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How to become a Data Scientist without a degree at a big tech company

By Ana Isabel Alonsagay

Digital data is all around us – from the Google searches we make, to the e-mails we send, to the products we shop for online. Such information now forms a core part of our daily activities, and, if leveraged correctly, can be used to further improve our communications and business processes. As such, with investments in data analysis expanding each year, so too is demand for skills in data science. 

The field is one of rapid growth and opportunity, with plenty of lucrative career pathways for those with the right expertise. Below, we explore the skills you need to succeed in the sector, and ways to get your foot in the door without a degree.

What skills are needed to be a data scientist? 

Successful data scientists carry a mix of both soft and technical skills, including (but are not limited to): 
  • Programming. Data scientists rely on a wide range of coding languages such as SQL, Python, C, C++, and Java; helping them organise and navigate large, unstructured datasets.
  • Math and statistics. The main goal of data scientists is to extract meaningful insights from raw data – making knowledge of probability, statistical analysis, linear algebra, calculus, and other mathematical or statistical elements essential. 
  • Business know-how. Most data scientists typically perform their tasks for the benefit of business outcomes, helping companies pinpoint areas for improvement in their marketing and daily processes. An understanding of business development (as well as the nature of your industry) is therefore necessary for success in this sector.   
  • Data manipulation and visualisation. Gathering data insights is just the first step of the job; the next stage typically involves presenting your analytics results in a clear, engaging manner. You must build familiarity with the various types of data visualisations (i.e. scatter plots; line, bar, and pie graphs; heat maps, etc.) as well the best approaches to data storytelling.
  • Machine learning. Data science is a field that often exists in tandem with AI – with the latter used to improve the processes of the former. Skills to develop and implement machine learning algorithms are thus in high demand in this sector, as they help automate the mundane processes of collecting, organising, and sifting through large datasets.

What are the benefits of working as a data scientist? 

With our data generation on an exponential rise (currently estimated to be about 2.5 quintillion bytes of data each day), recent years have seen a significant boom in demand for data science skills. Those aspiring to the field will thus enjoy a plethora of career opportunities – and with demand currently outstripping supply, plenty of employers are willing to pay a generous paycheque for those with the right experience. According to Payscale, the average data scientist earns about $92,123 AUD per year, with the potential to earn more as your expertise grows.  

Employment in this field also broadens your future opportunities for a field that’s only set to expand, as everything in our world grows ever-more data-driven. Plus, you’ll build skills that are transferrable to other IT jobs (i.e. cyber security, database administration, software development), diversifying your options across the tech sector.
To top it off, working in data science gives you the opportunity to make an impact in your industry. With the work of data scientists bringing about real change – whether it’s breakthroughs in tech or simply ways to expand one’s business – you’re bound to find long-term fulfillment in this highly rewarding sector. 

How to become a data scientist without a degree 

Pursue a qualification in data science

Entering the realm of data science doesn’t have to start with a degree. There are plenty other ways to get qualified in data science, such as through online course programs and certifications. These training options can help equip you with all the necessary aspects of data analytics – including areas of mathematics; statistics and probability; machine learning and AI; and essential coding languages such as Python, SQL, Java, and R.

If you’re delving into this field with zero tech knowledge and experience, online training options also typically have short courses available to help you get started in the essentials of the industry. Upskilled, for example, currently offers an ICTSS00109 - Short Course in Entry to Tech Skill Set, providing an overview of the basics in cloud computing, introductory programming, and ICT collaboration. 

Get involved in real world projects

With data science employers seeking hands-on experience among aspiring professionals, it’s important to also put your analytics skills to practice. This can be done by either pursuing your own projects (i.e. developing your own machine learning algorithms, pursuing the analysis of specific datasets) or by collaborating with others in the data science community. Plenty of online hubs such as Kaggle, Open Data Science, and GitHub are currently popular among those looking to enter and network with others in the data science field. 

Whichever you pursue, the end goal is to craft a professional portfolio that substantially demonstrates your expertise, creativity, and initiative in this sector. Undertaking internships are also a recommended way to build your experience while learning new skills in the actual data science industry.

Participate in competitions

Another common way of building practical experience is through data science competitions. Platforms such as Kaggle and DrivenData host plenty of these on the regular and are an ideal way to not only test your knowledge – but to also bolster your agility, adaptability and critical thinking skills. Many of these also result in a cash prize, along with the chance to conjure up effective data solutions to actual real-world problems.
Additionally, you’ll get to meet other like-minded professionals in the data science field as well as capture the attention of potential employers. With plenty of these competitions comprised of international participation, you’ll also have the  chance of expanding your professional network across the globe. 

Keep updated on industry trends

Finally, staying up-to-date on the latest data science trends and developments is crucial. As a sector that’s constantly evolving, it can often be easy to fall back on the latest breakthroughs, practices, and technologies – though this dulls your competitive edge for the job market. 

Keep yourself in the loop through publications, journals, and online communities in the industry, ensuring your knowledge stays fresh and aware of the latest skills demands. Of course, this also comes with continuous professional development; in a field as dynamic as data science, a constant desire to learn is critical. Keep your skills sharp through regular training and certification, helping you land lucrative, rewarding opportunities in the world of Big Tech. 

Upskilled offers a multitude of courses across the IT sector – from short courses in the fundamentals to diploma programs in cyber security and the cloud. Aspiring data scientists now have the option of pursuing our newly-launched data science course, a program that delves into all the basics required to launch a career in this rising industry. Best of all, with its online delivery, you can study at a pace and schedule that best fits your needs.

Explore the world of data science today, and enquire with us on the course. 

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