In this episode Jason talks to Lillian Pierson, CEO of Data Mania, an educational enterprise that helps emerging data scientists and leaders. Lillian is also the author of “Data for Dummies” and a LinkedIn Instructor who has taught over a million data enthusiasts through her courses.
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[01.01] Lillian’s background, and her mission in the data analytics industry.
[04:14] How Lillian’s book “Data for Dummies” helped her refine her mission from acquiring data skills, to helping people create a data strategy and find the ‘why’ behind data.
[12:05] Why Lillian enjoys creating educational content for all levels of expertise.
[15:16] What inspired Lillian to pursue a career in data
[17:33] How Lillian pivoted from being in data analytics, to data education and coaching
[23:30] Leveraging mentors and why everyone should have one to propel their career forward
[26:09] What is next for Lillian in the word of data
Today’s world is inundated with data. The amount of information available to us has grown exponentially and continues to grow. We are now able to explore the human genome, track how much we sleep each night, analyze the impacts that pollution has on our environment, and more.
Data science plays a pivotal role in analyzing this information and turning it into meaningful insights for people around the globe. Despite the need for so much data in the world, the pathways into data and possible career paths still remain relatively unclear. There are many different pathways one can take towards a career in data science; from getting boots-on-the-ground experience and transitioning into data from another field, or going towards a more academic route.
Data is a driving force that can be used to create and innovate new ideas, products, and services to better our society. However, not everyone has an idea of what they want to do with data or where they should start when looking for a career in data science. Sometimes, people may be so focused on their career that they don’t realise that their ability to grow – both professionally and monetarily – may be capped.
Look for opportunities everywhere to expand your knowledge and experience. The modern day market is more focused on who you are and what you can offer. You can present at conferences, write books or even pass on your knowledge to others.
Leadership in data science is not often brought up when considering different career paths in data. Data scientists are commonly thought of as the innovators that come up with new ideas for technology and tools, but what many people don’t realize is that there are also different levels of leadership within this field.
With any field, there needs to be leaders to help push innovation within their own companies and the industry in general. For example, a Chief Data Scientist has to oversee all of the work done by their team. They have to provide direction for each project and know how best to delegate tasks so they can get completed efficiently and successfully.
A data leader needs to have a strong understanding of the company’s goals and objectives, as well as an idea about where their company should be going in order to stay competitive in today’s digital world.
Data mentoring is a key component in an industry where innovation and hands-on experience is critical to success. This is where mentorship comes into play. It has proven time and again that it leads people towards better outcomes as opposed to being left alone in uncharted territory without guidance from seasoned professionals.
Data mentors are an invaluable resource for those who are just getting started or need guidance with their data projects. Contrary to popular belief, not all mentorship needs to be 1-on-1. There are many places where you can get the career support you need such as groups and industry meetups.
In the world of data science, skills are constantly changing and evolving. Mentorship can help you to develop and hone your skills while you expand your knowledge. Leadership on the other hand, can help others find their feet when faced with uncertainty about what they need to do next. In turn, those who work in the field of data should also lead by example so that others have something tangible to follow as an example for success within the field.