Last month I had an honor to participate in data science project reviews for the new graduates of General Assembly's Data Science Immersive program. In the span of just three months of full-time studies and endless nights of homework Chicago campus students mastered Python programming skills, machine learning, and modeling techniques; enabling them to solve real world challenges for their Capstone project. It is simply amazing to witness how GA’s accelerator empowered mid-career transformation for its participants. Indeed, given the non-traditional and often non-technical background of students (one of them was a Music major; another had Sports broadcasting and merchandising background; while the third did have a Statistics degree, yet very limited coding knowledge) their success seemed very unlikely. However, this cohort’s GitHub profiles and file depositories speak for themselves: they show a portfolio of projects (GA’s course required six of them), an accomplishment that makes them stand out from many other data science job applicants. Whenever folks talked about epochs, training vs. test splits, or their wanders in Random Forest, their eyes lit up and their enthusiasm was genuinely contagious.
Practical applications of capstone projects were as vast as students’ backgrounds: a simulation of a self-driving car, NBA match predictions, a music genre classifier and other. I’m sure that all of these newly minted data scientists will have a rewarding and fulfilling data science career. In fact, my interaction with these folks taught me some valuable career-change lessons. Whether we are bored at work, find our jobs too stressful, concerned about future career outlook, or simply stuck and burnt out; following the rules below can help make a successful transition to a more satisfying career:
1. Consider your interests.
Reach deep into your inner psyche and figure out what makes you tick and whether your current career is a good match. Quite often we don’t make rational career choices, whether pursuing passion and degrees that can’s support desired lifestyle of World travel, luxury living, and haute cuisine; or picking well-paying majors that result in resentful jobs. If you get excited about solving complex problems, overjoyed with access to new public data sets, or simply want to crack proverbial black box algorithms; then a career in Data Science shop top your list: data scientists are in demand, very well-paid, and the job will provide enough challenges to make your look forward going to work (or logging in from home for that matter.)
2. Test it out.
Data Science is traditionally viewed as an intersect of Business Acumen, Programming, and Statistics, basic coding skills are paramount before you start your data science journey. As part of the Data Science application process GA requires all prospective students to have some programming knowledge. In the age of Coursera, Udacity, edX, Codecademy and others, anyone willing to learn to code or run a statistical model could take a class of interest for free. This experience would help them determine if they have what it takes to do the job and continue with their chosen path If you prefer even more structured learning setting, GA offers numerous evening/full day course options that can help you get a better understanding of their approach and teaching methodology without committing to a 12-week program.
3. Learn more.
GA does a great job of hosting various information sessions, talks, lectures and webinars that help prospective students learn more about their chosen career track, ask questions, and ultimately decide if their interest is strong enough or whether this career is a great match for them. Educational providers like GA often take different approaches to training, and exploring your options with finding a provider that works best for your learning style is strongly encouraged here. Convenience, price, quality, time commitment, career assistance, and access to instructors and real-life practitioners - are some of the factors to consider here. One of the benefits of making a mid-career transition is the larger professional network, including either folks who work in data science, or work with data scientists, or even better – employ data scientists. This presents a great opportunity to interview people already in the field and gain their perspective.
4. Commit to learning new skills.
When you decide to learn new skills and are serious about making the next step, participating in an intensive bootcamp program is a practical choice. While this option is certainly faster than going back to school, it’s still a very large undertaking. You would have to devote most of your time (no time to work, limited time for personal life) to your education. During this period, you would still need to support yourself financially while paying your dues (GA does offer some Financial Aid options.) In short, it’s not enough to be mentally ready, you have to plan ahead and save some funds to cover your living expenses during your classes and subsequent job search. Once you are fully committed and take the class it’s important to take your education seriously: being an active class participant, completing your homework on time, collaborating with other students, reaching out to instructors and mentors; and above all polishing your skills and creating your work portfolio.
5. Make yourself found.
In addition to already mentioned GitHub portfolio, GA helps its students build a larger online presence consisting of effective LinkedIn profile and possibly professional website; all showcasing students’ skills that employers seek in their candidates. Networking events: both in-person meetups and online are strongly encouraged and often facilitated here.