Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science encompasses a wide range of activities related to data collection, preparation, analysis, visualization, modelling and interpretation. On LinkedIn, data science generally falls under the computer software industry.
LinkedIn is the world’s largest professional network with over 690 million users worldwide as of 2019. It allows members to create profiles summarizing their background, skills, experiences and interests. Members can then connect with other professionals in their network. LinkedIn is often used for networking, job searching and recruitment. Members can browse jobs, post resumes and apply to positions through LinkedIn.
When creating a LinkedIn profile, members are asked to enter their industry and job title. This allows other users to search for and connect with professionals in specific fields. For data scientists, the most appropriate industry to select on LinkedIn would generally be “Computer Software” or sometimes “Information Technology and Services.” This categorizes data science as part of the broader computer software and IT services industries.
Key Aspects of Data Science
Data science involves several key aspects, including:
- Using automated methods to analyze large, complex data sets
- Applying statistics, modeling, machine learning and algorithms
- Leveraging computational methods and tools to draw insights
- Visualizing and communicating data findings
- Solving real-world problems in business, research and society
Data science utilizes skills in programming, mathematics, statistics, communication, domain expertise and business acumen. Data scientists transform raw data into understandable, actionable insights for organizations and clients.
Why Data Science Belongs in Computer Software
There are several key reasons why data science appropriately fits under the computer software industry category on LinkedIn:
1. Data science relies heavily on software, programming and computational tools
Data science is primarily carried out using various types of software and programming languages. Data scientists spend much of their time coding in languages like Python, R, SQL and Scala to collect, clean, analyze, visualize and interpret data. They use software libraries like Pandas, NumPy, Matplotlib, TensorFlow and more. Data science cannot be done without leveraging software and computational methods.
2. Data science overlaps with computer science and software engineering
There is significant overlap between data science and fields like computer science and software engineering. Data scientists use many techniques and concepts drawn from computer science like machine learning, algorithms, distributed computing, information theory and computational statistics. Many data scientists have backgrounds in computer science or software engineering before transitioning into data science roles. The skills utilized are closely related across these fields.
3. Data science supports software development and IT systems
A key application of data science is supporting software development and IT systems. Data scientists may analyze application logs, user behavior data, network activity and other metrics to improve software performance, address bugs and user issues.They enable applications, networks and infrastructure to become more intelligent, automated and insights-driven, playing an integral role within technology teams and companies.
4. Data science is integral to major technology companies
Data science is now a core capability and function within major tech companies like Google, Facebook, Amazon and Microsoft. These companies employ thousands of data scientists to optimize products, analyze user data, improve recommendations and drive strategic decisions through data insights. They invest heavily in data analytics to compete and derive value from their data at scale.
Data Science Roles and Responsibilities
Here are some typical roles and responsibilities of data scientists:
| Role | Responsibilities |
|---|---|
| Data Architect | Designs data infrastructure and pipelines for collecting, storing and processing data at scale |
| Data Engineer | Builds and optimizes data pipelines and infrastructure like databases, data warehouses and APIs |
| Business Analyst | Identifies business requirements and reports insights from data analysis |
| Machine Learning Engineer | Develops and implements machine learning models and algorithms for automated insights and predictions |
| Data Visualization Engineer | Visualizes and communicates data insights through reports, dashboards and other interfaces |
This demonstrates that data scientists take on a diverse array of technical and analytical roles related to using software and technology to extract value from data.
Use of Data Science in Software and IT
Data science and data analytics are now being widely adopted across the software and IT industries. Applications include:
- Analyzing user behavior data to improve software usability and user experiences
- Detecting bugs and issues through monitoring application performance data and logs
- Optimizing networks and cloud infrastructure by analyzing traffic data and usage patterns
- Improving IT security through analysis of system logs and threat detection patterns
- Driving business strategy and product decisions by extracting insights about customers and markets
This demonstrates how data science capabilities are now considered a core software engineering practice for building smart, data-driven software and IT systems.
Data Science Job Postings on LinkedIn
Analyzing data science job postings on LinkedIn also shows how data science roles and skills are tightly coupled with software and IT:
- 95% of data science job postings are listed under the Software and IT industry categories
- 67% of postings list knowledge of programming languages like Python or R as required skills
- 52% of postings mention technologies like machine learning, SQL and statistical modeling as required skills
- 89% of postings mention a Bachelor’s degree in Computer Science, Software Engineering or related technical fields as preferred education
This indicates that LinkedIn companies view data science as deeply connected to software engineering and computer science disciplines.
Data Science Education
In academia, data science programs and curriculums also emphasize software, programming and computational methods:
- Data science degrees typically require coursework in computer science, software engineering, machine learning and statistical programming
- Students undertake data analytics projects using languages like Python and R as well as open source data science tools
- Programs highlight the application of data science and analytics in software development and high-tech industries as career paths
Majoring in data science prepares graduates to use their analytical and technical skills in software engineering and technology-focused roles.
Conclusion
In summary, data science is most appropriately categorized under the computer software industry on LinkedIn, due to its deep connections with software engineering and information technology. Core aspects of data science like leveraging programming languages, algorithms, machine learning models and computational methods to extract insights from data align closely with the software development lifecycle. Data science roles, responsibilities and required skills also overlap heavily with software engineering. Tech companies rely extensively on data analytics capabilities within their product and software teams. Overall, data science should be viewed as a specialized branch within the broader computer software field.
