From uncovering insights in vast datasets to building predictive models that shape business strategies, data professionals play a crucial role in today’s data-dominated world.
But what does the field of data analytics involve, exactly? And where can it lead you?
In this post, we explore the various career paths available in data analytics, including roles such as data analyst, data scientist, and business intelligence analyst. Read on to discover typical tasks, required skills, salary ranges, and future demand.
TABLE OF CONTENTS
- Overview of Data Analytics Careers
- Different Career Paths in Data Analytics
- Industries That Hire Data Analytics Professionals
- Skills Required for Data Analytics Careers
- Education and Certifications for Data Analytics
- Salary Ranges in Data Analytics
- Future Outlook for Data Analytics Careers
- Transitioning to a Career in Data Analytics
OVERVIEW OF DATA ANALYTICS CAREERS
Data analytics is about finding hidden value in a sea of numbers. Careers in this field involve taking large amounts of information, cleaning it up, and looking for patterns that can help organizations understand what’s happening and make better decisions.
Imagine a store owner who wants to know what products are selling best. They could use raw data to look at past sales numbers, figure out which items are popular, and decide what to stock more of.
Professionals in this field are prized for their ability to:
- Improve decision making: By providing actionable insights, data analytics helps businesses make informed choices that drive growth and profitability.
- Optimize operations: Identifying inefficiencies and bottlenecks leads to streamlined processes and cost reductions.
- Enhance customer experiences: Understanding customer behaviour and preferences helps businesses tailor products and services to meet customer needs.
- Identify new opportunities: Discovering hidden patterns and trends can uncover new market segments and business opportunities.
- Manage risks: Analyzing data can help businesses identify potential risks and develop strategies to mitigate them.
Data analytics can tell a valuable story
DIFFERENT CAREER PATHS IN DATA ANALYTICS
This field offers a variety of career paths. For instance, you could become a:
Data Analyst
This is a common entry-level role. Data analysts comb datasets to find trends and patterns. They use data mining and other techniques to answer specific business questions within departments like marketing, sales, or finance.
A data analyst’s typical tasks include:
- Using tools like SQL (structured query language) to query databases and gather relevant data for analysis
- Cleaning and preprocessing data, handling missing values, removing duplicates, and ensuring accuracy and consistency
- Identifying trends, patterns, and insights that answer specific business questions
- Creating data visualizations (charts, graphs, dashboards) to communicate findings
- Responding to one-off requests for analysis to address immediate business needs
Business Intelligence Analyst
BI analysts focus on creating and maintaining dashboards and reports that provide ongoing business monitoring. Their role is more strategic, providing high-level insights to guide business decisions.
Key tasks include:
- Collecting and integrating data from various sources to create a comprehensive dataset for analysis
- Building and maintaining interactive dashboards that provide real-time visibility into key business metrics
- Defining and tracking key performance indicators that align with business goals
- Automating regular reporting processes to ensure that stakeholders receive timely and accurate information
- Analyzing long-term trends and providing strategic insights to guide business decisions
Data Scientist
Data scientists focus on advanced analytics, predictive modelling, and the development of machine learning algorithms. Their role is highly technical, dealing with large and complex datasets and creating models to predict future trends.
A typical data scientist job description includes tasks like:
- Gathering and preparing large datasets
- Performing in-depth analysis to discover patterns, trends, and relationships within the data
- Developing predictive models, algorithms, and machine learning applications to solve complex problems and forecast future trends
- Evaluating model performance to ensure robustness and reliability
- Working with engineers to deploy models into production, ensuring they are scalable and maintainable
A career in data science involves developing predictive models
Data Engineer
Data engineers are responsible for building and maintaining the infrastructure that allows data to be collected, stored, and accessed efficiently. They ensure that data pipelines are robust, scalable, and capable of handling large volumes of data.
Typical tasks include:
- Developing and maintaining data pipelines to automate the flow of data from source systems to data storage and processing systems
- Designing data warehouses and data lakes that store large volumes of structured and unstructured data
- Managing extract, transform, load (ETL) processes that move and transform data from various sources into a useable format
- Ensuring data quality and integrity
- Implementing data security and privacy measures
INDUSTRIES THAT HIRE DATA ANALYTICS PROFESSIONALS
Any organization that collects and stores data can benefit from using data analytics. Here’s a brief overview of how it’s used in different industries:
Finance: In the finance industry, data analysts help manage risks, analyze market trends, and optimize investment strategies. They work with financial data to identify patterns, predict market movements, and ensure regulatory compliance.
Healthcare: Data analysts in healthcare analyze patient data, medical records, and treatment outcomes to manage hospital resources and support public health initiatives. They also help identify trends in diseases and treatment effectiveness.
Retail: In retail, data analysts study sales data, customer behaviour, and inventory levels to optimize pricing strategies, improve marketing campaigns, and enhance the overall shopping experience. They help retailers understand what products are popular and when to restock.
Marketing: Marketing data analysts focus on customer data, campaign performance, and market research to create targeted marketing strategies, measure campaign effectiveness, and optimize advertising budgets. They help businesses reach the right audience with the right message.
Manufacturing: In manufacturing, data analysts monitor production data, supply chain efficiency, and quality control metrics to improve processes, reduce waste, and increase productivity. They play a key role in ensuring that operations run smoothly and cost effectively.
Energy: Data analysts in the energy sector analyze consumption data, monitor grid performance, and forecast demand to optimize energy distribution and manage resources. They contribute to efficient and sustainable energy management.
The energy sector is one of many that relies on data analytics
SKILLS REQUIRED FOR DATA ANALYTICS CAREERS
A career in data analytics requires a blend of technical expertise, analytical thinking, and communication abilities.
Technical Skills
Being comfortable with SQL is probably the most important thing. You’ll need it to query databases, extract data, and perform basic operations on datasets. Advanced knowledge of Excel is also essential.
Other useful technical skills include:
- Programming languages like Python or R
- Data visualization tools like Tableau or Power BI
- Statistical techniques like regression and hypothesis testing
Soft Skills
Problem solving and critical thinking are key. You need to be able to approach issues methodically, break them down into smaller tasks, and apply analytical techniques to solve them.
Communication skills are also important. You need to be adept at explaining technical concepts to non-technical people through clear reports and presentations. Remember: This industry is about telling stories with data, making complex insights understandable and actionable.
EDUCATION AND CERTIFICATIONS FOR DATA ANALYTICS
A strong foundation in mathematics, statistics, and computer science is beneficial.
However, you don’t necessarily need a four-year degree to get started as a data analyst. You can develop the necessary skills through career-focused diploma or certificate programs devoted to data analytics. Look for programs that focus on practical projects and that include an internship so you can get real-world experience for your resume.
Data analytics certifications can validate your skills and help you land job interviews. Examples include:
- CompTIA Data+: A foundational certification for those starting out in the field
- Tableau Certified Data Analyst: Validates your ability to use Tableau for data visualizations and dashboards
- Power BI Data Analyst Associate: Focuses on using Microsoft Power BI to visualize data
SALARY RANGES IN DATA ANALYTICS
So what is a typical data analytics salary in Canada? According to PayScale, this is how much you can expect to earn in various roles:
- Low end: $47,000
- Median: $62,000
- High end: $84,000
- Low end: $52,000
- Median: $68,000
- High end: $87,000
- Low end: $61,000
- Median: $84,000
- High end: $112,000
- Low end: $61,000
- Median: $86,000
- High end: $117,000
FUTURE OUTLOOK FOR DATA ANALYTICS CAREERS
The demand for people who can glean useful insights from data is expected to increase significantly over the next few years. In the World Economic Forum’s Future of Jobs Report 2023, business intelligence analysts, data analysts, and scientists ranked among the careers projected to see the largest growth rates (30 to 35 per cent) through 2027.
The report says the demand for these roles will be especially strong in the following industries:
- Supply chain and transportation
- Retail and wholesale of consumer goods
- Financial services
TRANSITIONING TO A CAREER IN DATA ANALYTICS
Looking to develop the data analysis skills employers are looking for? Check out the online Data Analytics With AI program from Herzing College. It provides thorough training in data exploration, visualization, cybersecurity, and machine learning.
The program takes just 18 months to complete and includes an internship for real work experience.
Click below to get full program details and chat live with an admissions advisor. We’re here to help!
