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Data Science and Software Engineering - What you should know?

According to Google Economist, the sexiest job in 2019 will be Data Scientist and it is proven true so far because expert data scientists are high in demand everywhere today and taking up higher salary packages too. The job role of a Data scientist is highly similar to a Software Engineer with an average salary of $137k approximately.

With almost the same salaries, do they share the same roles or responsibilities? Well, it depends on the Company how are they defining the roles. In most cases, both have a different set of responsibilities, so in this blog we would discuss few of the useful comparisons between them.

What is Software Engineering?

Software Engineering is a structured approach to design, develop, and maintain the software program and avoid quality issues. It makes the requirements clear so that the development process can be easier to understand and implement.

A Software Engineer is a person who applies principles of Software Engineering to design, develop, maintain, and test the software program. The job role of a Software Engineer involves analyzing a problem, think, design and implement the best software solution and test it continuously. Software engineers majorly deal with complex business problems and try to find out the best solution too. The work of a Software engineer ends when he finishes implementation work for the problem.

What is Data Science?

Data Science converts or extracts data in multiple formats to meaningful information. Businesses have the flexibility to use this knowledge to make decisions and improve overall business processes. With data science, businesses can become intelligent enough to sell or push products.

Read: Deep Learning Tutorial Guide for Beginner

Data scientist are the data wranglers. The job role of a Data scientist involves making good data prediction on the basis of past behavior. A data scientist does not create solutions but he creates data models to generate such predictions or classifications. He tries to optimize data models so that new modes can be generated for better predictions or classifications. The work for a Data Scientist never ends because data behavior changes over time.

It may take a lot of time when a data scientist creates its first data model. Data scientists must analyze the data, clean the data, generate new features, decide the best features, train models, try different AI or machine learning algorithms, and measure different online metrics. Each of these steps demand for extra efforts and time like weeks or even months.

In this way, the work of Data Scientists does not have hard deadlines. It is tough for any data scientist to estimate when a data model can be deployed in production.

Data Science vs Software Engineering – Top Useful Technical Comparisons

Implementation vs. Experimentation 

  • Software engineering teams are generally busy in implementation and Data science team are busy in running experiments.
  • The goal of implementation is to add new functionalities to the system. At the same time, experiments are the way to check a hypothesis. Based on the result of the experiment, the hypothesis is proved either true or False.
  • If a hypothesis is proved false, it does not mean the experiment is failed. Based on your observations, you can start a new hypothesis and plan a new experiment.
  • It is sometimes possible that most of the hypotheses are proved to be false. But it does not mean you are wasting time. It is natural to try multiple approaches for complex problems until we find the right solution.

Data Science Vs. Software Engineering - Importance

The impact of IT is changing everything about science. Huge data is generated from everywhere and we need experiments for data management as it grows. Data science emerged as a solution here for data analysis, data management etc.

Without following any discipline, designing a software solution is not possible. Software engineering defines a set of principles to create, maintain, and develop a software product without any vulnerabilities.

Read: An Easy To Interpret Method For Support Vector Machines

Data Science Vs. Software Engineering - Methodology

In Data Science, ETL is the process for data extraction, transforming it to a logical format that is easy to understand and loading it into a system for processing. At the same time, SDLC (Software Development Life Cycle) forms the basis of software engineering.

Data Science Vs. Software Engineering - Approach

Data Science follows the process-oriented approach and allows pattern recognition, algorithms implementation etc. Software Engineering is framework-oriented that involves Waterfall, Spiral, agile frameworks and more.

Data Science Vs. Software Engineering - Tools

Data science involves data visualization tools, data analytics tools, and database tools. Software engineering involves programming tools, database tools, design tools, CMS tools, testing tools, integration tools etc.

Data Science Vs. Software Engineering - Platforms And Environments

Data science involves platforms like Hadoop, MapReduce, Spark, Data warehouse or Flink etc. Software Engineering involves platforms like data modeling, business planning, programming, maintenance, project management, reverse engineering etc.

Data Science Vs. Software Engineering - Required Skills

To become a data science expert, the person should know how to build data products. He should have the basic knowledge of domains, algorithms, big data processing, data mining, structure or unstructured data, statistics, probability, AI, machine learning etc.

Read: What Is Data Science? A Beginners Guide To Data Scientists

To become a software engineer, the person should have the knowledge of core programming languages, testing or build tools, configuration tools, release management tools etc.

Data Science vs Software Engineering:  General Key Differences

Now you know the basic concepts of Data Science and Software Engineering, let us look at the major comparisons between the two.

  • Data science comprises of machine learning, data analytics, and data architecture whereas software engineering is more of a framework that helps to deliver a high-quality software product.
  • A data analyst analyzes data and converts it into meaningful information. A software engineer helps to build software with maximum accuracy.
  • Data science is all about the big data whereas software engineering is the result of demand for new features and functionalities.
  • Data science helps in making good business decisions while software engineering makes the development of a software product more structured.
  • Data science is mostly driven by the data and software engineering is driven by the needs and requirements of end users.
  • Data science utilizes big data technologies to design data patterns. Software engineering is based on different programming languages and tools as per the Company requirements.
  • Data extraction is considered as one of the most useful steps in data science and requirement gathering and management is taken as a vital step in software engineering.
  • Software engineering is all about building apps or systems. Data science is all about building a data model that helps to consolidate, retrieve, and store data from multiple sources or applications.
  • Software engineering uses SDLC models to develop a software product systematically. It helps in product development step by step without any vulnerabilities. Data science uses ETL process for quick data management that involves data extraction, transforming it into useful information and loading the processed data into the system.

Wrapping up:

An important observation is that software design is made by the software developer based on the requirements as identified by the Data scientist or Data engineer. So, data science and software engineering usually go hand-in-hand. It is also useful to find information and patterns about specific function or product in data science.

Effective communication with clients or end-users helps to create more powerful business solutions because requirement gathering is the most important step in the SDLC. To know more on data science and its important tools and techniques, join data science certification program at JanBask Training and become a certified data scientist right away.

Read: What is Data Acquisition? Top 10 Data Acquisition Tools & Components

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