Associations today are wrestling with how to sort out an unnecessary measure of different information.
The capacity to change an ocean of information into noteworthy experiences can have a significant effect—from anticipating the best new diabetes treatment to recognizing and impeding public safety dangers. That is the reason organizations and government offices are hurrying to recruit data science experts who can assist with doing exactly that.
By extrapolating and sharing these bits of knowledge, information researchers assist associations with tackling vexing issues. Consolidating software engineering, demonstrating, measurements, investigation, and math abilities—alongside strong negotiating prudence—data scientists uncover the responses to significant inquiries that assist associations with settling on target choices.
Data Scientist Role and Responsibilities
Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. They design data modeling processes, create algorithms and predictive models to extract the data the business needs, and help analyze the data and share insights with peers. While each project is different, the process for gathering and analyzing data generally follows the below path:
2. Acquire data
3. Process and clean the data
4. Integrate and store data
5. Initial data investigation and exploratory data analysis
6. Choose one or more potential models and algorithms
7. Apply data science techniques, such as machine learning, statistical modeling, and artificial intelligence
8. Measure and improve results
9. Present final result to stakeholders
10. Make adjustments based on feedback
11. Repeat the process to solve a new problem
Common Data Scientist Job Titles
The most common careers in data science include the following roles.
Data scientists: Design data modeling processes to create algorithms and predictive models and perform custom analysis
Data analysts: Manipulate large data sets and use them to identify trends and reach meaningful conclusions to inform strategic business decisions
Data engineers: Clean, aggregate, and organize data from disparate sources and transfer it to data warehouses.
Business intelligence specialists: Identify trends in data sets
Data architects: Design, create, and manage an organization’s data architecture
Although the roles of data scientists and data analysts are often conflated, their responsibilities are actually quite different. Put simply, data scientists develop processes for modeling data while data analysts examine data sets to identify trends and draw conclusions. Because of this distinction and the more technical nature of data science, the role of a data scientist is often considered to be more senior than that of a data analyst; however, both positions may be attainable with similar educational backgrounds.
Data Science Career Outlook
By many accounts, becoming a data scientist is a highly desirable career path. For five years in a row, Glassdoor ranked data scientists as one of the 10 best jobs in America, based on median base salary, the number of active job openings, and employee satisfaction rates. Likewise, Harvard Business Review called data science “the sexiest job of the 21st century,” noting that “high-ranking professionals with the training and curiosity to make discoveries in the world of big data” are in major demand.
From startups to Fortune 500s to government agencies, organizations are seeing the value in capitalizing on big data. Google’s Chief Economist Hal Varian spoke about the need for data scientists back in 2009, telling McKinsey Quarterly, “the ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades.”
This prediction proved prescient. A report by LinkedIn ranked data science as one of the top emerging jobs in 2020.
The United States Bureau of Labor Statistics concurs, stating that employment of all computer and information research scientists is expected to rise 16 percent by 2028—a rate of increase that exceeds many other professions. Yet, data scientists are relatively scarce, meaning it’s now an opportune time to upskill and enter the field.
Data Scientist Salaries
According to Robert Half Technology’s 2020 Salary Guide, data scientists earn an average annual salary between $105,750 and $180,250 per year. However, compensation can vary depending on location. For example, average salaries in cities across the United States include:
Seattle: $108,399
New York: $101,387
Boston: $101,064
Los Angeles: $99,014
Austin: $96,495
Atlanta: $91,049
Washington, D.C.: $89,738
Chicago: $88,758
Charlotte: $87,306
Additionally, as data scientists gain experience, they often move into more senior positions with higher pay. These include:
Data Science Manager: $135,401
Data Science Director: $157,273
Essential Data Science Skills
Most data scientists use the following core skills in their daily work:
Statistical analysis: Identify patterns in data. This includes having a keen sense of pattern detection and anomaly detection.
Machine learning: Implement algorithms and statistical models to enable a computer to automatically learn from data.
Computer science: Apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.
Programming: Write computer programs and analyze large datasets to uncover answers to complex problems. Data scientists need to be comfortable writing code working in a variety of languages such as Java, R, Python, and SQL.
Data storytelling: Communicate actionable insights using data, often for a non-technical audience.
Data scientists play a key role in helping organizations make sound decisions. As such, they need “soft skills” in the following areas.
Business intuition: Connect with stakeholders to gain a full understanding of the problems they’re looking to solve.
Analytical thinking. Find analytical solutions to abstract business issues.
Critical thinking: Apply objective analysis of facts before coming to a conclusion.
Inquisitiveness: Look beyond what’s on the surface to discover patterns and solutions within the data.
Interpersonal skills: Communicate across a diverse audience across all levels of an organization.
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