Yes, the data science field is one of the fastest-growing in technology, with more than 2.7 million new jobs in data forecast to be created.
This growth also looks set to continue when you factor in the increased importance of data skills. According to the 2020 Digital Skills Survey, 89 percent of professionals believe that improved data skills will improve success at their organization, and 78 percent believe that AI is the technology that will have the greatest impact in the coming years.
In 2020, Glassdoor reported the average Data Scientist salary is $84,000 a year in Canada and over $113,000 in the U.S.
Even if you have no job experience in data, it’s still possible to become a Data Scientist. But before you begin exploring the specializations within the field of data science, you’ll need to develop a broad base of knowledge in a related field. That could be mathematics, engineering, statistics, data analysis, programming, or IT – some Data Scientists have even started out in finance and baseball scouting.
Whatever field you begin with, it should include the fundamentals: Python, SQL, and Excel. These skills will be essential to working with and organizing raw data. To move from a data science-adjacent field into data science itself, you’ll need to acquire a specific set of skills, and the most effective way to do this is by enrolling in a data science course or bootcamp with a structured learning program. A data science education ensures that you’ll cover all the basics – without getting lost in the weeds of irrelevant or out-of-date areas of study.
Expect to learn data science essentials like data collection and analysis, data modelling, data visualization and the data visualization tools most commonly used by Data Scientists. By the end of your data science course, you should know how to use Python, R, and Hadoop, and how to build models that analyze behaviour, predict unknowns, and be able to repackage data into user-friendly forms.
With skills training and a strong portfolio, you can begin working on establishing your public profile as a Data Scientist. A well-executed project that you pull off on your own is a great way to do just that. Pick a subject you’re really interested in, ask a question about it, and try to answer that question with data. Then, publish your work on GitHub to present your process, work, and findings to highlight your technical skills and creativity in a compelling narrative.
IDSA courses are designed to prepare students for real commercial work in the area of data science, AI and big data.
IDSA trainers are actively working in the industry and will teach you how to practice data science and advanced analytics.
The member network offers support and guidance from mentors and direct connections to the industry. You will meet employers in-person at IDSA events.