Data science is an emerging field in the IT industry. On the other hand, Software engineering is already well-established. Computer science graduates are usually aware of what software engineers are but have a vague knowledge of what data scientists do. Both these roles have their own importance in their respective teams. In this blog, we will discuss the differences between the two concerning skills, salary, career path, and methodologies. The goal of the blog is to better understand these positions and how they collaborate.
The terms data science and big data are slowly occupying the computer science world, the data scientist is emerging as a new career option. On the other hand, software engineering is an older discipline that has already made its perfect place in the IT world. These both are popular professions. The graduates can opt for any of the roles based on their interests. But for choosing the one, you must have the proper knowledge to differentiate between the two to make the right choice. The future in both roles is bright.
What is Data Science?
Hugo Browne-Anderson, a renowned data science evangelist, once said that "If you wanna do data science, learn how it is a technical, cultural, economic, and social discipline that has the ability to consolidate and rearrange societal power structure."
Data science is the process of extracting knowledge and insights from data by using scientific methods. It uses various processes and methods to study different kinds of data - structured, unstructured, and semi-structured data.
Data Science is now affecting our day-to-day activities. Some of the technologies to analyze data are data purging, data transformation, and data mining. The main goal of a data scientist is to optimize the algorithms and maintain a balance between speed and accuracy. They integrate with experts and work together to come up with an optimized solution.
What is Software Engineering?
The term software engineering is made from two words, software, which is the set of integrated programs, and engineering, which is applying theoretical and practical knowledge to design, build, test, and deploy the application. Software engineering is a branch of engineering that involves developing software products using well-defined principles, procedures, and methods.
The outcome of this process is an efficient and reliable software product. The processes involved in delivering a software product are understanding customer needs, analyzing requirements, creating a design, coding, testing, software deployment, and maintenance.
Data Scientist vs Software Engineer: Skills
Data scientist skills
Data scientists are highly qualified individuals; they are familiar with the dos and don'ts in an entire machine learning lifecycle. The more advanced your position, the more advanced set of skills are required. But as a beginner, you need to have a specific set of skills to get eligible for this role in companies. Let's have a look at the skills:
Math and statistics: A good data scientist should have a solid basic knowledge of both subjects. There are steps in data science that require statistical knowledge to make decisions, analyze and solve the problem. Additionally, linear algebra and calculus are also essential skills required.
Programming: The two most common languages used in data science are Python and R, and a data scientist needs to have a firm grip on one of the languages.
Data Visualization: This is a crucial skill for any data scientist. It is a way to communicate with your data and get valuable insights that help develop some business solutions. It becomes easy to analyze complex data by breaking it down into small segments.
Machine Learning: For a data scientist, ML is an essential skill to have. It is used to develop predictive models. There are the following types of learning: supervised, unsupervised, semi-supervised, and reinforcement learning. Applying the appropriate learning type gives you quality predictions and estimations.
Data Manipulation and analysis: Data manipulation skills help clean and transform the data for better analysis in the following stages. Data analytics skills help data scientists understand their data in more depth and gain valuable insights that can help come up with the solution.
Problem-solving skills: Data science problems are usually hard at the technical level. It needs a lot of research and knowledge to get good accuracy. So, it is important for a data scientist to have good problem-solving skills.
Programming: The two most common languages used in data science are Python and R, and a data scientist needs to have a firm grip on one of the languages.
Data Visualization: This is a crucial skill for any data scientist. It is a way to communicate with your data and get valuable insights that help develop some business solutions. It becomes easy to analyze complex data by breaking it down into small segments.
Machine Learning: For a data scientist, ML is an essential skill to have. It is used to develop predictive models. There are the following types of learning: supervised, unsupervised, semi-supervised, and reinforcement learning. Applying the appropriate learning type gives you quality predictions and estimations.
Data Manipulation and analysis: Data manipulation skills help clean and transform the data for better analysis in the following stages. Data analytics skills help data scientists understand their data in more depth and gain valuable insights that can help come up with the solution.
Problem-solving skills: Data science problems are usually hard at the technical level. It needs a lot of research and knowledge to get good accuracy. So, it is important for a data scientist to have good problem-solving skills.
These were some of the important skills to begin with the data science projects. Other skills are big data, software engineering, model deployment and good communication.
Software engineer skills
The software engineer skill set does not include the level of education needed for a data scientist. Some of the skills included don't require much effort to learn. Let's have a look:
Programming language: This is a core skill for any software engineer. They should have a firm grip on at least one programming language, but learning more is also better. Some of the popular languages are Java, C++, Python, and PHP. It is recommended to create projects in the respective languages to build a strong base.
Linux: Software engineers cannot avoid working on Linux because you can find it at many stages. When you deploy any application to a server, it is most likely running a Linux OS.
Database: This is another most essential skill. Databases like MySQL, Oracle, MongoDB are widely used. Software engineers create various applications, so some information may need to be stored in the database. So, to interact with it, this skill is a mandate. Concepts like joins and normalization are essential for learning.
Data structures and algorithms: This skill is the topmost priority by most companies to check problem-solving and coding skills. You can become a good software developer if you know about organizing data and using it to solve real-world problems.
Problem-solving skills: Software engineers spend more time debugging the errors than development, so it is important to have good problem-solving skills. This skill distinguishes great software engineers from good software engineers.
Software Development Lifecycle (SDLC): The development of any software will require an SDLC model. It helps in analyzing and understanding the customer's requirements. Further steps include designing the solution, developing the application, performing testing, and deploying.
Linux: Software engineers cannot avoid working on Linux because you can find it at many stages. When you deploy any application to a server, it is most likely running a Linux OS.
Database: This is another most essential skill. Databases like MySQL, Oracle, MongoDB are widely used. Software engineers create various applications, so some information may need to be stored in the database. So, to interact with it, this skill is a mandate. Concepts like joins and normalization are essential for learning.
Data structures and algorithms: This skill is the topmost priority by most companies to check problem-solving and coding skills. You can become a good software developer if you know about organizing data and using it to solve real-world problems.
Problem-solving skills: Software engineers spend more time debugging the errors than development, so it is important to have good problem-solving skills. This skill distinguishes great software engineers from good software engineers.
Software Development Lifecycle (SDLC): The development of any software will require an SDLC model. It helps in analyzing and understanding the customer's requirements. Further steps include designing the solution, developing the application, performing testing, and deploying.
Data Scientist vs Software Engineer: Methodologies
Every role requires a methodology that is followed by the individuals/team to give the results. Data scientists and Software engineers both have different methods to work. Let'sLet's have a walkthrough for both:
Data Scientist Methodologies
Business Understanding: This is a crucial step because it helps understand the customer's end goal. It would be best if you asked many questions to understand every aspect of the problem statement. The result of this stage gives us a list of business requirements.
Analytic Approach: Once the requirement is cleared, data scientists come up with an analytical approach. They express the problem in terms of statistical and machine learning techniques, which helps identify the problem statement pattern.
Data Requirements: This is the step to identify the data format, content, and sources for initial data collection.
Data Collection: Data scientists collect data from different sources using techniques like web scraping or premade data present on the repositories.
Data Understanding: Data scientists try to understand the collected data; they learn about the type and attribute of the data and check whether it is appropriate for the given requirement.
Data Preparation: This is one of the essential steps as data scientists start preparing data for their model. They need to make sure that the data collected and the ML algorithm selected are compatible with each other or not.
Modeling: In this step, data scientists understand whether their work is good to go or needs a kind of review. Modeling focuses on developing either descriptive or predictive models, and these models are based on the analytic approach taken statistically or through machine learning.
Evaluation: The model is ready to evaluate; data scientists can assess two ways: Holdout (dividing the dataset into three parts: training, validation, and testing) and Cross-validation.
Deployment: Once the data scientists are confident with their work, the model gets deployed.
Feedback: This step is usually made for most of the customers. They can check whether the deployed model fulfills their requirement or not. This stage can have several iterations.
Analytic Approach: Once the requirement is cleared, data scientists come up with an analytical approach. They express the problem in terms of statistical and machine learning techniques, which helps identify the problem statement pattern.
Data Requirements: This is the step to identify the data format, content, and sources for initial data collection.
Data Collection: Data scientists collect data from different sources using techniques like web scraping or premade data present on the repositories.
Data Understanding: Data scientists try to understand the collected data; they learn about the type and attribute of the data and check whether it is appropriate for the given requirement.
Data Preparation: This is one of the essential steps as data scientists start preparing data for their model. They need to make sure that the data collected and the ML algorithm selected are compatible with each other or not.
Modeling: In this step, data scientists understand whether their work is good to go or needs a kind of review. Modeling focuses on developing either descriptive or predictive models, and these models are based on the analytic approach taken statistically or through machine learning.
Evaluation: The model is ready to evaluate; data scientists can assess two ways: Holdout (dividing the dataset into three parts: training, validation, and testing) and Cross-validation.
Deployment: Once the data scientists are confident with their work, the model gets deployed.
Feedback: This step is usually made for most of the customers. They can check whether the deployed model fulfills their requirement or not. This stage can have several iterations.
Software engineer methodologies
Software engineers use various methodologies, let’s discuss the three popular methods:
Agile development: Requirements are refined iteratively by dividing the work into smaller chunks. The working style of teams is highly collaborative. There are regular scrum meetings. The agile process is transparent; there is an open interaction with investors and stakeholders. In agile, the planning cycles are shorter, so it becomes easy to accept and accommodate the change at any point throughout the project.
Waterfall model: It is a linear model that consists of sequential phases (requirements, design, implementation, verification, maintenance). It is mandatory to complete the current stage to proceed to the next step. There is no option to backtrack the flow for any modification in the project. It is a slow and costly methodology due to its rigid structure and also most of people confused to choose the right model between agile and waterfall.
Check this source for head to head comparison between them.
Rapid Application Development: This kind of development produces high-quality software at a low cost. It contains four phases: requirements planning, user design, construction, and cutover. The second and third phases repeat until users get to confirm that the product fulfills their requirements. RAD is helpful for small to medium size projects that are time-sensitive.
Waterfall model: It is a linear model that consists of sequential phases (requirements, design, implementation, verification, maintenance). It is mandatory to complete the current stage to proceed to the next step. There is no option to backtrack the flow for any modification in the project. It is a slow and costly methodology due to its rigid structure and also most of people confused to choose the right model between agile and waterfall.
Check this source for head to head comparison between them.
Rapid Application Development: This kind of development produces high-quality software at a low cost. It contains four phases: requirements planning, user design, construction, and cutover. The second and third phases repeat until users get to confirm that the product fulfills their requirements. RAD is helpful for small to medium size projects that are time-sensitive.
Career Paths: Data Scientist vs Software Engineer
Each company has its specific career roadmap. Below is the list of career paths for a data scientist:
Associate/Junior Data Scientist:
They have limited experience and start as entry-level data scientists.
They are usually assigned the task to explore and test new ideas along with refactoring the existing models.
This career period brings the opportunity to learn new skills and gain experience while working on real-world projects.
Senior Data Scientist:
They are responsible for building well-architectured projects.
They act as a mentor for associate-level data scientists and also deal with business-level people.
They collaborate with finance, researchers, software developers, and business leaders to define product requirements and provide analytical support.
They have limited experience and start as entry-level data scientists.
They are usually assigned the task to explore and test new ideas along with refactoring the existing models.
This career period brings the opportunity to learn new skills and gain experience while working on real-world projects.
Senior Data Scientist:
They are responsible for building well-architectured projects.
They act as a mentor for associate-level data scientists and also deal with business-level people.
They collaborate with finance, researchers, software developers, and business leaders to define product requirements and provide analytical support.
AI/ML Engineer:
They use exceptional mathematical skills to perform computations and work with the algorithms involved in this type of programming
They collaborate with data engineers to build data and model pipelines and manage the infrastructure and data pipelines needed to bring code to production.
They provide support to engineers and product managers in implementing machine learning in the product.
Principal Data Scientist:
These data scientists usually have 5+ years of experience and are well-versed with machine learning models.
They understand challenges in multiple business domains, discover new business opportunities, and leadership excellence in data science methodologies.
They have industrial maturity while delivering the designs and algorithms that is a plus point for cross-organization tradeoffs.
They use exceptional mathematical skills to perform computations and work with the algorithms involved in this type of programming
They collaborate with data engineers to build data and model pipelines and manage the infrastructure and data pipelines needed to bring code to production.
They provide support to engineers and product managers in implementing machine learning in the product.
Principal Data Scientist:
These data scientists usually have 5+ years of experience and are well-versed with machine learning models.
They understand challenges in multiple business domains, discover new business opportunities, and leadership excellence in data science methodologies.
They have industrial maturity while delivering the designs and algorithms that is a plus point for cross-organization tradeoffs.
Let's discuss a typical career path for a software engineer.
Associate Software Engineer:
They have limited experience and start as an entry-level engineer.
They are usually assigned a task to develop software that meets client requirements within a specified amount of time.
This career period brings the opportunity to learn new skills and gain experience while working on real-world projects.
Senior Software Engineer:
They master the software development lifecycle process.
They get the opportunity to train junior software engineers and manage small teams.
At this stage, engineers start to get introduced to other business elements such as high-level company objectives and project budgets.
Tech Lead:
They are responsible for the entire software development lifecycle.
They usually manage a large team of professionals that are part of software design and development.
They are responsible for reporting development progress to company stakeholders and provide input into the decision-making process.
Team Manager:
This role requires strong leadership skills.
They are responsible for the well-being of the entire team.
They help in the career progression of their team.
Technical Architect:
This role usually overlooks the entire architecture technical design.
They are responsible for providing technical leadership and building processes for the team members.
Chief Technology Officer:
They ensure that the technological resources can satisfy the short and long term needs of the company.
They are responsible for outlining the company goals for R&D.
They help various departments to use technology profitably.
They have limited experience and start as an entry-level engineer.
They are usually assigned a task to develop software that meets client requirements within a specified amount of time.
This career period brings the opportunity to learn new skills and gain experience while working on real-world projects.
Senior Software Engineer:
They master the software development lifecycle process.
They get the opportunity to train junior software engineers and manage small teams.
At this stage, engineers start to get introduced to other business elements such as high-level company objectives and project budgets.
Tech Lead:
They are responsible for the entire software development lifecycle.
They usually manage a large team of professionals that are part of software design and development.
They are responsible for reporting development progress to company stakeholders and provide input into the decision-making process.
Team Manager:
This role requires strong leadership skills.
They are responsible for the well-being of the entire team.
They help in the career progression of their team.
Technical Architect:
This role usually overlooks the entire architecture technical design.
They are responsible for providing technical leadership and building processes for the team members.
Chief Technology Officer:
They ensure that the technological resources can satisfy the short and long term needs of the company.
They are responsible for outlining the company goals for R&D.
They help various departments to use technology profitably.
Salary: Data Scientist vs Software Engineer
The salary of an employee is usually based on their skill, experience and performance. This section will give you a brief idea for the salaries of data scientists and software engineers.
Data Scientist Salaries
In India, the average salary for a data scientist in India is Rs.698,412 per year, and in the United States, the average salary for a data scientist is $117,142 per year.
Software Engineer Salaries
In India, the average salary for a software engineer in India is Rs.566,971 per year, and in the United States, the average salary for a software engineer is $99,315 per year.
Different Tools: Data Scientist vs Software Engineering
The data scientist role is highly competitive. They work on various tools that help them in carrying out their machine learning-related tasks: Some of them are listed below:
Apache Spark: It is a popular data science tool specifically designed to handle batch processing and stream processing. It is designed to support data analytics-related tasks and is capable of handling streaming data.
Tableau: It is a data visualization tool that helps in data analysis and decision-making. You can represent data visually in less time by Tableau so that everyone can understand it, and it becomes easy for you to solve advanced data analytics.
Tensorflow: It is a widely-used tool helping hand with various newly emerging technologies like Data Science and Artificial Intelligence.
TensorFlow is a Python-based library that you can use for building and training Data Science models.
BigML: It is used for building datasets and then sharing them easily with other systems. One can efficiently perform data classification and find the outliers in the dataset. Data scientists can make decisions due to their interactive data visualization process.
PowerBI: It is also one of the essential tools of Data Science integrated with business intelligence. It is possible to generate rich and insightful reports from a given dataset using PowerBI. Users can also create their data analytics dashboard using PowerBI.
Tableau: It is a data visualization tool that helps in data analysis and decision-making. You can represent data visually in less time by Tableau so that everyone can understand it, and it becomes easy for you to solve advanced data analytics.
Tensorflow: It is a widely-used tool helping hand with various newly emerging technologies like Data Science and Artificial Intelligence.
TensorFlow is a Python-based library that you can use for building and training Data Science models.
BigML: It is used for building datasets and then sharing them easily with other systems. One can efficiently perform data classification and find the outliers in the dataset. Data scientists can make decisions due to their interactive data visualization process.
PowerBI: It is also one of the essential tools of Data Science integrated with business intelligence. It is possible to generate rich and insightful reports from a given dataset using PowerBI. Users can also create their data analytics dashboard using PowerBI.
Software engineering tools are essential for any organization to be productive and tackle business challenges.
GitHub: It is also known as a Google Drive for software industry projects. This tool is a hosting service with open-source codes and allows you to upload any project. The IT community consists of many developers who can share, discover, and collaborate with this tool.
Jenkins: This is an open-source that offers orchestration capabilities to deploy various kinds of applications. It is used for development, continuous integration, testing, and deployment.
GitLab: It is a web-based tool popular for the developers’ lifecycle management. GitLab is a platform that manages git repositories and provides integrated features like continuous integration, issue tracking, team support, and wiki documentation.
Jira: It is used to plan and manage the projects. It becomes easy to customize the workflow, generate performance reports, track the team backlogs, and visualize progress.
Docker: It helps in the packaging of the software into a file system. Docker makes creating containers (lightweight, standalone, executable package of the software) easier, simpler, and safer to build, deploy and manage containers.
IDEs: Integrated Development Environment (IDE) enable programmers to consolidate the different aspects of writing a computer program.It increases programmer productivity by combining everyday software development activities into a single application like editing source code, building executables, and debugging.
Jenkins: This is an open-source that offers orchestration capabilities to deploy various kinds of applications. It is used for development, continuous integration, testing, and deployment.
GitLab: It is a web-based tool popular for the developers’ lifecycle management. GitLab is a platform that manages git repositories and provides integrated features like continuous integration, issue tracking, team support, and wiki documentation.
Jira: It is used to plan and manage the projects. It becomes easy to customize the workflow, generate performance reports, track the team backlogs, and visualize progress.
Docker: It helps in the packaging of the software into a file system. Docker makes creating containers (lightweight, standalone, executable package of the software) easier, simpler, and safer to build, deploy and manage containers.
IDEs: Integrated Development Environment (IDE) enable programmers to consolidate the different aspects of writing a computer program.It increases programmer productivity by combining everyday software development activities into a single application like editing source code, building executables, and debugging.
Summary
Software engineers and data scientists are among the famous roles in the IT industry. The skills required for both positions have similarities and differences. The work and salary might differ with company and location, but the specific part is already discussed. Thi blog also mentions the advancement in career for both the roles, which is the only matter of how much experience you have. These roles will never end because the requirement for both is high.