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Career Development, Data

Is a Data Science Career a Good Fit for You? Here’s What You Need to Know.

General Assembly
May 31, 2022

So you’re thinking of a career in data science, but you’re not sure if it’s the right fit for you.  Here is your data science guide, where we break down what data science is, day in the life of a data scientist, tips from GA’s data science alumni, career opportunities, and much more. 


According to Berkeley, data science is the ability to take data, understand it, extract value from it, visualize it, and communicate the findings. The term “data science” was coined in 2008 when companies realized the need for data professionals to analyze immense amounts of data. 

As a data scientist, “you need to be able to digest big data samples, clean the data, organize and analyze it, to then be able to look into the data and gather meaningful insights for the end-user, as well as various stakeholders,” explained Neal Manahan, GA Alumni, now an Analytical Framework Consultant, at Neilson IQ. 

Today, data science is one of the most in-demand career paths. According to DevSkiller’s recent Top IT Skills Report, companies have recorded a 154% increase in the demand for skills like Python by companies in 2021. 

However, due to the shortage of talent globally, the demand for data scientists is increasing faster than there is supply, that’s why data science is now one of the best-paid tech careers. According to Fortune Education, data science is a hot job market, with data scientists making a median salary of $164,500 in 2020 (an 8% increase from the $152,500 median pay in 2019). 


The spectrum of tasks for a data scientist can vary hugely day-to-day. Data scientists tend to work closely with data analysts, hence why a data scientist’s range of skills can be a mixed bag. To narrow down the daily tasks of a data scientist, we asked Kenny Evans, GA Alumni, now a Principal Machine Learning Engineer at Vizio, to break it down for us. 

Some of the day-to-day responsibilities of a data scientist include: 

  • Preparing large data sets, and running models to interpret the data, using tools like Python, SQL, and SAS. You are using these tools to plot and graph your data and to understand what type of data you are dealing with, and what information you need to get out of the data. 
  • Coding to gain further insights into the data, using tools like R, Python, JavaScript (Programming Languages).

Daily, “you see a lot of code, it doesn’t necessarily look pretty, but it’s fun if you know what you’re trying to get out of the data,” said Kenny.

  • Meetings with various stakeholders to present and further analyze specific findings from the data. It’s essential to collaborate well with others and understand each end-users needs to make sure you are extracting the correct information from the data. 


Data science may be a very well-paid and sought-after position by employers globally, but this shouldn’t be the only deciding factor in becoming a data scientist. Here are four questions you should be asking yourself to determine if data science is the right career choice for you.

Q1. Are you a critical thinker not afraid of a challenge? 

As a data scientist, you need to be able to make sense of and organize large data sets. This tends to be challenging and time-consuming and requires critical thinking skills. Extracting knowledge from data is vital, as it enables data scientists to reveal actionable insights and solutions. 

Q2. Are you able to work well with others? 

As we mentioned earlier, collaboration is a critical component of a data scientist’s daily life. So you need to be able to collaborate closely with teams from various backgrounds and experience levels, such as the data analytics and engineering teams and multiple stakeholders and users of the data.

Q3. Are you prepared to work hard to fine-tune your technical skills? 

Data science is one of those careers where you need to be able to use your tech stack well. Otherwise, you may extract and misinterpret data. This is why it’s so critical you are an expert in tools like Python and are prepared to keep learning. “My biggest tip for aspiring data scientists is Python. Spend a week doing a deep dive into Python, so you hit the ground running”, said Kenny. 

Hot tip: If you’re interested in joining a data science bootcamp, we recommend you brush up on your foundational data knowledge to rock the course. Spend a few weeks prior manipulating data sets and coding with tools like Python, SQL, and JavaScript. 

Q4. Are you quick on your feet?

Sometimes you will need to be able to make data-driven conclusions fast and be able to connect the dots before others. If you’re someone who has an insatiable curiosity, enjoys solving a challenging puzzle, and focuses on the source of the issue rather than the symptoms, you might enjoy the challenge. 

Still not sure if Data Science is the right career choice for you? Check out our ebook, “Landing Work You Love,” for more helpful insights and tips. 


During our Data Science Immersive, you are taught by industry experts, experience one-on-one career coaching, and connect with top employers worldwide to get hired. Here are some typical beginner roles our GA graduates tend to land post bootcamp graduation: 


Job Role: A data science associate is an academic program offered at the undergraduate level. It gives students the basic skills and fundamental knowledge to progress to a postgraduate level if they want to progress to a data scientist level. The main responsibility of a data science associate is to support the lead data scientist on the team, this would involve assisting in the production of statistical models, tools, and processes. 

Skills Required: Data Mining, Data Visualization, Statistics, Algorithms, Data Acquisition and Cleaning, Python (basic level).

Average salaries: (according to Glassdoor)

  • 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. 


Job Role: Data and AI specialists focus on transferring information from hardcopy to a digital format. At a high-level the main tasks of a data and AI specialist include, conducting thorough data analysis of the company’s information and storage systems, creating or implementing digital conversion programs in line with the company goals, installing and maintaining data collection software, and creating and submitting data collection reports. 

Skills Required: R, Python, JavaScript (Programming Languages), SAS, Data Processing, Knowledge of Computer Hardware Systems, Data Engineering (basic level), Data Storage Systems, Computer Science. 

Average salaries: (according to Glassdoor)

  • USA: Ranges from $41,000 to $121,000 per year. 
  • UK: Ranges from £21,000 to £66,000 per year.
  • Canada: Ranges from C$34,000 to C$68,000 per year. 
  • Singapore: Ranges from SG$1,900 to SG$9,100 per month. 
  • Australia: Ranges from AUS$66,000 to AUS$136,000 per year. 


Job Role: On a daily basis, the main responsibilities of a data engineer revolve around building systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. A data engineer’s key goal is to make data accessible so that companies can use it to evaluate and optimize their performance. 

Skills Required: R, Python, JavaScript (Programming Languages), SQL, Scala, SAS, Perl, Excel, Hadoop, Rational and Non-Rational Databases, ETL Systems, Data Storage, Automation and Scripting, Machine Learning, Kafka, Cloud Computing (Amazon Web Services), Data Security. 

Average salaries: (according to Glassdoor)

  • USA: Ranges from $82,000 to $170,000 per year. 
  • UK: Ranges from £30,000 to £78,000 per year.
  • Canada: Ranges from C$63,000 to C$121,000 per year. 
  • Singapore: Ranges from SG$3,000 to SG$10,000 per month.  
  • Australia: Ranges from AUS$71,000 to AUS$140,000 per year. 


Job Role: Data analysis involves answering questions generated for better business decision-making. Data analysts use existing information to uncover actionable data and focus on specific areas with specific goals. A data analyst’s primary responsibilities include managing master data, including creation, updates, and dashboard upkeep, developing reports and analysis, support the data warehouse in identifying and revising reporting requirements. 

Skills Required: Data Visualization, Data Cleaning, Python, SQL, Microsoft Excel, Critical Thinking, Communication.  

Average salaries: (according to Glassdoor)

  • 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.


Job Role: The roles of a data scientist and data science consultant often overlap but in general, a data science consultant’s main responsibilities include strategy development (using data to determine probable outcomes to help companies develop appropriate business strategies), strategy verification (involves verifying or validating a strategy using computer programs to predict the long-term strategy effectiveness), and data modeling and development (design or build various data models that are customized to serve a business’s unique needs). 

Skills Required: R, Python, JavaScript (Programming Languages), Database Creation, and Management, Data Processing, Data Visualization, Statistics, Data Acquisition, and Cleaning. 

Average salaries: (according to Glassdoor)

  • USA: Ranges from $72,000 to $154,000 per year. 
  • UK: Ranges from £34,000 to £65,000 per year.
  • Canada: Ranges from C$61,000 to C$116,000 per year. 
  • Singapore: Ranges from SG$3,800 to SG$12,000 per month.
  • Australia: Ranges from AUS$60,000 to AUS$130,000 per year. 


Changing careers doesn’t have to be hard, you just need a plan. To help you hit the ground running and get you one step closer to your dream data science job, we’ve created a four-step action plan.

Step #1: What industry do you want to grow your career in.  

One of the most important things to keep in mind is the industry you would like to work in once you are ready to start applying for your first job. Since data scientists are in demand, the world is your oyster, so it’s really up to you to decide which direction you want to take your career. 

According to Career Karma, these are the top industries hiring data scientists in 2022: 

  • Financial Services
  • Information Technology
  • Healthcare
  • Retail 
  • Media and Entertainment

Step #2: Step up your technical skills with a data science bootcamp. 

If you have no previous coding experience, it’s important you join a data science bootcamp that offers hands-on experience using skills like Python. Python is one of the top technical skills required to enter the field of data science. Thus, it’s crucial to have a solid fundamental understanding of this language.

“I came in with zero coding background and very little statistical knowledge and applied mathematics going into General Assembly. The combination of all of that work that was applied throughout the course was incredibly helpful”, says Neal. 

Step #3: Start your portfolio prep early. 

Portfolios are a crucial component to building your personal brand and grabbing the attention of recruiters and employers. For a data science portfolio, we recommend the following:

  • Don’t include all of your work in your portfolio. Choose project highlights where you can show off powerful Python coding skills. 
  • Spotlight your communication skills by coupling portfolio samples with an accompanying story, showing how you found a solution to your problem. 
  • Consider using GitHub instead of a website to host your portfolio. This shows you are knowledgeable in the tool and enables your work to be hosted in a space frequented by potential coworkers, mentors, and hiring managers. 
  • Employers will only skim through your portfolio. Thus, it would help if you made it as easy as possible for them to pick up on key findings through data visualization graphs and charts.

Step #4: Take advantage of your resources. 

There are plenty of great resources available to you online . When making the transition to a career as a data scientist, these resources will become your best friend: 

  • FreeCodeCamp
  • Khan Academy
  • Python
  • Data science videos on YouTube featuring subject matter experts
  • Code Wars

Networking is another essential resource. It’s imperative you remain active in the data science community and network with potential colleagues, recruiters, and hiring managers. Don’t be afraid to reach out to fellow data scientists on LinkedIn and ask them about their experiences. It’s also one of the easiest ways to build out your network. 

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