Data Science is perfect for you if you enjoy storytelling and solving complex problems with data.
By Mark P., Lead Data Scientist, Product Development DataCloud
As a Data Scientist at ADP, I use workforce data to tell stories, using curiosity to analyze and display the data. In this blog, I’ll share my observations of experiences and trends in the growing field of data science.
According to the U.S. Bureau of Labor Statistics, data science will continue to grow, and the number of jobs is estimated to increase by 28% through 2026. In other words, data scientists are in demand, and our role will continue to impact many industries.
What comes to mind when you hear “data science”? Numbers and graphs? Machine learning and big data?
Let’s dive into a quick definition.
What is Data Science?
My perspective on data science was shaped years ago. People started referring to themselves as data scientists and posting jobs for “data scientists” around the same time that machine learning with big data was spreading to industries and companies beyond tech.
I view data science as the methodical analysis of an extensive dataset to understand a subject of interest. Machine learning is a powerful means of such analysis, but not the only one. I focus on a different area, writing query code and dynamic calculations to produce interactive visualizations. To me, the significance of big data is more of a spectrum than a boundary. Science is a systematic study for understanding, and we can understand things with smaller amounts of data too. But big data like ADP has made the insights and applications deeper and more reliable.
Pragmatically speaking, data science can be whatever an employer considers it and communicates through the specific skills they seek. No definition of data science can replace an employer’s expectations, the candidate’s expression of their experience, and conversations about career fit and advancement. With evolving technologies and models, there are a growing number of opportunities in this career. As a Data Scientist at ADP, it is certainly rewarding to have occupational, organizational, and demographic facts on over 30 million US workers to explore – anonymized of course!
Top Trends in Data Science
Currently, two of the most visible trends in data science are cloud-based development and the advanced application of natural language processing (NLP).
Cloud-based platforms and services such as Amazon Web Services and Databricks make it easier to source data, develop analyses and models, collaborate with colleagues, and deploy products. We work closely with these partners and have often spurred innovation in their products as we expand our capabilities.
NLP has many current and potential applications in human capital management, including client support, occupation and skill classification, job posting development, and candidate recruitment. Since jobs are diverse, overlapping, and constantly evolving, building and maintaining comprehensive, systematic knowledge can be challenging. NLP can make our solutions more scalable and data-driven than classifications created by human experts alone.
Day in the Life as a Data Scientist
My research on restaurant employment and wages during the COVID-19 pandemic represents many common day-to-day components of data science work. While it is well-known restaurants were one of the most heavily impacted industries, ADP data shows some cities fared better than others. You can see this in the 18-month employment trends for 3 of the largest 50 US metros.
Visualizations like these are the tip of the iceberg: the most visible part of the work requires much more underneath. In addition to conceiving and developing metrics, models, and graphics to create knowledge, data scientists need to find good data sources and write code to retrieve and process their information. They need to understand the limitations of their sources – things like sample bias, predictive labels, outright errors – and communicate and correct them.
And data scientists need to query people as well as data! For example, interviewing local restaurant association executives for their expert perspectives and calling US Bureau of Labor Statistics economists to discuss statistical methods.
How can I gain experience in Data Science?
If you are interested in data science, you can find a ton of resources, including boot camps, online courses, Medium articles, and YouTube videos. If you look up #datascience on TikTok, it has 89 million views! Of course, classes are a great way to acquire vital education, but they can be a significant investment in time and money. You may wish to test your interest with a project that involves either a question you’d like to answer or a problem you’d like to solve. You’ll gain not only motivation but also a proof point to share with potential employers.
As an example, when 2020 presidential candidate Andrew Yang proposed a universal basic income, I was curious to know who might benefit from $1k a month and how to quantify the benefits objectively. I searched for household spending data, turned up relevant data and code from the Bureau of Labor Statistics, and then used free versions of SAS and Tableau to create a public dashboard to answer that question.
I’d advise anyone interested in data science to follow their curiosity and search the web for public data and free tools. You’ll face technical challenges along the way, but sites like W3 Schools and Stack Overflow can help you tackle them as they arise. Of course, many people prefer the structure of classes to an open-ended, “many-options-no-right-answer” type of project. The former is fine – but if you can take the leap and try the latter, you’ll gain a good experience of what real-world work is often like!
Final Thoughts
Data Science is a great option if you can:
Three self-examination questions for Data Scientists interested in ADP:
Interested in a career in Data Science? Let’s work together!
Learn more about working at ADP here and our current openings.
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