The history of data science dates back to 1962 when the US mathematician John W. Tukey wrote his book The Future of Data Analysis. There, he pointed out the shifting interest from statistics to data analysis as a more scientific approach.
Ever since the official field’s establishment over 20 years ago, it has captured the attention of almost all industries. The purpose of data science work is to provide insights to businesses and organizations regarding stakeholders’ data to make better and more informed decisions.
The modern definition of data science is a multidisciplinary field that combines the work of mathematics, statistics, computer science, and communication through storytelling. It’s essential to have the skills required for all these fields to qualify for jobs in data science.
What Do Data Scientists Do
Data science covers quite a range of disciplines, which may cause confusion about the exact tasks and responsibilities of the profession. Most people can’t tell how a data scientist is different from a statistician, a mathematician, or a software engineer. So, in the next section, we’ll elaborate on the job description and daily tasks.
Duties and Responsibilities
A data scientist’s roles tend to be broad, but the following list gives a general idea of their day-to-day activities:
- Identify and frame the problems that directly impact the business or the organization
- Acquire structured and unstructured data from different sources
- Use programming tools to store, clean, and process data
- Work with other people holding data scientist positions or similar roles
- Conduct an initial investigation and exploratory data analysis
- Choose models and algorithms to use on the data
- Apply data science techniques to perform an in-depth analysis
- Interpret the results and find solutions and opportunities
- Present the results to stakeholders understandably to non-technical individuals
- Make necessary adjustments based on feedback
Data Science Jobs Types
The term data science is so vast that it’s often used as a blanket title for several jobs with different roles. Mostly, they can be classified into four types — data analyst, data engineer, machine learning engineer, and data science generalist.
- Data science analyst jobs—These positions require collecting and cleaning up data and looking for patterns and trends in the data sets. Analysts should have a good grasp of programming and statistical tools. They also need to report to stakeholders.
- Data engineer jobs—This is another popular career path, as companies have to handle a lot of traffic and data coming in. Data engineers need to set up data infrastructure that can provide analysis simultaneously. This role requires expertise in statistics and machine learning.
- Machine learning engineer jobs—This career consists of feeding data into models defined by other scientists and scaling it out to production levels. They don’t necessarily have to understand the predictive models and the underlying mathematics.
- Data science generalist jobs—These are excellent entry-level data jobs. The position requires candidates to have basic knowledge in many fields like mathematics, statistics, programming, and machine learning.
When you consider the vision and goals for your career, getting acquainted with the different types of jobs will help you decide which path you want to take.
Fields of Application
Many hiring managers and companies have dedicated data science teams. The healthcare industry, environmental organizations, and the government are examples of fields that have incorporated data science roles into their structure.
Data science healthcare jobs are most useful for medical imaging. Professionals develop algorithms that can identify patterns and interpret images from MRIs, X-rays, and ultrasounds. That way, they process images with outstanding accuracy and can help notice tumors and other anomalies faster.
Today, environmental data science jobs are among the most fundamental and far-reaching career choices. Experts in the field use predictive analytics to forecast possible climate change problems. They also determine the direct consequences of policy decisions and lifestyle choices.
Data scientists can also identify which areas need immediate intervention to lower the global carbon footprint. That has proven most helpful to the government, which led to the increase in the number of data science government jobs.
For instance, the US Division of Homeland Security has employed data scientists to deal with big data. They have successfully integrated data from various security offices to anticipate potential dangers across the country.
The finance industry has also benefited from innovative and cutting-edge technology. The introduction of machine learning, artificial intelligence, and big data has improved fraud detection, decision making, customer data management, and customer analytics. All that led to the availability of more data science finance jobs.
Data scientists’ work environment differs depending on the field, the team role, and the employer. Professionals usually have a full-time schedule, with extra hours, if they need to finish a task or a project. The conditions often include:
- Long hours seated on a desk
- Reporting to the office for the use of computers, scanners, and other office equipment
- Presentation of findings and solutions to stakeholders
Some roles in information technology companies, such as Microsoft data science jobs, require collaboration with associates and other team members.
As more organizations are offering remote data positions in response to the pandemic, there are a few new characteristics:
- Reduced meetings and more focus on the technical part of the job
- Using coordination platforms to share ideas with associates
- Undefined boundaries of working hours and break time
How to Become a Data Scientist
It’s common for hiring managers and employers to scout for professionals with advanced degrees when trying to fill in data science jobs. For this reason, it would always be better to have a relevant master’s degree up your sleeve.
Careers in the data science field require a solid foundation in computer science or mathematics. A bachelor’s degree in either area is essential. The graduate can then continue to pursue a master’s degree in data analytics, data science, or a similar field, even though it’s not mandatory.
Still, it’s beneficial for graduate-level data science careers to have competencies in some sub-areas. That can be in predictive analytics, big data, statistical modeling, data mining, machine learning, enterprise analytics, and data storytelling.
To qualify for data science contract jobs, a candidate should possess the following core skills:
- Statistical analysis — the ability to identify patterns and anomalies in the data
- Machine learning — the implementation of algorithms and statistical models
- Computer science — the application of artificial intelligence, database systems, numerical analysis, and software engineering principles to data
- Programming — the use of languages, such as Java, R, Python, and SQL
- Data storytelling — presentation of insights to a non-technical audience
Data science careers also require soft skills such as:
- Analytical thinking — the ability to deal with complicated issues and find solutions
- Inquisitiveness — the skill to continuously try new methods to solve an issues
- Interpersonal skills — an aptitude for effective communication on all organizational levels
Unlike other jobs, data scientist careers require no licenses. However, it would be prudent to acquire certifications before applying for jobs to have a distinctive and competitive advantage. A couple of certifications to consider getting are:
- SAS Certification
- IBM Data Science Professional Certificate
- HarvardX’s Data Science Professional Certificate
Apart from getting certifications and earning a master’s degree, there are several ways for candidates to easily stand out and land jobs for data scientists:
- Create a portfolio — That’s one of the most effective ways to display your knowledge and skills. A portfolio can also be a substitute for job experience if you’re a recent graduate and looking for data science jobs for freshers. It should contain several medium-sized projects that exemplify your competencies in relevant areas.
- Write a blog — Data scientists need to educate themselves on the latest discoveries in the field to be competitive. Blogging can help them expand their knowledge and document all new findings. It can also be useful for improving communication skills.
- Expand their network — Candidates looking for data science jobs entry-level positions can benefit from networking. They’ll need good interpersonal skills when attending community events, and meeting established data scientists to increase their chances of getting referrals. It’s also a way to learn more about the field or particular employers.
Most data science jobs’ salary is quite high, even for entry-level positions. It’s one thing that moves people to pursue a career in the field. New hires receive about $85,075 annually, which is well above the median US annual income.
Seasoned data scientists receive an annual salary of $110,727 to $135,156. Like any other career, when an employee rises through the ranks, the salary increases as well. A director of data science can have an annual base salary of $118,000 to $204,000.
Data science jobs in the USA tend to have different median annual incomes depending on the city or state. For instance, a data scientist in Charlotte, NC, receives an average yearly salary of $86,709. In contrast, data science jobs in the Bay area have an average annual income of $125,281.
The job outlook for data scientists is favorable for the next few years. According to the Bureau of Labor Statistics, data scientist jobs will grow by 16% annually by 2028.
That’s considerably faster growth compared to all other occupations in the US. However, only 5,200 additional jobs will be added to the existing 31,700. That’s a smaller number than in most professions because data science isn’t a common field.Nonetheless, the data scientist job outlook shows it remains an in-demand occupation. From startups to Fortune 500 companies to government agencies, all are eager to fill in data scientist roles by highly-skilled candidates.