How to become a data analyst

AVERAGE SALARY
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Average salary in Canadian dollars
$67,136
JOB PROSPECTS
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Job Bank: More job openings (18k+) are expected than can be filled by 2028
18k+ new jobs green-arrow-up
JOB BANK
800+ jobs on LinkedIn
COMMUNITIES
Kaggle, IBM Data Science Community, Open Data Science , Analytics Vidhya, The Business and Data Analyst Forum - LinkedIn, Data Science Society , Data Analyst Group - Facebook, KDnuggets , Cross Validated (Stack Exchange) ,

There’s never been a better time to enter the field of data analytics. By some estimates, roughly 2.5 quintillion (that’s 2,500,000,000,000,000,000) bytes of data are being generated every single day and by 2025, there will be 175 zettabytes (that’s 1,000,000,000 terabytes) in the global datasphere. The explosion of modern data generation has likewise created a boom in long-term career opportunities in a challenging and well-compensated field.

Data analytics is the science of analyzing data and extracting useful conclusions to inform an organization’s strategies and operations. Organizations around the globe have embraced data-driven decision-making to more effectively discover opportunities, solve problems and mitigate risk. 

But before we get into more detail, it might be useful to draw a distinction between two thriving and often-confused careers in the industry known broadly as “Big Data” or “data science.”

What’s the difference between data analytics and data science?

If you’ve ever searched for data analytics job postings, you’ve likely also received results for another interconnected field: data science. While the two share many similarities, they differ in scope, skills, responsibilities and goals. Understanding these differences is vital in order to determine which is the best career path for you.

Both data science and data analytics use data to solve problems. But data analytics is a branch of data science, and it has a narrower focus.

Data analytics uses processed, historical data to identify trends, generate actionable insights and answer questions to drive better business strategies.

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.

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 coding expertise.

Data analytics, while still a complex and challenging field with plenty of career opportunities, doesn’t require quite the same depth of mathematical and programming expertise as data science. Consequently, data analysts can find it easier to get into the field, especially if they bring additional work experience with them, or are using data analytics skills to enhance existing business knowledge, for example.