Learn the truth behind the most common myths about data science careers
The amount of data being generated by research, data mining, and online activities is increasing exponentially. This makes the presence of individuals with the knowledge and skills necessary to interpret this data and the insights it holds more important than ever.
For many, however, the prospect of a career in data science (or of making use of data scientists for your own organisation and initiatives) is somewhat daunting. How does one become prepared for this career? Is data science a sustainable field, or a passing fad?
We’ve compiled 3 of the most common myths about data science careers and have debunked them below, one by one.
Myth #1: An Advanced Academic Background is Needed to Become a Data Scientist
A common stereotype about data scientists is that they are all former professors or academics who just needed a way to move into the private sector. While this is certainly true for some, you do not need a PhD to become a data scientist. Data scientists have a range of academic and vocational backgrounds from business development, to linguistics, to computer programming to biology. What matters most is having a knack for analysis and a desire to learn about new quantitative systems, tools, and languages. Data science training, such as that offered through The Southampton Data Science Academy’s online Fundamentals of Data Science (Technical) course—which is CPD-certified—can help you hone the analytical and technological knowledge and experience you already have, helping you to succeed or move forward in a data science career.
Myth #2: Data Science and Analytics Will All Be Performed By Machines, Soon
We hear it all the time: “Automation is coming for your job!” This isn’t just a concern for those in the manufacturing sector; artificial intelligence and machine learning are quickly becoming more practical and less futuristic. For those interested in data science career development, this can make it unclear whether pursuing this type of education and career is a smart move in the long-term.
Machines are getting smarter, but they can’t replace experience and judgement
However, data scientists don’t work like machines. Although they certainly spend a lot of their time processing quantitative information, useful and insightful data analysis often requires the human touch. Careers in data science are taking off because data scientists serve as critical connecting points between the increasing capabilities of computers and machines and the human use for these capabilities.
Myth #3: Pursuing a Career in Data Science Means Becoming a Business Analyst
Finally, many individuals with the necessary talent and interest in data science do not meet their full potential in this area because they assume that data scientists are just ‘quant’-focused business analysts. Although data scientists absolutely can employ their skills as business analysts, there are many other roles and industries that also require data science. In fact, there is a cross-sector data science skills gap: according to a report in The Huffington Post, an average of 56,000 data-related jobs will be created every year in Britain until 2020, and by 2018 the USA will be faced with 140,000-190,000 unfilled data science positions. This demand comes from many industries, including tech, governments, education, retail, and more.
Data science skills can uncover many career options for you
In addition to the obvious applications of data science in the corporate sector, the healthcare, media, tech, and entertainment sectors all use large-scale data analysis on a regular basis to better understand how to meet the needs of the people they serve. With a data science course under your belt, you can demonstrate your value to employers across multiple sectors and ensure that you are in the best position possible to pursue a robust, fulfilling career in data science.
Are you interested in learning more about data science and analytics?
Contact The Southampton Data Science Academy for details regarding our Fundamentals of Data Science (Technical) course.