What Is Data Science?
Before we go and discuss the term data science, let’s first learn why Data matters. Here’s a famous statement by Eric Schmidt, Executive Chairman at Google:
You see, the rate of data generation has grown. With every digital click that we do, we generate data. And this data is a treasure for organizations, wonder why?
Consider this, if you search for a certain product on Amazon, and are looking to buy it. All of a sudden, Amazon starts sending emails to you about the same product with better discounts. Will you be interested in buying that product in that case? Well, data says that 37% of Amazon sales are generated via recommendation engines that make the above thing happen.
Data science is an IT approach that drives the creation of innovations. With data science, you make use of statistical modeling, machine learning algorithms, and programming to build solutions that could do predictions, recommendations, detections, sentiment analysis, etc.
Data science is heavily inclined toward the value of approximation, the results of data analysis, and the understanding of its results. Like software engineers, data scientists aim to optimize algorithms and manage the trade-off between speed and accuracy.
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What Is Software Engineering?
Software engineering is a process of information gathering, designing, implementing, testing, and maintaining software systems systematically. It is an approach that focuses on producing software that is dependable, high-quality, and maintainable.
Now, let’s break down the above statement and discuss it in detail.
Imagine you have a plan to build a new home. First, you think about what you want in your house—like how many rooms, where the kitchen should be, and what color the walls should be. This is like the initial concept and gathering requirements in software engineering, where we decide what the software needs to do and how it should look.
Next, you work with architects to design your house. They draw up detailed plans showing where each room will be, how big the windows are, and where the doors go. This is similar to the design phase in software engineering, where we plan how the software will work and look.
Then, builders start to construct the house according to the plans. They use materials like bricks, wood, and concrete to build walls, floors, and roofs. This is like the implementation phase in software engineering, where we build the software using code and programming languages.
Once the house is built, the owner checks everything to make sure it’s safe and works correctly. This is similar to the testing phase in software engineering. After shifting into the new house, one has to maintain it for longer use. This covers the maintenance phase of software engineering.
Qualifications Required for Data Science and Software Engineering
With the introduction of data science, many talents have been drawn to improve their computer programming and machine learning abilities. Also, it is observed that software engineering skills are required for data scientists. The abilities required for aspiring data scientists and software engineers are listed in the following section.
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Qualifications for Data Science
Data Scientists come from various educational backgrounds. Primarily, they involve a B.Tech or M.Tech, majoring in Computer Science or Information Technology, a B.Sc or MSc in Statistics, and a few MBA graduates from top business schools.
The role primarily requires a skill set in mathematics, statistics, knowledge of programming languages, and other computer science essentials. Machine Learning plays a major role in Data Science.
To sum it up, a Data Scientist can be from the following educational backgrounds :
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Qualifications for Software Engineering
A software engineer requires a Bachelor’s degree majoring in a related computer program as a minimum entry-level requirement. Software eEngineering demands a deep understanding of at least a couple of languages and how they function. Some of the popular languages are Python, JavaScript, C, C++, Ruby, and Java.
Knowledge of data structures and algorithms is recommended, as a lot of interviewers look for these skills during recruitment. Along with theoretical knowledge, there is a high recommendation for practical knowledge in reading code, building projects, and working in a team.
An outline of the qualifications and requirements for Software engineering are listed below:
- Bachelor’s degree in any related computer program
- Deep and practical programming knowledge
- Data structures and algorithms
Career Paths for Data Science vs. Software Engineering
Let’s look at how to get started in Data Science and Software Engineering. Further in each part, we discuss the professional choices you can pursue if you want to be a Data Scientist or a Software Developer.
Data Science Careers
As a fast-developing industry, we do not expect this need to slow down anytime soon. Specializing in Data Science leads to entry-level positions as an Analyst in several sectors.
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These roles can further lead to senior and lead roles. To maximize your earnings, your eventual goal can be a C-suite role in data science as a chief data officer for an organization.
Timeline if you are choosing to specialize in Data Science.
- Data Scientist
- Senior Data Scientist.
- Machine Learning Engineer.
- Lead Data Scientist.
- Chief Data Officer.
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Software Engineering Careers
An aspiring software engineer can expect regular opportunities to progress in their career. As you learn technical skills and gain experience, new opportunities will open. A career in software engineering can be kickstarted with the following roles:
These roles can further lead to senior and lead roles such as
- Junior Software Engineer
- Senior Software Engineer
- Tech Lead
- Team Manager
- Technical Architect
- Chief Technology Officer
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Data Science vs. Software Engineering Salary
Pay is normally skill-based, competency-based, or performance-based. This section will give you a brief overview of the salaries in the Data Science and Software Engineering domains.
Data Scientist Salaries
One of the most crucial variables influencing a Data Scientist’s salary is experience. A data science professional’s remuneration grows dramatically with each year of expertise.
| Level | India (INR/Year) | USA (USD/Year) |
| Entry Level | 8,00,000 | 1,46,436 |
| Mid Level | 13,00,000 | 2,30,239 |
| Experienced | 23,00,00 | 2,15,705 |
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Software Engineer Salaries
The average software engineer’s salary is highly regarded, and it is no secret that they are in high demand to earn a large amount.
| Level | India (INR/Year) | USA (USD/Year) |
| Entry Level | 7,75,000 | 1,35,214 |
| Mid Level | 15,00,000 | 1,79,750 |
| Experienced | 24,00,00 | 1,83,064 |
Both Data Scientists and Software Engineers are highly paid professionals, and both professions come with great benefits and challenges.
** Source of salaries of Data Scientist vs. Software Developer: Payscale
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Difference Between a Data Scientist and a Software Engineer
All the differences in this article Data Scientist vs. Software Developer domains have been summarized in the table below.
| Data Science | Software Engineering |
| Data Science focuses on gathering and processing data. | Software Engineering focuses on the development of applications and features for users. |
| Includes machine learning and statistics. | Focuses more on coding languages. |
| Deals with Data visualization tools, Data Analytics tools, and Database Tools. | Software Engineering deals with programming instruments, database services plan instruments, CMS devices, testing devices, integration apparatus, etc. |
| Deals with Exploratory Data. | Software Engineering focuses on systems building. |
| Data Science is Process Oriented | Software Engineering is methodology-oriented. |
| Skills include programming, machine learning, statistics, and data visualization. | Skills include the ability to program and code in multiple languages. |
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Which One is Better, Data Science vs. Software Engineering?
Ultimately, when comparing the professions of Software Engineer and Data Scientist, the crucial question arises: “Which field is superior?” Data science and software engineering require you to have programming skills. While data science includes statistics and machine learning, software engineering focuses more on coding languages.
Both professions are in great demand and highly rewarding. Finally, it is determined by your area of interest. In the ever-expanding realm of technology, while the field of data science is experiencing remarkable growth, the significance of software engineers remains paramount. Software engineers play a crucial role in constructing the very foundations upon which data scientists rely. While data scientists extract insights from data and contribute new perspectives to businesses, it is the expertise of software engineers that allows these insights to be transformed into tangible software solutions. Hence, the symbiotic relationship between data scientists and software engineers ensures the continuous advancement and success of technological endeavors.
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