So, You Want to Become a Data Scientist

Bruce Dorland

March 21, 2020

If you’re contemplating a move into the role of data scientist, congratulations. Harvard Business Review calls it “the sexiest job of the 21st century”. The job market reports a data science skills shortage. Indeed ranked Data Scientist #3 on their list of Best Jobs for 2020.

Data Science is in demand

Here’s a mind-blowing stat: 90% of all the data in the world was generated in the last two years. 

Digital transformations have taken hold in every industry, where every transaction creates data.

If you’re already working in this field in some capacity, you’ve seen the volumes of data being generated – and you know the critical function of analyzing that data for insight. Data scientists cleanse data, identify patterns, unearth trends, then map them to business goals. 

Not only is demand for data scientists up, their salaries are up as well. The average salary for a data scientist in Canada starts around $80,000 a year for a junior position, and can pay well into six figures, depending on the scope of responsibility.  

What does a data scientist do?

Data is arguably the most valuable asset of any company, but by itself is useless unless it’s interpreted. It’s the data scientists’ job to bring structure and creativity to large quantities of data and extract meaning from the raw numbers. Findings are applied to business functions to create opportunities for growth and improve services, as well as in critical societal realms to support improvements in healthcare, research, and the environment. The areas of opportunity are endless.

Data scientists generally have a foundation in STEM with combined skills in computer science, modeling, stats, and analytics. Artificial intelligence, machine learning, blockchain, and big data, are critical to finding useful patterns buried in the data.

Here are the 4 main skill sets a data scientist needs.

Math &
Programming & DatabasesVisualization &
Soft’ Skills
Machine learning
Statistical modeling
Logistic regression
Decision trees

Computer scienceScripting (Python)
Statistical computing (R)
Databases (SQL)
Cloud data services
Visualization tools 
Translate data insights into meaning
Mapping / Munging
Communicate the ‘why’ & clearly
Hacker approach
Curious about data
Business savvy

Make the transition to Data Science

Data Scientist is both an academic credential and a job title. If you don’t have a degree, you can still achieve your goal of becoming a data scientist while at work. Research job descriptions for data scientist to understand the necessary functions. Speak to your manager at work and tell them you’re taking steps to move in the direction of a data scientist role. Ask to map an appropriate career path that serves both you and the needs of your company.

Here’s a sample steps and actions you can take. Before starting them though, consult with another data scientist or your manager to get their thoughts! Validate that the actions you’re taking are going to get you where you want to be.

1. Start with a bachelor’s degree in IT, computer science, statistics, or other STEM field. If you don’t have one already, make sure this is right for you before simply ‘signing up’. Degrees are expensive to many (especially if you just completed one!), so it’s not for everyone – but there’s other steps to get there, so not to worry.

2. Gain data experience as a data analyst (broad title, so make sure the job entails what you’re looking for). Specialties eg: research, security, e-commerce, healthcare, environmental, marketing, etc.

3. Take online courses (MOOC, Udemy) to up your skills.

4. Participate in Kaggle competitions – the place for data science projects. Do projects that are of personal interest and fun!

5. Take on a project at work where there is no raw data. Figure out how to get it, complete your analysis, offer new insights. You could volunteer to do this with your current employer as a side project.

6. Join a data science meetup and learn from others7. Work on toy projects, take on a side hustle, leverage data for good.

Commit to this journey and you will benefit from a highly skilled, creative and dynamic career. Be prepared to validate and iterate as you move through each step to build your data science skills.

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