The Data Scientist Career Path: Everything You Need to Know

Nearly every type of organization — from government, to retail, to healthcare — needs data scientists. Data scientists organize and analyze raw data from various sources, enabling these enterprises to make informed decisions to ensure efficiency, boost profitability, and fuel growth.

Demand for data science professionals is expected to increase significantly in the next decade. The U.S. Bureau of Labor Statistics (BLS) estimates 22 percent growth through 2030, which far exceeds the 7.7 percent projected increase for all occupations. That translates to a need for an average of 3,200 data scientists each year through 2030.

A graphic showing how much more in demand data scientists jobs are versus all other occupations.

These positions (which according to the BLS earn a mean annual salary of $103,930) tend to be clustered in Maryland and Virginia, as data scientists are in high demand with the federal government. Data scientists are also in high demand in New York, California, Texas, and Washington state.

The demand for data scientists coincides with a marked increase in the sheer amount of available data. According to Statista, just two zettabytes of data (i.e., two trillion gigabytes) were created, copied, captured, or consumed in 2010, a number that is expected to increase to 79 zettabytes by the end of 2021, and then mushroom to 181 zettabytes by 2025.

These data increases are prompting organizations to seek highly skilled data professionals as they pivot toward data-driven decision making. For example, 92 percent of the executives responding to the 2019 MIT Sloan Management Review said they had begun investing more heavily in data and AI. That same year, Entrepreneur reported that companies that leveraged big data were 8 percent more profitable than those that did not.

Clearly, data scientists have a vital role — one that will only continue to increase in importance and value over time.

Data Scientist Career Path: How to Get Into Data Science

CareerOneStop indicates that 37 percent of data scientists have obtained their bachelor’s degree, usually in a field such as statistics, computer science, information technologies, mathematics, or data science. In addition, 35 percent of data scientists hold a master’s degree, and 14 percent have attained a doctoral degree.

Yet, some believe that a degree is not as crucial to career success as gaining early proficiency in programming languages such as Python, Java, and R, which can provide significant benefit in the long run. Carlos Melendez, COO and Co-Founder of the artificial intelligence and software development company Wovenware, stressed in an October 2021 Forbes piece that education should begin as early as elementary school:

“Every student, regardless of their occupation, will need to be data-literate to succeed in a world where data will increasingly be king.”

A data analytics boot camp will teach you the skills to pursue an entry-level data science role and to enter this exciting career. Such boot camps are short-term, intensive courses lasting three to six months and offer flexible scheduling, online coursework, and practical training.

To learn all these skills and more, check out Columbia Engineering Data Analytics Boot Camp, as it can serve as your gateway to an exciting, fulfilling career.

Data Science Requirements

Again, a solid foundation is essential, and Columbia Engineering Data Analytics Boot Camp can help you learn the skills needed to become a data analyst. These skills include learning Python, Java, R, MATLAB, and NoSQL. Additional in-demand skills include

Data visualization uses maps or graphs to give data visual context. LinkedIn Senior Content Marketing Manager Paul Petrone has likened it to “telling stories with insights gleaned from the data.”

Data cleaning is the process of removing data that is incorrect, redundant, corrupted, incomplete, or incorrectly formatted.

Machine learning uses algorithms to discern patterns in data sets and powers search engines, social media platforms, voice assistants, and the recommendation systems used by content providers.

Linear algebra/calculus are advanced math skills that are crucial for those in data science. Linear algebra has been called “the mathematics of data,” in that it has applications to machine and deep learning, and calculus is no less crucial in building algorithms.

Microsoft Excel, while not as sophisticated a skill as others listed here, remains important given its widespread popularity and usage within the field of data.

Soft skills like critical thinking and communication are also taught in data bootcamps. Melendez notes the importance of such skills in his most recent Forbes piece, as well as an earlier article published in July 2021. He lists empathy, teamwork, open-mindedness, and a business mindset as important soft skills, indicating that problem-solving has also become a vital skill as the pandemic has worn on and “the neat and orderly world of data scientists was turned upside down.”

Melendez’s point is that the data informing predictive algorithms may no longer be reliable at present. He offered an example illustrating the recent spike in visits to doctors’ offices, as COVID-19 began to wane in certain areas and patients could move about more freely. While such an uptick would normally suggest that customers are poised to change carriers, it is more realistically due to the fact so many people put off doctors’ appointments due to lockdowns or fear of exposure to the virus. As you can see, understanding the various causes behind consumer behaviors is crucial to being able to glean relevant insights from collected data.

In other words, a data scientist must consider data context and additional variables while also applying analytic best practices and common sense.

Data Scientist Career Path — Data Science Careers

While there are many different roles in the data science field (including software engineers, business analysts, etc.), the focus here will be on the data science career path.

As you learn how to become a data analyst, sometimes referred to as a junior data scientist, you will need a strong skill foundation to be successful. Applicable skills may include a proper math background, aptitude in data visualization and data cleaning, and familiarity with different programming languages.

Junior data scientists work on the more basic aspects of data analysis, including extracting, cleaning, integrating, and loading data. Focused mainly on predictive analysis, they often use pre-existing statistical models or work with specifications laid out by a more senior data scientist.

Those entering the data science field usually remain junior data scientists for a year or two before becoming mid-level data scientists. Mid-level data scientists enjoy greater autonomy with less frequent check-ins, and are expected to know how to perform exploratory data analysis and build the necessary statistical models for problem-solving.

In addition, mid-level data scientists may have the opportunity to work with senior data scientists in more advanced areas of machine learning and AI.

Individuals three to seven years into their data careers may qualify for a promotion to senior data scientists. While mid-level data scientists construct the statistical models that will solve problems, senior data scientists put that model to use in conjunction with other advanced tools. Moreover, senior data scientists are responsible for monitoring and fine-tuning an organization’s methodologies, while collaborating with key stakeholders and communicating the organization’s data insights to customers and company leaders. Senior data scientists are also responsible for mentoring junior data scientists.

Data science managers are responsible for the big picture — hiring the right people, establishing high standards, setting worthwhile goals, and understanding which KPIs are appropriate for the team.

As with managers in other sectors, the idea is to create a productive work environment while maintaining flexibility as products and industries continually evolve. A data science manager should be cognizant of new developments and prepare their team accordingly, as that will ensure their organization remains competitive.

Data science managers typically have at least five years of previous experience as data scientists, and many disciplines require one to three years of prior supervisory experience as well.

Data Scientist Career Path — The Future of Data Science

Data science job growth is occurring across a variety of industries every year. In fact, CareerOneStop is bullish on the future of data science, predicting a 31 percent increase in data science roles annually through the next decade. And, according to the U.S. Bureau of Labor Statistics, the top three states employing the most data scientists are California, Texas, and New York (respectively) with New York City being the top metropolitan area for data scientist employment in the U.S. While demand for data scientists is extremely high in these areas, these professionals are in high demand across the country and the globe.

Through the explosive growth in the Internet of Things (IoT) — i.e., wearable tech, smart home devices, baby monitors, etc. — more granular data will be generated to inform decision-making and provide additional insights. Moreover, with the ongoing rollout of 5G and its impact on data flow, as well as the potential of 6G bringing the advent of the “Internet of Everything,” the need for data scientists will only continue to increase. Consider the potential impact in the following sectors:

Transportation: Data is critical to the development of autonomous vehicles (AVs) because transportation-related information may soon be processed by vehicles rather than humans. Because AVs are such advanced forms of artificial intelligence (AI), they will require an exponential amount of data to function. If the technology reaches its full potential, one of the biggest benefits will be safer roads.

Data scientist Stefano Cosentino was hired by the German engineering firm Bosch in 2017 as part of the team developing autonomous vehicles. While he was uncertain of his role at first, over the next two years it evolved to the point where he was leading a 10-person team that contributed to the development of such vehicles by providing on-demand data analyses. In addition, Cosentino wrote on the website Towards Data Science:

“We have developed rule-based and probabilistic root cause analysis solutions to support the forensic team. We have created a feature bank that is enabling various ML projects. One is scenario identification, which we use for KPI estimation, verification and validation, as well as issue tracking. Another use of the feature bank is for anomaly detection.”

Healthcare: Some 30 percent of the world’s data is created by the healthcare field, and by 2025 it is expected to increase to 36 percent. Too often, however, this information is siloed, making it inaccessible to all who need it during a patient’s care journey. This issue — interoperability, or the ability of systems or organizations to share data — is an ongoing challenge, and one data scientists can help solve. This can be done by culling data from various sources (electronic health records, genomics, imaging, etc.) and analyzing it, thereby providing clinicians with insights that will enable them to personalize care.

Finance: So much of the finance field involves interpreting real-time data and forecasting future trends or market events. Technologies like artificial intelligence (AI) and machine learning (ML) are becoming increasingly essential to those processes, and data scientists use those tools to analyze and manage risk, leading to better decision-making and greater profitability.

Supply chain management: The global supply chain was already undergoing a digital transformation before the pandemic hit, but the outbreak of COVID-19 accelerated that trend; making the need for advanced technologies like AI, blockchain, and robotics more pronounced.

Data scientists in this sector use predictive analytics to make the supply chain more agile and efficient. This includes anticipating demand, determining where inventory should be positioned proactively to avoid out-of-stock events, determining the optimal network of manufacturers and storage facilities, and developing optimized routes for transporting inventory.

Columbia Engineering Data Analytics Boot Camp, based in New York City, offers learners the opportunity to gain in-demand data science skills via practical, real-world scenarios and professional instruction with flexible scheduling.

Data Scientist Career Path — Data Science Salary

Another appealing aspect of a data science career is the compensation. The mean annual salary for a data scientist in the U.S. is $103,930, according to the Bureau of Labor Statistics.

And, according to the BLS, the states with the highest mean annual salary were California ($129,060), New York ($124,240), and Washington ($118,320). The business sectors reporting the highest annual salary for data scientists include computer/peripheral equipment ($144,090), finance ($143,490), and merchant wholesalers ($142,300). As you can see, data science isn’t just an exciting and in-demand field, it’s also a lucrative career path!

A graphic illustrating the three states with the highest data scientist salaries.

New York City’s Columbia Engineering Data Analytics Boot Camp can help you to prepare to become a data scientist and jumpstart your transition into this exciting field.

Begin Your Data Science Career Path

A career as a data scientist can offer considerable opportunities and rewards. The need for these professionals is only growing on a national and global scale, with unprecedented growth in both the quantity and granularity of data, as well as the growing usage of that data to drive decision making and fuel AI and ML.

Columbia Engineering Data Analytics Boot Camp is the place to prepare to join this exciting field. Become one of the data science professionals on the leading edge of data discovery and change your future today.

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