The Evolution of the Data Science Career Path
With data becoming the new oil, the data scientist jobs are gaining immense traction. A few years ago, The Harvard Business Review (HBR) hailed ‘data scientist’ as the sexiest job position. HBR’s article is still spot-on as IBM predicts an explosion in the demand for data scientists (more than 300,000 job openings) in the US by 2020. Previously, we have covered how to become a data scientist. But, for the starters, it must be remembered that the data science career path is not a linear one.
As the novelist Jhumpa Lahiri puts it, there is a great deal of mystery in that word: “sexy”. According to her, the word “sexy” means “loving someone you don’t know”.
Do we really know who data scientists are, where they come from, and what they do? ‘Data scientist’ is a loose term, and it is therefore not surprising that you are struggling to find the right career track.
This is one of the primary reasons that has led to the mismatch between the data science job market and the online courses & MOOCs.
In this post, guest blogger Caroline Linne from MastersinDataScience.org will talk about the evolution of data science career path.
The Evolution of Data Science Career Path
By Caroline Linne
State of Data Science
Data science is a lucrative, rapidly growing field with many opportunities for people with the right skills. Organizations such as IBM, Glassdoor and the U.S. Bureau of Labor Statistics all report growing opportunities in data science. Though exact estimates vary, data science is a clear leader in job growth and opportunity. Growth of 20% to 30% from 2016-2026 is projected.
The many job openings available to data scientists come with extremely competitive and growing salaries, with median entry-level pay of more than $95,000, according to recruiting firm Burtch Works.
The expanding use of new techniques and technologies such as convolutional neural networks and natural language processing are enabling new applications of data science. The rapid decrease in the price of computing power, the availability of massive data sets and the prevalence of reliable, fast internet are enabling the use of data science in more industries than ever. These factors paint a picture of a future where data science is an essential element of nearly every industry.
These trends of sustained job growth, rising salaries, and new applications are expected to continue, making now a great time to begin a career in data science. It may seem daunting, but if you have strong analytical skills and enjoy programming, this field may be perfect for you. Here’s a look at the training, responsibilities, and opportunities in a data science career.
Education and Experience Needed
Data Science Degrees
Despite being a relatively new career, data science programs are rapidly becoming ubiquitous. There are many options for aspiring data scientists to gain the skills they need to succeed. These include certificate programs, online courses, and master’s degrees in data science.
Though a master’s degree in data science is not required for all data science positions, having a master’s degree is important to gain the skills and credentials for the best opportunities and to ensure upward mobility.
Though a master’s program may sound intimidating, there are options to fit many needs. Online, remote courses and flexible schedules allow working professionals to fit a graduate degree into their busy schedules. Many top-tier schools provide data science master’s programs. While some require on-campus participation, others can be done entirely online. Some programs offer a hybrid format, with on-campus and online components in tandem. This wealth of options helps lower the barriers to getting started on a career in data science.
In addition to education and certifications, data scientists need real-world experience. Many master’s programs offer final projects where students apply their learning. These projects can take many forms, from internships with large companies to independent research. These are a great start, but there are many more ways to gain practical experience and apply learned skills. Completing guides, tutorials or competitions online is another great way to gain experience. Tutorials on sites including Medium, Instructables, and Kaggle offer great starting points to explore applications of data science.
Online competitions provide quantifiable evidence of your skills, experience, and progress. Kaggle is an extremely popular site for data scientists working at all levels. It provides a community that encourages growth and development through projects and competitions. This is a great option for anyone interested in applying their data science skills to real tasks.
What Is It Like to Be a Data Scientist?
Data scientists work in a wide variety of industries and jobs. A data science degree can lead to positions with many different responsibilities and titles, including data scientist, data analyst, data architect, data engineer, statistician, database administrator, and business analyst. Roles can have significant overlap but also unique areas of focus and specialization.
Experience, training and personal skills influence which roles data scientists pursue. Master’s programs often allow students to specialize in a certain area of data science. Focusing on specific skills like data visualization, database management or advanced statistical techniques will direct a data scientist’s career in a certain direction.
All these roles share key responsibilities. Using databases, generating reports and running basic statistics are all integral tasks, requiring competency with statistical programming languages (such as R and Python), database languages such as SQL, and processing tools such as Hadoop or Jupyter Notebook. Data scientists will spend significant time extracting, transforming and loading data, as well as accessing this data to generate reports that summarize the content of these datasets.
How to Get the Career You Want
Being successful in a data science career requires more than a knowledge of statistical techniques and programming. The most important step to succeeding in data science is figuring out what you want. Data science careers can involve many different roles and responsibilities.
To be successful, it is important to know your strengths and weaknesses. While some weaknesses can be overcome through practice and education, embracing your strengths will help you get ahead. Developing valuable skills that you enjoy and where you excel will ensure success.
At the same time, it is important to have a broad understanding of data science topics. Being competent enough with the entire data pipeline to work independently is essential. This means you must be familiar with the tools used by data scientists to move from collecting data to making decisions. You don’t need to be an expert on every step. However, you do need to be competent enough to go through this entire process without someone holding your hand.
Most popular data science career paths – Machine Learning Engineer, Data Engineer, Data Scientist or a Data Analyst
Finally, data science is a rapidly changing and active field. To succeed, you must keep up with the trends. Taking courses on new packages, tools or concepts will keep you at the forefront of the field. Learning new skills will allow you to continually learn and apply cutting edge techniques to your problems. Actively learning and applying your learning is the best way to stay ahead in the field of data science.
Author Bio: Caroline Linne is an Editorial Coordinator at mastersindatascience.org.
Author: Tanmoy Ray
I am a Career Adviser & MS Admission Consultant. Additionally, I also manage online marketing at Stoodnt. I did my Masters from the UK (Aston University) and have worked at the University of Oxford (UK), Utrecht University (Netherlands), University of New South Wales (Australia) and MeetUniversity (India).
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