, data analysts can transition into data science roles by adding advanced math and programming skills to their toolkit while gaining a deeper understanding of machine learning. In fact, many data scientists begin their career as data analysts.  Data scientists, conversely, might grow into more senior managerial roles, at which point managing people and business strategy requires them to focus more on interpreting data than collecting it.

• What is the average salary for a data analyst?

  Data analytics can be a high-paying career, but average salaries depend on your level of experience, the size and location of the company you’re working for, and the role you’re in.

  According to Talent.com, data analysts in Canada earn an average salary of $89,699. Entry-level salaries start at $65,322 and can reach up to $140,299 for more senior level positions. Related roles fetch similar salaries: data managers earn an average of $136,270; business analysts make on average $130,617; and business systems analysts pull in average salaries of $110,000.

  Those interested in transitioning into data science and becoming well-versed in machine learning engineering can expect even higher salaries, earning on average $150,186.

• How do I become a data analyst with no experience?

  While it may be challenging to land a full-time job without any prior experience, volunteering, interning, or working on freelance projects is a great way to gain that practical, hands-on experience.

  The first step to building a profile ahead of applying for junior data analytics roles is to develop the right skills. Online courses and coding bootcamps focused on programming languages such as Python or R, can be an excellent start for newcomers to the data analytics field.

  The next step is to create a portfolio that showcases your data analytics projects, while also demonstrating problem solving and data analysis capabilities.

  Earning globally-recognized certifications in data analytics that show proficiency in data analytics tools and methodologies, as well as relevant programming languages, are a way to get the experience you need to land a job.

  Finally, joining a community and networking is another way to get your foot in the door. You’ll be able to connect with other data analytics professionals in the field, and get advice from those with more experience.