Table of Content
The differences between data science and data analytics
The similarities between data science and data analytics
Choosing between a data science and data analytics career
Which data career is right for you?
How an MDA at the University of Niagara Falls Canada will help you succeed
You’ve always loved numbers, and a job exploring the wonderful world of data is just the challenge your brain’s been looking for. The only problem is that you’re caught between a career in data science versus one in data analytics. To help you choose, let’s compare these two rewarding fields.
What is data science?
We’ll start by explaining a bit more about what data science is. Data science draws on various disciplines, such as computer science, predictive analytics, statistics, machine learning, and even data analytics, to extract insights from large amounts of structured and unstructured data. A data scientist aims to ask questions and establish solutions to problems that an organization might not have considered.
What is data analytics
Now that you have a better understanding of data science, what’s data analytics? Data analytics involves collecting, cleaning, and performing statistical analysis on existing datasets to address an organization’s current issues. Data analysts also focus on effectively presenting their findings.
The differences between data science and data analytics
Although the terms are often used interchangeably, there are key differences between data science and data analytics. Here are the two biggest ones to consider:
Scope
Data science is a broad term for a group of fields that examine large datasets. Data analytics is a more concentrated branch of data science that’s often part of a larger process.
Focus
Rather than looking for specific insights, data science is more about finding out which questions should be asked. The goal of data analysis is to find answers to questions being asked right now.
The similarities between data science and data analytics
Despite their differences, data science and data analytics still share similarities. Some of them include:
Helping with decision-making
Both data scientists and data analysts help companies make informed, data-driven decisions.
Technical skills and knowledge
Both professions require experience with statistics, Excel, data visualization, modelling proficiency, and programming languages including R, Python, Tableau, and SQL.
Communication skills
Because they collaborate with colleagues in different departments, some without a technical background, both data analysts and data scientists must communicate their findings in clear, digestible ways.
Choosing between a data science and data analytics career
Still no victor in the competition of data scientist versus data analyst? Luckily, we’ve broken down their specific attributes a little further.
The role and skills of a data scientist
A data scientist’s primary responsibilities include designing data modelling processes as well as creating algorithms and predictive models to extricate required information. To do this, they need to know machine learning, software development, data mining and warehousing, and various programming languages.
The role and skills of a data analyst
The main duties and responsibilities of a data analyst usually include interpreting datasets using statistical tools, maintaining data systems and databases, and putting together reports that communicate trends, patterns, and predictions based on their findings. Alongside data analysis, their primary skills are database management and reporting, data modelling, data visualization, and understanding different programming languages.
Which data career is right for you?
Before you make your final choice, there are some other factors to consider:
1. Your educational background
Most entry-level data analytics positions require at least a bachelor’s degree in science, mathematics, or technology-related areas, as they provide a solid foundation for collecting, processing, and analyzing data as well as applying it in a business context. Similarly, future data scientists usually start their educational paths with a background in data science, computer science, statistics, or mathematics, all of which provide essential skills for handling and analyzing complex data sets. Professionals in both fields sometimes choose to expand their capabilities through additional academic credentials, such as a Master of Data Analytics (MDA) or another relevant degree.
2. Your interests
If you thrive on building algorithms and working with challenging mathematical models, your calling may be in data science. If you’re excited by the idea of helping organizations solve urgent problems and improve their operations, then data analytics might be worth pursuing.
How an MDA at the University of Niagara Falls Canada will help you succeed
With a curriculum that includes diverse, comprehensive courses and practical learning opportunities, the MDA program at the University of Niagara Falls Canada offers in-depth training in the data lifecycle that will equip you for either path you choose. And, because each career journey is unique, you can further customize your education by picking one of three program streams in Marketing Analytics, Operations Analytics, or General Analytics. Like what you’ve heard so far? Feel free to connect with a student advisor, check out our awards and scholarships, or start your application. We look forward to taking this next step with you!
Frequently asked questions
Should I learn data science or data analytics first?
Starting with data analytics will give you a firm foundation in handling data and performing statistical analysis, allowing you to advance to the more sophisticated tools and techniques of data science.
Which pays more, a career in data science or data analytics?
In Canada, the average salary of a data scientist is $135,260. The average salary of a data analyst is $91,948.
*Source: talent.com
Can a data analyst become a data scientist?
Yes. Many data scientists start their careers as data analysts and make the change once they’ve expanded their skills in areas like statistics, machine learning, and programming languages like Python and R, all of which are included in our MDA curriculum.
How much harder is data science than data analytics?
While both fields require a strong technical background, data science is considered more difficult than data analytics, as it requires proficiency in technologically advanced areas such as machine learning and predictive modelling.
Does data analytics require coding?
For data analysts, coding is a valuable skill that allows them to properly manage, clean, and manipulate data, perform complicated analyses, create data visualizations, and automate certain tasks.
