If you are looking to transition your career to data science, the most common advice you may have heard is to learn Python or R, or to learn machine learning.
Here are 7 reasons why switching your career to data science will be your best career decision yet:
1. Data science career prospects are high because it’s a new industry with many career opportunities
Data science jobs are still new to the industry and majority of them did not exist five years ago. The modern big data industry is different to older professions such as Law, Accounting, Actuarial Science or Finance. These professions have established standards, institutions and strict career processes.
The big data industry on the other hand, is constantly evolving based on the needs of businesses and their data centric project requirements. The new projects and divisions require multi-skilled individuals from diverse industry backgrounds who have been trained with latest big data technology.
In just the last two years, the big data industry has rapidly adopted new technologies and innovative techniques. This means new data science graduates are equipped with the latest skills and practical experience to help businesses utilise their data. Many businesses are making teams redundant due to automation; while on the other side, employing large teams of data professionals to build new smart technologies. This trend is increasing year-on-year as businesses are learning how to utilise their data for their operations, products and strategy.
2. The career outlook in data science is worth a career change or transition to analytics
The career opportunities in data science are advancing quickly with IBM predicting a 28% increase in demand for data scientists. The ability to analyse, manage,and predict data are skills that are valuable across all current industries, verticals and new innovative businesses.
The industry needs more data professionals and the talent is likely to come from a multitude of professions and backgrounds. Industries need professionals to upskill in this area because these professionals already have the domain expertise to apply data-driven technology and techniques. This means there is an opportunity to transition into a data science career even if you are not from a big data or analytics background.
You could switch career paths into data science from:
Professionals from all backgrounds have successfully switched careers into data science, big data and AI.
3. There is a clear career path and career track on how to change careers to a data scientist
The first step to start a career in data science is training to be a data scientist. The Institute of Data has a range of programs specifically designed to help accelerate your learning and job placement in the big data industry.
To fast track your data science career path and excel within the industry you will require:
- Education & Certification – you need to become educated in data science and analytics processes through an industry recognised course
- Industry Networking – you need to meet and build industry contacts
- Work Experience – you need to gain at least 2 years of practical industry experience working as a Junior Data Scientist or Data Analyst
- Data Scientist Job – once trained, certified, and experienced, you will possess the successful makings of a Data Scientist and will be earning a six-figure salary (AUD$125k+ in 2018). You will have a sought-after skill set – making you a valuable asset to any business you work for.
4. Training to be a data scientist is the fastest career progression to switching career paths
The fastest way to enter the data science & AI industry and become a valuable asset to any company, is to get trained.
If you have the skills that can help industry leaders solve their data related problems at a time where there is a growing need for data science professionals, you will have the advantage of fast career progression and being one of the in-demand data professionals that can help businesses improve efficiency and grow.
Some of the skills that may help when switching career paths to data science include:
- Being able to code or willing to learn – Python, SQL, R
- Data Visualisation & Communication – presenting data trends to be easily understood
- Problem-solving mindset – ability to come up with data-driven solutions
- Knowledge of Experiments – being able to run tests, design experiments, evaluate outcomes
However, even if you don’t currently have any or all of these technical skills, there are education programs available for any professional interested in a career in data science, or anyone looking to upskill in data science.
5. Switching career paths from accounting to data science or moving from finance to data science are valid career options
Moving from a career in finance or accounting to a career in data science is a strategic long-term career move that will pay dividends in the future. The skills and processes you are currently utilising and implementing on a daily basis are a great foundation to become a Data Analyst, Data Scientist, Segment Leader, AI Machine Learning Researcher/Practitioner, or a Business Analyst. Employers are interested in professionals with previous career backgrounds who have developed domain expertise and/or the softer skills of the workplace.
So, if you make the switch, you will be adding qualifications and skills to your existing career. You will utilise your previous skills in a data science job. You will have the advantage of becoming an enhanced data science professional with the aptitude to help businesses become data-driven. You will deliver real outcomes to businesses by using your full breadth of skills in accounting/finance and newly acquired data science skills.
6. A mid career switch to data analytics will likely have additional career options or opportunities than staying in your current role.
There are many benefits to a mid career switch to data science and analytics but here are 3 of the most alluring aspects of a career in data science:
- In-demand Skill Set – You will learn new practical skills and techniques necessary for a career in data science. This will enable you to become an in-demand professional and re-energise your career and career prospects, while also enabling you to make a real-time impact in your day-to-day projects.
- Increased Network – You will be learning alongside like-minded professionals from diverse industry backgrounds looking to progress their careers, which is an in-built opportunity to network and secure future business contacts and future collaboration opportunities.
- Earning Potential – Due to the growing global industry shortage of trained and experienced data science professionals, starting salaries in big data range from AU$60k-$90k – with experienced data science roles averaging of AU$125k+ salary. The global shortage means there will also be international opportunities available to you as a data scientist.
7. No-one is too old to be a data scientist because the industry needs experienced professionals to start a career in data science
When it comes to joining the data science & AI community, it is inclusive and welcoming to those willing to put in the hard work to learn the skills required to successfully lead a career in data science. The big data industry values professionals with experience, existing knowledge, a desire to learn and implement ideas, the ability to think analytically and innovatively, and those that demonstrate a proactive work ethic to make data-driven decisions for businesses.
These qualities are valued regardless of age, gender, or previous professional background. So in short, no-one is too old to be a data scientist or to make a career change to data analytics. In fact, if you are a more seasoned professional your previous learned experience combined with your industry knowledge will only amplify your capabilities as a data scientist.
Here are 5 more myths about a career in data science that may be stopping you from achieving your full potential in the big data industry.
A career change to data science requires motivation for career progression and training to be a data scientist. To successfully switch career paths to data science, you need to be focused and willing to learn new skills while adapting to the new profession of the data science industry. Career opportunities in data science are rapidly evolving and the need to upskill professionals into the industry is constantly increasing.