First and foremost, it’s critical to understand the difference between data analytics and data science. To do so, let’s take a look at a definition of both.
According to Northeastern University, data analysis involves answering questions generated for better business decision-making. It uses existing information to uncover actionable data and focuses on specific areas with specific goals.
On the other hand, data science focuses on discovering new questions that you might not have realized needed answering to drive business innovation. Keep reading for an in-depth overview of both disciplines to decide which career path would better suit your career aspirations.
WHAT ARE THE SKILLS NEEDED TO WORK AS A DATA ANALYST?
Here are the must-have skills employers want to see on your resume:
A hot tip to make your application stand out: Project management has become another vital skill sought after by employers. As more and more companies are starting to hire fully remote workers, employers are looking for people who can effectively manage their own time and projects and achieve goals.
WHAT ARE THE SKILLS NEEDED TO WORK AS A DATA SCIENTIST?
Take a look at the top six skills required to become a data scientist in 2022:
- Machine Learning
- Deep Learning
- Advanced Statistics
- Predictive Modelling
A hot tip to make your application stand out: Present the work you have done in a portfolio using real-world data samples to showcase the problem you have solved for the company. Additionally, write blogs to convey your findings in simpler terms, you can publish these findings on your LinkedIn page. Additionally, your ability to present and communicate well are two soft skills to help you stand out amongst the crowd.
HOW TO CHOOSE BETWEEN A DATA ANALYTICS AND A DATA SCIENCE CAREER
Choosing between a data analytics and a data science career can be challenging since the job roles often overlap. Here’s a quick checklist to help you decide which career you should choose:
- Do you enjoy exploring existing data and information to uncover actionable insights? (Data Analytics)
- Are you prepared to discover new questions from data sets to drive business innovation for stakeholders? (Data Science)
- Can you see yourself cleaning large data sets to check if a hypothesis is true or false quickly? (Data Analytics)
- Do you possess dexterity in tools such as Excel and SQL to slice and dice large data samples? (Data Analytics)
- Do you have experience working with BI tools like Power BI for reporting? (Data Analytics)
- Are you prepared to spend the majority of your job processing, cleansing, and verifying data integrity? (Data Science)
- Are you comfortable identifying new trends in data to make predictions for the future? (Data Science)
- Are you a good storyteller able to convey complex technical terms in an easy to understand manner for your audience? (Data Analytics)
CAN A DATA ANALYST WORK AS A DATA SCIENTIST AND VICE VERSA?
The short answer is yes, a data analyst can transition to a data scientist, and vice versa. However, one important thing to note is that it’s more common for a data analyst to transition into data science rather than the other way around.
One main reason for this is that data analysts can quickly build on their technical skills to become data scientists. Let’s look at a high-level overview and some basic day-to-day activities of both jobs to understand this better.
DATA ANALYTICS 101
One of the prerequisite soft skills required for a data analyst career is communication. Data analysts need to be quick storytellers to deliver insightful data findings to their stakeholders.
“Our job is to tell what’s in the data versus data scientists focusing on the data’s cause and causality,” explains Hai Tran, Senior Data Analyst at IAG.
On a day-to-day basis, 70% of your time as a data analyst is spent cleaning large data sets, manipulating data, getting the final results, and finally creating a dashboard with the results using Tableau. Your day is structured around your project and constant communication with your team to understand what results you need to obtain from the data.
DATA SCIENCE 101
As a data analyst, you already possess the fundamental understanding of the skills and tools required to become a data scientist, hence why it is more common to see data analysts transition to a more complex data scientist role.
One of the primary skills that data scientists possess over data analysts is writing code and solving complex algorithms.
On a day-to-day basis, around 80% of your work as a data scientist involves working on different data pipelines, heavy-duty SQL work for data modeling, and programming.
“Data science involves making slightly more advanced recommendations compared to data analysis, which focuses more on asking the right questions,” adds Anuj Loomba, Senior Data Scientist at oOh! Media.
JOB OUTLOOK FOR DATA ANALYSTS AND DATA SCIENTISTS
In 2021, data analytics and data science represented two of the most in-demand, high-paying jobs. This is partly because data science and analytics are evolving more than ever before. In addition, with the acceleration of digital adoption across all industries, companies are looking for people who can solve problems with data.
Both jobs also offer some of the most competitive salaries in the industry. According to Glassdoor, the average salary for a data analyst in the following countries ranges from:
- USA: Ranges from $46,000 to $106,000 per year.
- UK: Ranges from £20,000 to £44,000 per year.
- Canada: Ranges from C$44,000 to C$88,000 per year.
- Singapore: Ranges from SG$3,000 to SG$7,000 per month.
- Australia: Ranges from AUS$60,000 to AUS$120,000 per year.
The average salary for a data scientist in the following countries ranges from:
- USA: Ranges from $82,000 to $167,000 per year.
- UK: Ranges from £30,000 to £73,000 per year.
- Canada: Ranges from C$62,000 to C$123,000 per year.
- Singapore: Ranges from SG$4,000 to SG$10,000 per month.
- Australia: Ranges from AUS$76,000 to AUS$150,000 per year.
WHAT INDUSTRIES ARE HIRING?
According to Career Foundry, the top industries hiring data analysts in 2022 include:
- Business Intelligence
- Sharing economy services
According to Naukri Learning, the top industries hiring data scientists in 2022 include:
- BFSI (banking, financial services, and insurance sector)
- Media and Entertainment
- Digital Marketing
- Cyber Security
- Mining, Quarrying, and Oil and Gas Extraction
KICK START YOUR DATA ANALYTICS OR DATA SCIENCE CAREER TODAY
Our 12-week Data Analytics and Data Science Immersives feature expert instructions and one-on-one career coaching with the best in the industry. We also help you build your network and connect you with top employers worldwide.
FREQUENTLY ASKED QUESTIONS
Q1. DO RECRUITERS VALUE A FOUR-YEAR UNIVERSITY DEGREE IN DATA SCIENCE MORE THAN A ONE YEAR OF BOOTCAMP-STYLE TRAINING?
Both a university degree and bootcamp experience are valued by recruiters, and ultimately it depends on what experience the employer is looking for.
A new study from Switchup analyzed the hiring rates of coding bootcamp graduates among the big five tech companies — Apple, Microsoft, Facebook, Google, and Amazon. Bootcamps like Code Fellows, Hackbright Academy, Hack Reactor, Product School, and General Assembly were featured bootcamps with the highest percentage of alumni (from 2% to 11.15% of alumni) employed at the big five.
Q2. CAN YOU APPLY TO A DATA ANALYTICS OR DATA SCIENCE JOB THAT REQUIRES THREE YEARS OF EXPERIENCE, IF YOU HAVE LESS THAN THREE YEARS?
Yes, apply. If you think you have the right skills and drive, go for it. First, consider if the skills you developed in your previous role are transferable and applicable to this new position. Don’t let these three-year requirements deter you.
Q3. WHICH IS BETTER FOR BUSINESS: ANALYTICS OR DATA SCIENCE?
Business analytics is concerned with the analysis of data to make key business decisions, while data science uses statistics and various other methods to complement and inform business decisions. While there’s no correct answer, if you’d prefer to be more involved in a business decision, then a business analyst role is probably for you.