How Data Helped Me Choose a Career in Data
Choosing a data-driven career path was the best decision I could have made.
My data career is something that I built from scratch, and I never knew that I would end up in this field. It is the pinnacle of my work and truly fulfils me intellectually, spiritually, and financially. This career surprised me — because I never thought it would be my dream job.
It's surprising how life sometimes works, as you never know where you'll end up and what work you'll do in the future. From my teens to my 20s, I was building my career to become a biology researcher, and I rarely thought of a job outside of research life. I loved the research work and felt I would be nothing without it. I was content with everything because my environment supported my goals. I felt I would make a great biological researcher — although life always has a different plan.
In this article, I want to share my experience, my thought process in deciding on a data career, and how using data could help me determine my career steps. Let's get into it.
Finding a New Passion
My path towards a research career was thorny because I pursued a very niche major and struggled financially. The competition was also fierce because research positions were rarely open. Amid these challenges, I realized another truth — I wasn't the brightest researcher in the room. I had a lot of passion for research, but it didn't translate well into biological research; either I lacked creativity, or my writing wasn't up to par. I could never secure any research position after my Master's degree.
With not much going on in my life, I tried to find a new passion in my work because I felt that research was something I could do, but perhaps not in academia. That's why I decided to research to find my new passion.
Researching a New Career
I know what I'm good at — I excel at analyzing data during my research and am passionate about any work I undertake. In this limbo period when I couldn't engage in research work (such as pursuing a PhD, working in a laboratory, etc.), I felt the need to do something I hadn't done before applying for a job in the industry and learning new skills. However, I didn't want to go in unthinkingly, so I researched the best careers for the future.
The above graph represents my research. Essentially, I was trying to find data regarding the fastest-growing careers. What would be the next big thing? If I wanted to switch careers, it needed to be closely related to what I already knew but not become a dead-end, like my previous career choices.
My research findings indicated that many future career options were related to energy, health, or data. The point was out of the question as it wasn't within my expertise. Health was somehow related, but the job titles were too specific for me. Finally, the data field seemed worth further exploration.
Future of data career
First, I researched how valuable data skills would be in the future — would they be necessary for a company? Would they advance my career? And would they be hard to learn? One of the research summaries I found provided insights into all these questions. Essentially, I discovered that:
More than 80% of executive respondents said that data skills would be valuable to their company,
81% of respondents mentioned that data skills were necessary for senior leadership positions,
The demand for data experts is increasing each year.
The above research suggests that a career in data will be valuable in the future and promises a brilliant job, as both demand and top-level roles will require data skills.
I also sought additional research sources to support the idea that a data career is the future. Another World Economic Forum 2018 study found that 73% of respondents expected their enterprise to adopt data technology. By 2022, 85% of respondents planned to have expanded their adoption of user and entity big data analytics. These results are promising as the research indicates that established data roles would be developed.
Many studies suggest that a data career is a good choice, and I've been convinced that data is the future in every industry. However, given the many job titles for data careers out there, I didn't have any idea about a suitable data career for me. That's why I looked at another research study that categorized data job titles and discovered that 'Data Scientist' was at the top of the chart. With that in mind, I felt that Data Scientist was a role I would consider for my future career, but what exactly do data scientists do, and what skills do I need?
What Data Skills?
While I have established that a data career has a bright future and that 'Data Scientist' is the title I'm aiming for, what data skills do I need to break into this career, and what does a data scientist do for a living?
For starters, I referred to research by CustomerThink to identify the data skills required for a career in this field. Interestingly, this research divides the skills depending on job roles; however, only two parts within a data career emphasize data proficiency: Business Manager and Researcher. Most of them require the following skills:
Statistics / Statistical Modeling
Big and Distributed Data
Machine Learning
Bayesian Statistics
Data Management
Algorithms
Coming from a research background, I identified some of the skills above as my areas of expertise, such as Statistics and Bayesian. However, I lack skills like Algorithms, Big Data, and Data Management. Nevertheless, I still feel this is my career because I hadn't realized before the research I conducted that there were positions in the industry requiring such a high level of statistical knowledge, an area I'm good at.
Next, I sought research to tell me what data scientists do daily. In this case, I found an insightful study by Forbes that clarifies what most data scientists do during their employment. The results are shown in the following picture.
I was initially surprised by these findings. In my mind, data scientists would be deeply involved with data by analyzing it and developing statistical models, which I mostly do during my research. However, the industry seems to follow a different pattern than academia, with most of a data scientist's time spent cleaning and organizing data.
I was intrigued: why would data scientists need to spend so much time cleaning data? Many sources explain why data cleaning is necessary, and most say it's done to improve data quality — which is logical.
Would I want to spend most of my time cleaning datasets? The answer, at this current moment, is yes. But at that time, I was still uncertain about this activity. Nevertheless, I knew I wanted to transition to a data career because all the evidence pointed to excellent prospects, which aligned with my previous work. That's why, after all this research, I decided to 'get my hands dirty' by taking online courses and attending boot camps, but that's a story for another time.
Key Findings
From the research I conducted to find my new passion, I discovered some key insights that significantly influenced my career decision:
The data field is one of the fastest-growing occupations.
The demand for data experts will increase, and data skills are required at the senior level.
'Data Scientist' is the most sought-after title in the data field.
There are six skills I need to learn to succeed in the data field: Statistics / Statistical Modeling, Big and Distributed Data, Machine Learning, Bayesian Statistics, Data Management, and Algorithms.
Most of a data scientist's time is spent on data cleaning.
Conclusion
Finding a new passion is difficult, especially when I've spent my entire education training to become a biological researcher. However, I needed to face reality and seek a new career passion because my previous experience led to a dead-end career-wise.
That's why I utilized what I was good at — analyzing data. I used data to find my new career and discovered that a job in data is one of the fastest-growing occupations, and the required skills align closely with my previous experience.
With all the data showing promising prospects and not a dead-end career, I decided to pursue a career in data and start everything from scratch in this new field.
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