Once coined the sexiest job of the 21st century, data scientist has become one of the most popular job fields in recent years, as organisations of all industries have come to realize the value of data-driven decision making. The demand for data scientists has skyrocketed, leading to a significant increase in the number of people entering the field. This high demand has in turn led to a significant increase in the number of universities and bootcamps offering data science programs, and a proliferation of online resources for learning data science skills. The popularity of data science has also led to a lot of hype around the field, which has had a profound impact on its reputation. On one hand, the hype has helped to raise awareness about the importance of data science and the wide range of career opportunities available in the field. On the other hand, the hype has also led to unrealistic expectations and a lack of understanding about what data science is, and what data scientists actually do. Some people have the impression that data science is a magic wand that can solve all problems, while in reality it’s a complex field that requires a lot of hard work, dedication and critical thinking.
What does a data scientist actually do?
Data scientists possess a comprehensive array of skills in statistics, mathematics, programming, combined with domain expertise, which they use to analyse and make sense of complex data. They have a wide range of responsibilities, including collecting, cleaning, and processing data, building predictive models, creating visualisations, and communicating findings to stakeholders with the goal of uncovering insights that can help inform business decision making. For example, by analysing customer data, a data scientist may discover patterns in purchasing behaviour that can be used to develop targeted marketing campaigns. Or by analysing sales data, a data scientist may identify product categories that are underperforming, which can inform decisions about product development or discontinuation. In short, data scientists play a critical role in making data-driven decisions that can drive the success of an organization.
What degree do you need to become a data scientist?
Data science is a diverse and dynamic field, and there is no set path to starting a career in data science. Data scientists come from a diverse range of professional backgrounds and fields of study. Historically, data scientists have completed a university degree in fields such as statistics, computer science, and engineering. However, it is also common for data scientists to come from non-traditional fields and learn through alternative paths.
For example, at our data team includes individuals from computer science, marketing and finance, mechanical engineering, mathematics, and biology research, some with a PhD. Despite these differences, a shared interest in problem-solving and a passion for data is commonly found among data scientists.
The amount of data being generated is constantly increasing, and as a result, the demand for data science skills is projected to grow along with the increasing use of machine learning and AI. We asked a few members of our team about their journeys, and in this blog we’ve summarised the necessary skills, and practical steps one can take in order to embark on a successful and fruitful career in data science.
Watch the following video to hear about the background of some of our data scientists, and learn about their journeys:
Skills
”If you want to be a good data scientist you have to think as if you were a business owner, but code as if you were a software engineer."
Our data scientists were in agreement about one thing, that a balance of business understanding, communication, and technical skills are equally important for a successful data scientist. The following are some of the top skills that are necessary to get started and succeed in data science.
Communication
When it comes to a successful data scientist, communication skills are key.
When beginning a career in data science, it is important to recognize that stakeholders are often more interested in the results and value of a project as opposed to the methods used to achieve them. It is essential for data scientists to maintain open communication with the business to understand their needs and requirements, as a lack of understanding can hinder buy-in for the solutions being proposed.
Adapting to target audiences
It is also important for data scientists to adapt their language and communication style to their audience, as stakeholders may not have a technical background and may not be interested in technical details such as the latest deep learning algorithm. Without strong communication skills and an understanding of the business use cases, effectively communicating with stakeholders at various technical levels can be challenging. In order to effectively convey information, it is important to master the art of storytelling and adjust language to match the audience. It’s not always a case of one size fits all, you may find yourself adjusting your language for the different stakeholders you encounter and their technical level. Sometimes getting into the technical detail will be what is required and will add the most value and credibility. Knowing how to read the room and adapt will be the nimble task in front of you, and it takes practice. The temptation to dive into talking technical terms can be an alluring safe space, but it’s important to understand that this can often hinder the ability to stay relevant and on point with the audience. If a stakeholder can’t understand you, you simply won’t be invited into the room for the next problem solving conversation, and opportunities for harnessing data science will be missed.
Translating business problems to technical solutions
As a data scientist you will often find yourself in situations where you have to translate a vague problem into a clearly scoped project. The ultimate goal for data scientists is to provide value to clients or business stakeholders, which may not be accomplished by creating overly complex models. It is more important to understand the business problem, use appropriate tools to solve it, and generate enthusiasm for the solution. If people can’t see the value, the solution you’ve created to support decision making may quickly fall out of use. In addition, whether working as a consultant or an in-house data scientist, it is crucial to understand the priorities and concerns of the business. Dollars are important, but as a data scientist you need to understand the flow of data, business decision-making processes, and key players within the organisation.
SISP
A trap that data scientists tend to fall into is “SISP” or Solution In Search of a Problem. These data scientists may have just learned a cutting-edge machine learning model or a new technology stack, and are eager to apply it to the business. Some may even build a solution based on what they have learned, only to realise that the business doesn’t need it. For example, a data scientist may have just mastered a new Computer Vision model called YOLOv8, but it doesn’t add much value to a business who has mostly structured data in relational databases.
”The ability to ask the right questions to identify the core of the business problem and then translate it into a technical solution is a vital skill for a data scientist.
Programming Skills
Data scientists require programming skills, with Python and SQL currently in high demand among employers. Being able to demonstrate your coding abilities is crucial in impressing potential employers, with many including a technical challenge as part of the interview process.
Python
Python is a versatile and flexible programming language that can be used for a wide range of tasks, from web development to scientific computing, with a rich ecosystem of libraries and active community support. As a data scientist, you can use Python to tackle the entire data science workflow, such as data wrangling, data exploration, machine learning model development, visualisations, etc. We suggest you familiarise yourself with the most common libraries used in data science, such as NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, and PyTorch, which make it easy to perform these tasks effectively and efficiently.
SQL
SQL is essential for data scientists to work with databases and extract meaningful insights from data. It is primarily used for data extraction and manipulation, such as joining tables, creating aggregates etc. More recently, cloud platforms even offer the capability of generating machine learning models directly using SQL (e.g., in BigQuery), making it an accessible and convenient option for data scientists.
Even if you don’t have a computer science background, getting your hands dirty and practicing your skills will go a long way. Building a portfolio of projects to showcase your work is also beneficial.
Data Literacy
It’s not enough to be able to code and simply apply existing machinery to data.
You need to make friends with the data first.
It is important to have a deep understanding of data, which includes various skills such as statistics, analytics, visualisation, and creating data pipelines. A genuine interest in the data and the willingness to experiment with different approaches is crucial when solving problems, especially when working with unusual or noisy data. Exploratory data analysis, feature selection, and feature engineering are all important aspects of the development process and you may even find that you need to come up with your own solution to solve a tricky problem.
It is also essential to have a good understanding of the entire data pipeline, including where the data comes from, how it is stored, and how to access it. Knowledge of best practices for setting up a data pipeline is also important.
Machine Learning
To be a successful data scientist, it is important to have a comprehensive understanding of the various models available and the specific problems they can solve. A solid understanding of the different business cases each model is applicable to is also important.
Additionally, simply inputting data into a model is not sufficient. It’s necessary to apply basic statistics to understand the data and features first. In other words, walk before you run. There are usually a few models you can choose from, but having a deep understanding of what’s happening under the hood is crucial in selecting the right one and interpreting the output of the model.
Practical steps you can take today
Want to know the expert tips from our data scientists for launching your career in this field?
Watch the following video:
Finding where your passion lies
Being a data scientist involves taking on various roles, so it’s important to have an open-minded attitude and pursue the areas that interest you the most. Don’t stress too much and instead focus on building your expertise gradually. Experiment with different techniques, gain exposure to multiple domains and work with knowledgeable people. Most importantly, it is essential to continue pursuing what you are passionate about.
If you love what you do, you’ll be naturally more curious and inclined to research and experimentation, which leads to better results.
Challenging imposter syndrome
”Because I did not get formal data science training, the first thing would be imposter syndrome. For example thinking that I would not be qualified for a job, or that I would not be able to find a solution for a particular problem.”
– Vivian Mai
Finding where your passion lies
Being a data scientist involves taking on various roles, so it’s important to have an open-minded attitude and pursue the areas that interest you the most. Don’t stress too much and instead focus on building your expertise gradually. Experiment with different techniques, gain exposure to multiple domains and work with knowledgeable people. Most importantly, it is essential to continue pursuing what you are passionate about.
If you love what you do, you’ll be naturally more curious and inclined to research and experimentation, which leads to better results.
Challenging imposter syndrome
“Because I did not get formal data science training, the first thing would be imposter syndrome. For example thinking that I would not be qualified for a job, or that I would not be able to find a solution for a particular problem.” – Vivian Mai
Being an active field of research, data science evolves every day, and there are regularly new technologies and problems emerging. It’s not necessary to know everything. Even if we don’t know something today, a great data scientist can identify knowledge gaps, learn those skills, and come back tomorrow having achieved something new.
Building a portfolio
The most effective way to learn is by gaining practical experience. Websites such as Kaggle offer a great opportunity for this, particularly for those just starting out and looking to build a portfolio to showcase to potential employers. There are a variety of challenges, free data, and a helpful community to support you. The more varied the projects, the better, as it enables you to use different techniques and tools. Additionally, if you are struggling with a specific challenge or in need of inspiration, the community is there to help.
Seeking out opportunities
Do what you can to flex those data science muscles. Seize any opportunity which allows you to employ your data science skill set, whether it be through volunteering for tasks within your team or reaching out to others for help. It’s not a title which makes you a data scientist, but the nature of your job. To progress in your career, it’s recommended to try new things and continuously learn by taking courses or participating in challenges like Kaggle. This will not only help you improve your skills, but also discover new passions within the field that may lead to new opportunities.
Building your network
Networking and building a community is invaluable when starting a career in data science. Joining a professional networking platform like LinkedIn and reaching out to people in the field can open doors to new opportunities. When connecting with people, it is important to personalise your message, rather than simply adding them as a connection. This will increase your chances of getting a positive response and hearing about more opportunities.
Staying up to date with new technology
To stay current in data science, it is crucial to continuously learn and stay up to date with the latest technologies and paradigms as they constantly evolve. Consider the cloud – a couple of years ago it was innovative to use a virtual machine, but now we can launch a serverless auto scaling cluster with an automatic failover or disaster recovery, an advancement that has made production models more robust. This plentiness of options can be intimidating. Stay selective and be realistic about the learning process but accept the need to continuously learn new algorithms and techniques to stay ahead in this rapidly changing field.
Conclusion
How long is it going to take to become a data scientist?
Given the complexity of backgrounds that data scientists come from, and the different paths one can take, the timeline is not well-defined and can vary greatly for each individual. Some may be fortunate enough to secure a position immediately after graduation, while others may take months or even years, depending on their current commitments and the amount of time they can dedicate to learning.
Becoming a data scientist takes time and effort, but having a plan and being consistent in your learning will help you achieve your goal. To maximise your chances of success, it is best to clarify your career goals early on, and take proactive steps to build a strong portfolio, expand your network, and gain hands-on experience through data science projects. Initially, focus on understanding the general concepts and gradually build your knowledge, and try to get hands-on experience as much as possible. Make sure to educate yourself from credible sources, whether that be through a university program, online courses, or learning from colleagues.
Don’t be afraid to take on new challenges and put yourself out there, as it will only make you a better data scientist.
Check out Mantel Group’s Emerging Talent Programs to get a head start on your career journey, and have a look at the resources below to get started.
Resources
Check out some of the resources mentioned:
- AI Australia Podcast
- Data Science Central: https://www.datasciencecentral.com/
- LinkedIn influencers: Cassie Kozyrkov and Allie K. Miller
- For online courses:
- Coursera: https://www.coursera.org/
- Udemy
- Toward DS https://towardsdatascience.com/
- Roboflow – https://blog.roboflow.com/
- Vicki Boykis Blog – https://vickiboykis.com/
- Reddit – https://www.reddit.com/r/datascience/
