Which occupations are available to those with a degree in data science?

Though views on data science haven’t changed much in the years after the Harvard Business Review dubbed it the “sexiest job of the 21st century” in a 2012 edition, opinions are still quite positive. The variety of job pathways available in data science is one of the main reasons it is still regarded as the greatest vocation in the world. “Data science” is a subject that offers its practitioners a range of career alternatives rather than a particular employment. 

Data science is an in-demand career, particularly in Australia. The amount that research predicts the sector will expand varies; some sources estimate that between 2019 and 2024, there will be a more than 11% rise in the number of data science employment; others predict a growth as high as 28%. Leaving statistics aside, one thing is for sure: graduates will have enough of work and future prospects to pursue profitable careers as data scientists. 

However, just what will these possibilities entail? Here are a few positions that someone with a degree in data science may apply for: 

First Career: Generalist in Data Science (Analyst) 

Generalist data scientists, who play the fundamental role in data science, concentrate on finding valuable insights in data. Generalist data scientists use experimental and exploratory techniques to find patterns, deconstruct difficult issues, and make well-informed predictions about what will happen in the future, regardless of the size of the datasets they are working with. 

Accountabilities

Generalists in data science usually handle tasks like these: 

• collaborating with stakeholders to find ways to use daAccountabilitiesutions 
• Data mining and analysis to inform corporate, marketing, and product development strategies 
• Evaluating information and methods for collecting it 
• Creating personalized data models and algorithms
• Predictive modeling to provide ideal client experiences 
• creating procedures and instruments for tracking and evaluating the performance of models and data. 

Data scientists need to be proficient in a variety of statistical computer languages, including R, Python, and SQL, in order to be successful in this position.

In this capacity, data scientists often work with NGOs, the government, and business. Their work is often experimental and focused on finding previously undiscovered insights in data, but they also need to be capable at solving problems in order to respond to particular inquiries. Strong communication abilities are possessed by seasoned generalists as they must be able to convey their findings to decision-makers who are not data scientists.

The fundamental knowledge required to comprehend the work of professionals in the majority of specialized data science fields is possessed by generalist data scientists. A large number of data scientists in specialized professions start out as generalists before honing their skills in a specific specialization.

Pay and employment prospects

• Work outlook: The Australian government’s Job Outlook website states that “Very Strong” job growth is anticipated for generalists in data science, commonly known as analyst programmers. The greatest growth rate is shown in this rating. 
• Average pay: $112,113 is the average pay for a generalist in data science, according to Indeed.

Path #2: Scientist (Analyst) by Research 

A research data scientist’s responsibilities are comparable to those of any other research scientist. study data scientists conduct experiments with data to get insights into certain study issues, regardless of whether they are employed by the government, business, or academic institutions.

Responsibilities

The following are typical duties for research scientists: 

• evaluaAccountabilitiesng both quantitative and qualitative information 
• carrying oAccountabilitiesor study 
• Obtaining information and data 
• drafting reports and research papers 
• examining analytical techniques and, if necessary, developing new techniques 
• proactively engaging with stakeholders to address the requirements for research
• Building research models in accordance with best practices 

Research scientists (analysts) need to be proficient in many coding languages, such as Python, Hadoop, SQL, JavaScript, and HTML, to be successful in this position.

Research data scientists work on a wide range of fascinating projects in many sectors. A large number of research data scientists are employed in the biosciences. For instance, biostatisticians, who are data scientists who characterize biological processes as statistical functions, utilize data science to assess medications’ effects on human bodies without requiring tissue samples or lab equipment. Drug discovery is the method that both small companies and major research institutes employ.

Numerous instances of data science-driven research may be found on the bioRxiv website, such this study that explains how to utilize computer vision (machine learning) to detect malignant tumors in histology pictures.

Pay and employment prospects

• Job outlook: Research scientists could anticipate “Moderate” job growth, according to the Australian government’s Job Outlook website. 
• Average pay: $96,173 is the average pay reported by Indeed for research scientists (analysts). 
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Third career path: Machine learning engineer (developer/analyst) 

One of the main subfields of data science is made up of machine learning engineers. They have a thorough grasp of the many machine learning models that are out there and have honed an innate sense of which models work best for a certain job. 

Experts in this field might operate as developers or analysts. Machine learning models, including k-nearest neighbours and decision trees, are used by analysts to classify and identify correlations in data. Models are built by developers to enable services to do intricate tasks. An example of this is the usage of natural language processing models by chatbots to respond to customer inquiries. 

The kinds of labor required for the developer and analyst pathways vary. Analysts should concentrate on building the most accurate and practical models rather than trying to generate a marketable product. On the other hand, developers must spend a substantially greater amount of time programming in order to produce “minimum viable products” that satisfy certain performance criteria (such as robustness).

Responsibilities

• Machine learning engineers could be in charge of things like: 
• The creation of autonomous AI softwaAccountabilitiese model automation 
• Accountabilitiesots, translation applications, and virtual assistants
• Creating systems for machine learning 
• Conducting experiments, carrying out statistical evaluations, and analyzing test outcomes
• Formulating and using algorithms 
• overcoming issues with data sets.  

Machine learning engineers need to be proficient in many coding languages, such as Python, Java, and R programming, in order to succeed in this position.

Pay and employment prospects

• Employment outlook: There is no official forecast for employment growth in this sector provided by the Australian government. 
• Average pay: $113,230 is the average pay for a machine learning engineer, according to Indeed.

Data Engineer (Developer): Career #4 

Data is received by modern organizations from a multitude of sources, including supply chain orders, online sales portals, marketing initiatives, and point-of-sale terminals. These many sources will often provide data in a multitude of forms. Data engineers are experts in building the framework required to make it easier for data to be moved from several sources into a single data storage or analytics platform. 

Any organization that processes big amounts of data from several sources has to have data engineering in place. The capability of data science to analyze whole datasets as opposed to dealing with subsamples is a significant advantage over conventional analytics. Data engineers employ “data pipelines” and “data warehouses,” which are two important forms of infrastructure for moving and storing data, respectively. 

Data engineers frequently create “ETL” (Extract, Transform, and Load) pipelines, which transfer data from sources into analytics systems. Without interfering with the current data warehouse, the ETL procedure converts data into a format that is suitable for usage with a particular analytics tool.

Because of the nature of their job, data engineers prioritize programming skills above statistical knowledge more than other data scientists. 

Responsibilities

• Among the duties that data engineers could have are:
• obtaining information and creating data set procedures 
• Data analysis using programming languages and tools 
• Finding methods to enhance the quality, efficiency, and dependability of data
• carrying out market research 
• Using massive data sets to solve commercial issues
• using advanced statistical, machine learning, and analyst programming techniques. 
• identifying patterns in data and using prescriptive and predictive modeling techniques to it. 
• Data engineers need to be proficient in many coding languages, such as Scala, Apache Spark, Java, and Hadoop, to succeed in this position. 

Pay and employment prospects

• Employment outlook: There is no official forecast for employment growth in this sector provided by the Australian government. 
• Average pay: Indeed reports that a data engineer makes, on average, $99,261.

Career #5: Developer/Data Warehouse Architect 

The duty of data warehouse architect is to define how an organization’s data will be integrated, stored, and retrieved. It is a complement to the position of data engineer. Data warehouse architects have to make sure that complicated datasets are stored in a way that satisfies their employer’s analytics requirements since variations in data structure have an impact on how analysts can access and utilize the data.

A data warehouse architect must be aware of the appropriate data storage type (such as relational, wide-column, graph, or document-oriented database) for a specific job in order to satisfy those demands. The best kind of data storage for a warehouse will depend on the specific analytics workflow that the warehouse is used to support. For example, a warehouse that receives a lot of data once a day (also known as “batch loading”) will need different data storage than a warehouse that receives a steady stream of data. 

In addition, experts in this domain may create non-warehouse data stores like knowledge graphs, which arrange data in a way that illustrates the connections between each data point in the graph (database). This is helpful for industries like banking since it allows for analytics that illustrate how a shift in one company’s fortunes might affect the fortunes of the firms it partners with.

Responsibilities

• Among their duties as data warehouse architects might be: 
• Creating and managing solutions for data management 
• evaluating the demands for corporate data 
• Implementing Software for Data Management 
• storing and getting data out of computer or cloud storage 
• Data analysis using programming languages and tools 
• Using massive data sets to solve commercial issues
• proactively determining and meeting the data and storage demands of stakeholders. 
• Data engineers need to be proficient in many coding languages, such as Oracle, SQL, J2EE, or Cognos, in order to succeed in this position. 

Pay and employment prospects

• Job outlook: Data warehouse architects should have “Very Strong” job growth, according the Australian government’s Job Outlook website. The greatest growth rate is shown in this rating. 
• Average pay: $141,085 is the average income listed by Indeed for a data warehouse architect.

Career #6: Data journalism and investigations (analyst) 

Investigative data science is becoming more and more crucial in fields including law enforcement, media, insurance, due diligence, and risk assessment. Conventional investigations must manually review data to find pertinent connections and depend on subjective judgment to determine the relevance of the evidence. Larger-scale studies are made possible by data science, which also makes it possible to evaluate significance effectively using statistics and other empirical metrics.

One well-known instance of investigative data science is the work done by the International Consortium of Investigative Journalists (ICIJ) in response to the “Panama Papers leaks.” In order to identify linkages among the data, the ICIJ transferred information from the 11.5 million stolen documents into a graph database and ran analytics queries on the output. They were able to reveal how many people from various countries were concealing money overseas thanks to this procedure. 

Responsibilities

• Investigative reporters and data journalists may be in charge of the following tasks: 
• Knowing how to use statistics to convey tales 
• investigating, purifying, and evaluating data collections 
• Recording procedures, techniques, and code 
• supplying reporters with data and analytical techniques 
• concentrating on openness and data integrity 

Pay and employment prospects

• Employment forecast: For investigative and data journalists, the Australian government does not provide official employment outlook projections. 
• Average pay: $110,000 is the average compensation for data journalists and investigators, according to Indeed.

Examine many employment options for data scientists.

The employment listed above for data scientists with degrees are only a portion of what choices are open to those with the necessary qualifications. The fact that data scientists’ abilities are applicable to a broad range of tasks and that they are always coming up with new applications for them is one reason why there is still a significant need for them. 

For anybody interested in pursuing a career in data science, the University of New South Wales’ 100% online Master of Data Science program provides the perfect path.  As the only Australian institution with worldwide rankings in Computer Science, Statistics, Mathematics, and Economics, students in the program get top-notch instruction and will graduate among Australia’s most marketable graduates. 

Students studying data science at UNSW benefit from a program that focuses on imparting the abilities that companies find most valuable. This online program is appropriate for individuals at any point of their career since it allows you to tailor your studies around your current obligations.

Data scientists have a plethora of career options, which makes them the perfect choice for anybody looking to enter a field that is in high demand and gives its members the freedom to choose their own path.