Data science is in hot, global demand– and for good reason. Focused on the collection and analysis of valuable data, the field has spurred massive technological advancements (particularly in areas of artificial intelligence, machine learning, and big data), and currently plays a major role in business growth and development.
Australia, in particular, is projected to see a sharp employment spike between 2021 and 2022 – rising from 301,000 workers in 2016-2017 to a whopping 339,000.
For those with the right skills and passion, data science can offer plenty of career opportunity – with its demand only set to grow in time.
Below, we explore the steps in pursuing a data scientist role, and how Lumify Learn can help.
Know What to Study
Your first step in pursuing data science is to know exactly what to study. While varying advice will come from different experts, below are the three main areas of study any data scientist is expected to learn.
Linear algebra and programming are core element of data science, helping professionals solve complex data problems and manage large sets of information.
Of all languages, experts recommend prioritising Python and R. Python is one of the most widely known coding languages for being general-purpose, easy to learn, and having easily readable syntax. Due to its widespread use, data scientists needn’t worry about interfacing or compatibility issues.
R, on the other hand, is used for more statistical and academic purposes, helping data scientists carry out thorough analyses with ease.
Aspiring data scientists must learn to use popular machine learning algorithms to successfully gather and analyse large volumes of data. Through machine learning, professionals can accelerate data processing and more accurately extract predictions and meaningful conclusions from the information.
Simply put, the technology helps automate the process, helping ease the workload of data scientists while coming to more precise outcomes.
Finally, any aspiring data scientist must equip themselves with sharp skills in statistics.
Data analysis typically requires descriptive statistics, probability, inference, and understanding the concepts of selection bias in order to come to more optimum conclusions. Other key areas in statistics one may explore include regression, hypothesis testing, statistical inference, and experimental design.
One might consider probability and statistics to play the largest role in the field, as its these knowledge areas that make in-depth analyses and data predictions possible.
Take Up a Course
Once you’ve determined your key areas of study, your next step is to pursue a formal qualification. Most professionals start out with a completed undergraduate degree in a related field (i.e. statistics, information technology, or mathematics) before moving on to a post-graduate course – such as a Graduate Diploma of Data Science or a Master of Data Science.
According to Job Outlook statistics, those who hold a post-graduate or graduate diploma qualification make up a majority (45.4%) of those working in the field, with bachelor’s degree graduates a close second (44.8%).
However, there are also a wide variety of online programs in data science for those just entering the field. These courses provide thorough training in basic key concepts, skills, and practices, offering students the credentials to pursue further, higher education in the industry.
Lumify Learn’s Certified Data Science Professional course is one such program, equipping aspiring data scientists with the fundamentals of database, cloud, and AI concepts. The course is made up of four, globally-recognised Azure certifications from Microsoft, helping one build their core knowledge of the practice and how such skills can be applied to a Microsoft Azure environment.
Pursue Personal Projects
Formal study isn’t everything, however; as one requires substantial practical experience to succeed in the job market.
Employers will likely favour applicants with a robust portfolio showcasing their skills, so experts recommend pursuing a multitude of projects. Professionals typically begin with smaller bodies of work (i.e. creating machine learning solutions), before advancing to larger projects of a more focused area (i.e. a project predicting the stock market).
Having a set of completed projects under your belt not only enhances your value as a job candidate – it also allows you to put your knowledge and theory to practice. Getting out their and building solutions can deepen your understanding of how data science works in the real world, and helps you pinpoint any skills areas that may need improvement.
Additionally, it helps to connect with a community of other professionals. Getting to network with others in the same industry – whether in real-life or online – grants you the opportunity of learning from others and getting real-time feedback. This can also help you sharpen skills in presenting your final decisions or conclusions with others.
Online programming communities such as GitHub are great places to start when seeking others to collaborate or share projects with. You’ll also get to expand on your business connections – which may prove helpful when seeking employment.
Sharpen Your Soft Skills
Lastly, it’s important to focus on your soft skills as well as your technical, data-driven skills.
Data science relies on effective, excellent communicators, as professionals are expected to present and explain their findings in plain language. One must also be able to convince others of their conclusions by highlighting the business-relevant benefits of their decisions. Additionally, they must be able to break down the research process and how they arrived at the final outcomes.
It is this ability to communicate their analyses that allows data scientists to have an impact on their organisation and its future actions. The skill also goes hand-in-hand with complex problem-solving. Data scientists are expected to pinpoint issues or opportunities from their examined data, and provide appropriate solutions or methods in approaching them.
But it isn’t all about the technicalities of data science, either. Experts must also demonstrate a thorough knowledge of their specific industry or business; as this plays a key role in making effective decisions for organisational growth and development.
Finally, professionals in the field are expected to be lifelong learners – able to keep pace with constantly changing consumer needs, market trends, and technological developments. Aspiring data experts may need to constantly reskill their analytics capabilities through courses and training programs, as required by industry demands.
Get Started On Your Data Science Career With Lumify Learn Today!
As mentioned, Lumify Learn currently offers aspiring data scientists with an online Certified Data Science Professional course – offering students an in-depth look at the fundamentals of cloud data, databases, artificial intelligence, and machine learning solutions. Comprised of certifications under Microsoft Azure, students can graduate with globally-recognised credentials often highly-valued by employers.
Additionally, the course offers weekly webinars for the opportunity to connect with key figures and other like-minded professionals in the industry. Through completely-online modules, one can also tailor their studies according to personal needs and schedule.
Get started on a rewarding career in data science today – and enquire with us on a course.