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What Does a Data Analyst Do? [2023 Guide]

Studying at Robertson May 12, 2023

what is a data analyst what is a data analyst

A Data Analyst reviews large amounts of data, identifies trends, and communicates those patterns to company stakeholders at all levels. They provide business insights that forecast projections, enhance company performance, and optimize revenue streams.

Data is the backbone of all major business decisions today. It’s sourced, reviewed, and assessed to solve operational challenges and optimize performance. Poor data quality, however, can have a staggering financial impact. In fact, a recent study found that organizations lose an average of $17.3 million annually due to unreliable data. That’s where a Data Analyst comes in.

A Data Analyst is a sharp, inquisitive, and methodical thinker who evaluates large amounts of data, identifies larger trends, and communicates the significance of those patterns to company stakeholders at all levels. In essence, a Data Analyst is the heartbeat of any high-performing business, and is one of the most in-demand roles today (with more than 20,000 Data Analyst jobs in active recruitment in Canada alone). 

So what is a Data Analyst, exactly? This critical role provides visibility on company performance, offering an overview of trends that helps leadership steer the ship and weather storms. Additionally, a Data Analyst provides business insights that:

  • Forecast projections
  • Enhance company performance 
  • Optimize revenue streams


If you’re considering a career as a Data Analyst, it’s important to understand the path it takes to get there. Here, we’ll unveil a roadmap for your journey by identifying the skills you’ll need, the tools you’ll use, and four simple steps you’ll take to become a Data Analyst.

Let’s get started.

What Skills Do You Need To Be a Data Analyst?

Data analyst skills

Data Analysts work in a variety of disciplines and industries, so the skills you need will be just as varied. Below are 18 in-demand Data Analyst skills — both technical skills and soft skills — that employers expect to see.

Technical Skills 

Technical skills are specialized functions and specific tasks that are required to perform essential duties related to a particular job, whether it’s in the realm of science, technology, engineering, arts, or mathematics (STEAM). Below are some of the technical skills required for a Data Analyst role. 

1. Microsoft Excel / Spreadsheets

Excel is one of the most widely used tools in business for good reason: Its nimble, relatively user-friendly interface enables professionals of all skill sets and ability to leverage its tools. Where Data Analysts really shine with Excel is through more advanced functions like a VBA Lookup or writing Macros. 

2. Structured Query Language (SQL)

A command of SQL is widely considered the most important skill for a Data Analyst to have, as it’s recognized as the industry-standard database language. It’s also popular among businesses because it integrates well with other programs. At its core, SQL is a more robust version of Microsoft Excel, with the ability to process large-scale sets of data. 

3. Statistical Programming Languages (R or Python)

Similar to SQL, a basic description of R and Python is that they are more advanced versions of Microsoft Excel. Data Analysts commonly use them for their interactive, statistical environments that enable a more comprehensive analysis and visualization of data. 

4. Probability and Statistics

Processing data is an obvious skill set that any Data Analyst would need, but understanding models and conveying their meaning requires a completely different set of skills. Quintessential tasks that fall under probability and statistics include sampling and simulation as well as fostering in-depth knowledge of Regression and Bayesian models, loss functions, and time series.

5. Data Management and Preparation

Data management is defined as “the practice of collecting, organizing, protecting, and storing an organization’s data so it can be analyzed for business decisions.” A Data Analyst serves as the guardian of a company’s database, ensuring the accuracy and fidelity of that data. 

6. Data Visualization

Once a set of data has been mined, cleaned, analyzed, and reviewed, the next step for a Data Analyst is to illustrate those findings visually through elements like heat maps, charts, and graphs to understand trends and outliers. This is particularly useful when conveying findings to non-technical roles within an organization. 

7. Econometrics

This skill is helpful across a variety of industries, but it’s especially relevant for those closely tied to the economy, like mining, agriculture, and manufacturing. Econometrics is the process of combining theory, math, and statistics principles to transform quantitative assessments into qualitative, actionable checklists that can help develop economic policy. 

8. Machine Learning

As a subset of artificial intelligence (AI) technology, machine learning is all about automating processes. As it applies to data analysis, it creates efficiencies within an analyst’s process by applying algorithms to replace slower, more manual work. This, in turn, shortens the length of time necessary to review large amounts of data and removes human biases from the process. The result: comprehensive and objective insights revealed faster than a human is capable of producing.

9. Data Warehousing

Regarding business intelligence (BI) for companies, data warehouses are exactly what they sound like: A storage facility for housing information — from which dashboards and reports are subsequently created. It’s a veritable vault of intelligence that assists in making informed business decisions.

10. Research

A Data Analyst with research skills has the ability to conduct studies to test hypotheses that inform business decisions and pivot points. While analyzing data involves reviewing and drawing conclusions, researching goes one step further to experiment with the data to test theories. 

Soft Skills

Soft skills are non-technical, interpersonal skills that support how you work. While some skills can be taught and learned, other soft skills are more inherent to your personality traits and work style. 

11. Domain Knowledge

While nearly every industry needs Data Analysts, a general understanding of your specific field — otherwise known as domain knowledge — is essential. For example, a business intelligence analyst for an auction software company should have a general understanding of how auctions are conducted and managed, how revenue is generated, and industry-specific terms. 

12. Attention to Detail

An errant decimal point, extra zero, or any other typo in a data set can have a massive ripple effect on subsequent data analysis, and there’s an ample opportunity for human error when parsing through large quantities of information. That’s why keen attention to detail is an essential skill in this role; making sure you’re checking (and double-checking) your work.

13. Project Management

Building on attention to detail, strong project management skills are also essential in a Data Analyst role, as it requires the ability to collaborate and communicate cross-functionally to receive data requests and process them effectively. 

14. Critical Thinking

This essential skill requires removing unconscious human bias — or “gut feeling” — to question the data and probe your findings with an objective approach to draw your conclusions. 

15. Problem Solving

A Data Analyst is, by nature, a problem solver at heart, with the ability not only to uncover opportunities for performance improvement, but also serve up data-backed recommendations and opportunities to adjust or pivot processes in the future.

16. Effective Communication

Part of the role of a Data Analyst is to draw conclusions based on visible data trends, and being a strong communicator is an essential part of conveying those conclusions. Effective written and verbal communication skills are vital in this role to storytelling in a clear, thoughtful way, both in reporting and in conversation. 

17. Presentation Skills

Presenting findings clearly means honing presentation skills: Like creating an organized deck in PowerPoint or Google Slides that visualizes your message, moves at an appropriate pace, and leaves the door open for answering questions. 

18. Public Speaking

Public speaking skills come naturally for some, but for others, this is a muscle that needs to be flexed, only growing stronger with practice. Public speaking requires growing increasingly comfortable communicating with groups of all sizes. 

What Does a Data Analyst Do Day to Day?

What does a data analyst do?

At their core, Data Analysts are problem solvers who can identify audience insights, diagnose performance issues, and drive revenue for a company. While no two days may be the same, the following are some of the most common daily tasks you’ll come across in this line of work as a Data Analyst: 

  1. Develop and design databases and building infrastructure that will house, compile, and process aggregated data. 
  2. Mine or collect data from a variety of relevant sources. 
  3. Clean and filter selected data. 
  4. Create segments, establish cohorts, and find other ways of grouping data sets. 
  5. Data manipulation, or the process of adjusting data by applying logic to create a new data set. 
  6. Analyze and interpret results, identifying patterns, and developing narratives that tell the story of those trends.
  7. Produce reports that summarize findings, creating data visualizations, and high-level synopses of larger patterns. 
  8. Present findings to relevant stakeholders and departments at all levels of the company with the ability to effectively communicate the message behind the analysis. 

Data Analyst vs Data Engineer vs Data Scientist

data analyst vs data engineer vs data scientist

If you’re considering a career in data analysis, you’ve likely heard about three core roles: Data Analyst, data engineer, and data scientist. But what purpose does each role serve, and how do they differ?

The most basic explanation is that an analyst is generally considered an entry-level role, an engineer is more mid-level, and a scientist is the most senior-level role in the field with the most advanced skill set. As you progress in your career to higher-level roles, more sophisticated and complex skill sets are required to perform the duties of each function. 

Data Analyst 

  • Position: Entry-level
  • Education Required: Bachelor’s degree, diploma or equivalent certification or degree in computer science, information systems, statistics or a related field
  • Skills Required: Data processing, modeling, and reporting 

Data Engineer 

  • Position: Mid-level 
  • Education Required: Master’s or equivalent certification or experience
  • Skills: Data Analyst skills, plus creating and integrating APIs, optimizing performance, and data pipelining

Data Scientist 

  • Position: Senior-level
  • Education Required: Master’s or equivalent certification or experience
  • Skills: Data scientist skills, plus advanced analytical skills, machine learning, and data conditioning 

The 4 Types of Data Analysis

types of data analysis

What is data analytics? Before you set out to start your career, there are four core approaches or types of analysis that you should know, each of which strives to solve different business needs.

Descriptive Analytics

Descriptive analytics is one of the most basic types of data analysis, using historical information to describe visible patterns. This approach strives to assess what’s already happened so that businesses can glean insights and inform change. 

Within this type of reporting, there are two subsets of further analysis:

  • Ad hoc reporting: Generated as-needed to reveal the answer to a specific question or meet a business need. 
  • Canned reporting: Routine reporting conducted on a scheduled date, pulled from an established reporting framework.

Diagnostic Analytics

Just as it sounds, diagnostic analytics uses established data points to understand and diagnose performance issues or gain perspective on cause and effect within business operations. This approach uncovers both what happened, and why.

Through this type of reporting, there are two subsets of further analysis: 

  • Discover and alerts: Analyzes data to flag an issue before it happens or as it begins, enabling proactive, corrective action. 
  • Query and drilldowns: A more granular approach to diagnostic analytics, used to uncover greater detail that underscores why certain business results were — or weren’t — achieved.

Predictive Analytics

The most common type of data analysis used in business, predictive analytics identifies patterns, builds connections, and diagnoses performance issues. Its primary purpose is to make data-backed predictions on what may or will happen in the future. Predictive modeling is commonly used in retail sales, for example, to uncover trends in seasonal decline based on historical data. 

Prescriptive Analytics

This advanced type of data analysis leverages machine learning to predict results and recommended actions. It serves as one of the most comprehensive approaches to analytics, offering a window into what happened, why it happened, what’s most likely to happen down the road, and the recommended course of action. 

Common Data Analysis Tools

Data Analysts use a variety of tools, depending on the industry. If you are pursuing a career in data analytics, employers will expect you to be comfortable with a variety of tools, including:

  • Google Sheets 
  • Jupyter Notebooks
  • Microsoft Excel 
  • Microsoft Power BI 
  • R or Python
  • SAS
  • SQL
  • Tableau

Types of Data Analytics Jobs

Data analytics jobs may vary across industries, so your role will look slightly different depending on whether you work at a financial institution, insurance company, or marketing agency. All types of organizations can benefit from having a Data Analyst who has an in-depth understanding of data and how to use it. 

1. Business Analyst

Business Analysts review business-specific data that can be used to optimize performance within an organization. They typically evaluate internal and external data for trends, successes, and any potential issues and present their findings and recommendations for improvement to their teams.

2. Data Analyst

Data Analysts gather large sets of data in order to interpret it, draw conclusions from it and recap findings for internal teams. Analyzing data helps them make informed decisions, whether it’s about pricing products, cutting costs, readjusting budgets, or effectively reaching target audiences. 

3. Data Analyst Consultant 

Data Analyst consultants are data problem-solvers. They’re responsible for presenting data insights, trends, and suggestions to organizations and advising them on how they can make stronger business decisions. The primary difference between a Data Analyst and a Data Analyst Consultant is they typically don’t work in-house. Instead, they may work for several organizations at one time and may have many different clients over their career. 

4. Financial Analyst

Financial Analysts are one of the best known types of data analytics jobs. The primary function of financial analysts is to look at financial data like stocks, bonds, mutual funds and other investments. By evaluating investment performance, they’re able to better predict trends and advise businesses (or even individuals) on their financial decisions. 

5. Marketing Analyst 

Marketing analysts are people analysts — their goal is to help organizations better understand their target audiences so they can effectively reach them. They analyze data that relates to target demographics including market research, purchasing trends, web traffic, and surveys and use that data to determine ways companies can better reach new and existing customers. A large part of this job involves backing up the data and presenting it to other teams, so strong presentation skills are a big bonus. 

Frequently Asked Questions (FAQs)

What Is the Average Data Analyst Salary?

So how much does a Data Analyst make? The average base salary for a Data Analyst in Canada is $72,175, but that figure can vary dramatically based on location.

As of January 2023, the average salary reported in Toronto was $89,004. In Halifax, however, the average salary was more than $25,000 less at $62,488 annually. 

Canadian Cities with Highest Data Analyst Salaries


Average Annual Salary

 Toronto, ON 


 Vancouver, BC


 Ottawa, ON


 Montreal, QC


 Edmonton, AB


 Brampton, ON


 Mississauga, ON


 Calgary, AB


 Halifax, NS


Source: Indeed (As of January 14, 2023).

Is Data Analysis Hard? 

Data Analysis can be an incredibly rewarding career path for individuals who are naturally curious, logical thinkers who are driven by data and results. Due to the variety of skills required to do the job — including ten technical skills and nine soft skills — becoming a Data Analyst might feel challenging for some. For others, it can be the perfect fit to launch, level-up, or change your career. 

How Long Does It Take To Become a Data Analyst?

Given that many Data Analyst roles call for a diploma or Bachelor’s degree, it can take an average of three to four years to become a Data Analyst. However, that range can vary dramatically depending on specific requirements set forth by individual companies. At Robertson College, you can enroll in our Data Analyst program to earn your diploma in just 12 months

How To Become a Data Analyst

Knowing how to become a Data Analyst requires enrolling in a program to earn your certification, developing your technical skills, creating a portfolio that demonstrates your savvy, and applying for jobs once you’re ready. Here are four simple steps to take to become a Data Analyst:

  1. Earn a Degree, Diploma or Certificate. Most entry-level roles in data analysis require a diploma or Bachelor’s degree in computer science, statistics, data analysis, or equivalent experience. Robertson’s 12-month diploma program compresses the average time it takes to become a Data Analyst, getting you to your next step, faster.

  2. Learn General-Purpose Programming Languages. Build your experience with technical skills required and seek out opportunities to practice learning on specific programs like SQL, R, or Python.

  3. Create a Portfolio. The ultimate goal of a Data Analyst portfolio is to demonstrate the variety of skills required to effectively do the job. Be sure to include examples of past projects where you’ve used specific tools and programming language that your potential employer cites in the job description.

  4. Apply for Jobs. Seek out entry-level Data Analyst jobs for which you’re qualified, polish up your resume, and write a knock-out cover letter that demonstrates exactly why you’re the right fit for the role. 

Becoming a Data Analyst can be the perfect fit for someone looking to start, level up, or change up their professional path. By enrolling in the right program, honing your technical skills, and networking, you’ll be starting your new job in a matter of months. 

Ready to kickstart your career? Enroll in our Data Analyst program today.

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