Data Scientists work with basic and advanced techniques such as machine learning algorithms, to uncover patterns from within large sets of data. This process helps Data Scientists uncover actionable insights to share with stakeholders. Jobs in nearly every sector require those with data science training. Data Scientists play an integral role in finance, healthcare, government, retail, and web development. Data Scientists receive high pay for their contributions to their organization. As businesses continue to gather data, the need for those with the skills to analyze it and find insights is likely going to continue to grow in the coming years.
There Are Ample Career Opportunities in Multiple Industries & Sectors
Choosing a career in data science opens a range of career opportunities beyond Data Scientist. Many popular sectors such as marketing and advertising, banking and finance, and healthcare, all rely on those with data science training to bring forth valuable insights. In the banking, financial services, and insurance (BFSI) sector, Data Scientists assist with tasks like detecting fraud and providing clients with customized financial advice. Additionally, machine learning algorithms help with stock trading and risk analytics. Those who work in this sector also can apply data science skills to auto insurance, wealth management, credit reporting, cybersecurity, and Software-as-a-Service.
In the advertising and marketing sector, those with data skills can use their background to create ad copy that targets a specific audience, as well as to make personalized recommendations for services and products. Data analytics helps these professionals leverage various social media platforms as well. With the help of programming languages like R and Python, marketing professionals are able to make recommendations based on data to increase profits and reach more customers.Â
Within the healthcare sector, Data Scientists create algorithms that lead to better patient care. Data analysis also has applications for medical imaging, which leads to more accurate diagnoses and better courses of treatment for patients. Data also helps Doctors provide better disease treatment through predictive medicine. By applying machine learning algorithms to clinical trials, imaging studies, and drug efficacy analysis, doctors are able to provide patients with better care when they are sick, as well as prevent illness from occurring.Â
In web application and development, the prevalence of data science has created a corresponding need for AI and machine learning. Before a new application is released to the public, Data professionals often collaborate to review and analyze data on its efficacy and popularity. Its utility in this sector has led data science to be a crucial component of the entire development lifecycle.
Data Science is a High-Paying Career
Data science tools and techniques are used in a range of professions with high-paying salaries. Not only can data science be a lucrative profession, but there is currently a low supply of data science workers. Despite the fact that there are many data science positions currently available, there are not enough individuals with the skill set needed to fill these roles. This means that those who have training in computational and quantitative data skills, as well as a background in data storytelling and clear communication, will have a competitive advantage when searching for open positions. This section will examine some of the most popular fields where data science skills are needed, as well as the corresponding job requirements and salaries for each role.
Data Analysts gather, clean, organize, analyze, and visualize vast amounts of data. To find the needed information, these professionals query databases, as well as create and modify algorithms that gather data from large datasets. Data Analysts work with automated tools to extract data from primary and secondary sources. After performing data analysis, they create reports that offer recommendations that can be shared with other internal or external stakeholders. Data Analysts also use their training to forecast any trends that may impact their organization or the services or products it offers. They work with a range of data tools such as Python, R, SAS, SQL, Excel, and Tableau, to perform these tasks. On average, Data Analysts in the US earn $80,000 a year.
Data Engineers are involved with creating and testing data ecosystems for their organization to ensure they’re stable and fully optimized. These professionals also update the organization’s existing system with upgrades and newer versions of technology to help the system perform as efficiently as possible. Their daily tasks involve gathering data, performing primary and secondary research, and locating patterns from within the data that can be used to forecast trends. Data Engineers then create reports based on their analytics findings and share these with stakeholders. These professionals make an average yearly salary of $115,000.
Those who work as Data Architects are tasked with designing blueprints that are used to manage, integrate, centralize, and protect databases. These professionals also provide Data Engineers with the most current systems and tools necessary to perform their job. These individuals use their background in data to create and implement a design strategy that adheres to their organization’s needs and goals. They plan end-to-end data architecture, find data collection sources, maintain database security, and audit data management systems to improve their performance. To perform these tasks, Data Architects use tools like Pig, Hive, and Spark, as well as ETL, data warehousing, and data modeling. The average salary for a US-based Data Architect is $155,000.
Machine Learning Engineers are one of the most in-demand professions in which data science skills are necessary. Those who work in this profession must have a range of skills such as knowledge of Python, Java, A/B testing, REST APIs, SQL, data pipelines, and machine learning algorithms like clustering and classification. Machine Learning Engineers research, create, deploy, and test machine learning systems, make products to satisfy the needs of clients, extend machine learning libraries and frameworks, train retraining systems, apply statistics to machine learning, and visualize data findings. The average yearly salary for a Machine Learning Engineer in the US is $152,000.
Database Administrators ensure an organization’s databases are functioning properly and that the proper employees have access to it. These professionals also perform database recoveries and backups. They store and manage data on database software and optimize how the database is designed and deployed. Database Administrators also integrate security protocols to keep databases safe from threats. They perform data archiving and create documentation and reports to reflect their efforts. Database Administrators rely on a background in data modeling, database backup and recovery, database design, and other tools to perform their professional role. These individuals earn an average of $105,000 annually.
Another profession in which data science skills are required is Statistician. These professionals must be well-versed in data organization as well as a range of statistical theories. They collect data and provide their organization with key insights based on their analysis and interpretation. In addition, Statisticians also design new methodologies for Engineers to use. Statisticians must have more than a logical mindset; they must also be experts in a range of database systems, like data mining, SQL, and machine learning technologies. The current average salary in the US for a Statistician is $86,000.
As the name implies, Data Scientists work with a range of tools and skills to process, analyze, and interpret data. These professionals must be able to understand an organization’s needs and challenges to provide it with the best options. To do so, they run predictive analysis to find actionable insights. These individuals also locate patterns and trends that help their employer make more informed decisions. On a daily basis, Data Scientists collect, clean, and integrate data. They also automate the process of gathering and managing data. They provide their organization with recommendations and reports that convey data findings. To perform these tasks, Data Scientists rely on tools like Python, machine learning, R, SQL, and MatLab, as well as a background in mathematics. US-based Data Scientists earn approximately $152,000 a year.
This Profession Offers a Range of Freelance Opportunities
Not only are Data Scientists in-demand for in-house positions in a range of industries, but these professionals also have ample freelance opportunities. Because Data science is largely IT-based, those who work in this field generally do not have to be physically located in their place of employment. So long as they have access to a computer with a strong internet connection, Data Scientists can typically perform their jobs remotely from any location.Â
Freelance Data Scientists don’t have to work for just one employer. Often, Freelancers are hired to work on one project or job and are paid according to the decided-upon bidding amount. This allows Freelance Data Scientists to work on a range of projects as their schedule permits and to have the flexibility to work remotely on an as-need basis. As the need for qualified Data Scientists continues to grow, it is expected that more freelance opportunities for qualified data professionals will be available in the near future.
Data Science Can Add Value to an Organization
In addition to earning a competitive salary, Data Scientists also contribute to their organization in meaningful ways and can make a lasting impact. These professionals provide a range of services, depending on their employer and line of work. They can detect fraud, find early-stage tumors in patients, improve shipping routes, and help athletes perform to their peak. This section will explore a few of the ways Data Scientists help bring value to their organizations.
Thanks to data science and the process of gathering and analyzing data from different sources, gone are the days when organizations must take high-stake risks in the dark. Data Scientists use existing data to design models capable of simulating a range of possible actions. This allows organizations to predict outcomes before taking financial leaps and make data-driven decisions about which path is most likely to result in the best outcome for their organization as a whole. Not only does data science help improve the decision-making process for an organization’s future endeavors, but it also has applications for evaluating how successful these measures were once they were implemented. This helps companies remain aware of how the decisions made have affected the well-being of the organization in the immediate and long-term future.
In industries where products or services are sold, data science skills come in especially handy. Whether it’s collecting data from customer surveys or Google Analytics, many organizations gather customer data from at least one source. However, when that information just sits there or isn’t leveraged to its full potential, it has no utility. Data Scientists help their employers use existing data that may not be valuable on its own and combine it with various other data points to yield insights that can be extremely valuable about the target audience of its products or services. Additionally, data science aids with precisely pinpointing these various audiences, which allows organizations to customize their offerings to those most likely to purchase them.
Within an organization, ensuring the employees are well-trained using the available analytics products and software is essential to its success. In some industries and jobs, Data Scientists are relied on to demonstrate how effective a system or tool is for extracting valuable information and fueling action. The greater number of staff members who are fluent with programming languages, software, and other data-related products, the more effectively the business as a whole will run. Additionally, ensuring proper employee training frees up their time so they can focus on the most pressing business challenges rather than attempting to learn the software.
Those with a background in data science can do more than contribute to an organization’s bottom line or product offering; they can also help an employer determine which job candidates are the best match for their needs. In the past, Recruiters would spend many hours each day reading through resumes. However, the prevalence of big data is changing this approach. A wealth of information is available on job applicants in corporate databases, on social media, and via job search websites. Data Scientists can leverage these data points to focus on the applicants who are the best fit for the organization’s needs. To do so, they must mine huge stores of already available data, including resumes and applications, as well as data-based games and skills tests, and offer speedier recommendations to recruitment teams that help them make more accurate choices.
Another important way data science skills bring value to an organization is that they can be used to help management make better decisions. Data Scientists with years of experience are often considered strategic partners or trusted advisors to those in upper management. They have a track record of helping the organization get the most out of its analytics capabilities. Data Scientists also convey the importance of their organization’s data through written and verbal communications, which lead to better decision-making throughout the organization. To do so, they measure, track, and document pertinent information like performance metrics.
To remain competitive, especially in a volatile financial market, it’s essential for companies to have realistic yet ambitious goals and to clearly define and articulate them. Data Scientists analyze and explore their employer’s data and focus on patterns, trends, or outliers, which can provide significant insights. When a trend in the data is spotted, and a Data Scientist communicates this through data visualizations or reports, Managers can use their recommendations to inform which initiatives to take to improve customer engagement, increase revenue, and ensure the organization performs optimally.
Data Scientists Help Their Organizations Identify Opportunities
Data Scientists who use an organization’s analytics system formulate questions about the in-use processes and baseline assumptions. This scrutiny is intended to help them develop new, innovative analytical algorithms and methods to handle data. A Data Scientist’s job involves them proposing ways to continuously improve the methods in place for gathering value from their employer’s existing data.
It Provides a Diverse, Marketable Skill-Set
To become a successful Data Scientist, it’s imperative to have a diverse set of hard and soft skills and competencies. Many of these skills are transferable and have value that extends beyond a current employer or professional role. Data Scientists must have a keen eye for detail. They often must pinpoint minute details from within a vast dataset and use this information to collect vital insights. An eye for detail also helps these professionals find timely solutions to optimization issues that are most pressing for the organization’s well-being. In addition to a detail-focused mind, Data analysts must also possess sharp analytical skills. This ensures that they will be able to spot any recurring or new problems and that they can provide relevant solutions. By collecting and analyzing raw data, they are able to propose better, more informed recommendations for their employer.
Organizational skills are also essential for Data Scientists. Being organized helps these professionals with the process of identifying and gathering sufficient data to help them gather insights. It also assists with time management, which is essential in a work environment that imposes critical deadlines. Another soft skill essential for data science is an innovative mindset. Data Scientists must leverage creative thinking at all times when creating new solutions for their organization. A creative mindset is also vital to helping them create new organizational processes aimed at reducing resource waste and improving work efficiency.Â
Along with strong organizational and creative skills, Data Scientists must be skilled problem-solvers. They must locate any critical issues within an organization and corresponding concerns. Data Scientists who are efficient at spotting these problems and devising corresponding solutions can then devote their time and efforts to more pressing tasks. Clear communication is also essential to be a Data Scientist. Once these individuals have gathered a sufficient amount of data and performed analysis on it, they must then communicate their findings to others. This involves both written and verbal communication in the form of meetings, reports, presentations, and data visualizations. By clearly communicating complex information, they ensure that the stakeholders within an organization will have access to the necessary insights and can use them to fuel their decision-making process.
A range of hard skills are also crucial for Data Scientists to perform their role effectively within an organization. These professionals must have technical training. Although the specific technical requirements for a Data Scientist will vary depending on the employer and organization’s needs, most possess knowledge of several programming languages such as Python, R, and SQL. They also have a strong background in mathematics, as well as statistical analysis and computing. Data Scientists must be able to wrangle and analyze data, as well as process vast datasets. They often work with deep learning and machine learning. A background in working with various analytical tools is also essential for Data Scientists. Many have a working knowledge of popular data analytical tools like Hadoop, SAS, Hive, Pig, or Spark. Additionally, a background working with data visualization tools such as Tableau is often essential to become a Data Scientist.
Data Science a Career Path with Opportunities for Growth
Not only is data science a popular and high-paying field now, but it is expected to continue to be in-demand for years. Data Science is projected to grow by 36% from 2021 to 2031, a growth rate that well surpasses other fields. More than 40,000 new data science positions are expected to be added during this time.
As machine learning and AI technology continue to develop, it is likely that the field of data science will continue to experience change. In addition to leveraging new technology, Data Scientists will likely become bridges that can communicate with both humans and computers. Additionally, those who work with data science will likely face more cloud computing in the years ahead. This tool allows Data Scientists to utilize powerful computer resources capable of processing vast sets of data. As the number of organizations that move to cloud computing continues to increase, it is likely that future Data Scientists will need to be increasingly familiar with the techniques and tools for performing cloud-based data processing.