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Data Analytics Vs Software Engineering – A Detailed Exposition

Have you been wondering about the differences between two booming fields, i.e., Data Analytics vs Software engineering? Do not overthink and waste your time searching for the same. Today, you will learn about both sectors in brief along with ways to enter them. We will also look at the list of crucial skills in each branch so that you can compare. i.e., Data Analytics vs Software engineering and make a career choice. You will find the conclusion at the end of the article if the body of the article does not clear your confusion.

Data Analytics Vs Software Engineering (1)

Data Analytics

Data Analytics is an approach that involves the use of specialized software and systems to improve the performance of a business through the examination of datasets to identify trends and patterns to draw meaningful insights for decision-making. Data Analytics techniques and technologies are applied in commercial industries to assist management in making better business decisions. If you ever become a data researcher or a data scientist, you will use specialized analytics tools to disprove or verify scientific theories, hypotheses, and models. Let me explain the differences in a simple way .i.e. comparison of Data Analytics vs Software engineering.

Software Engineering

Software engineering is a process that is carried out to analyze the requirements of the user to design, build, and test software applications to fulfill those requirements. It will help in eliminating low-quality projects and ensure that the clients get the benefit of receiving applications and software within the deadlines. If you are a software engineer, you would use a set of methodologies and rules to design and build software as per the given deadlines and budget limit of the client. You will also be responsible for monitoring software applications for regular upgrades. To put it short, a software engineer will create and deliver problem-solving software solutions to clients. 

Qualifications to Become a Data Professional

Having a degree in the relevant field will definitely help you become a data analyst. However, not all colleges offer a degree program in data analytics. That is when you must choose other options where data analysis is taught as a part of the course. Some of them are

Data Science:

Many institutes are offering degrees in data science and as a matter of fact, data analytics is one part of it. It means you can join the course and acquire skills that are used in data analytics fields like statistics, mathematics, and computer science. Some programs also give you the opportunity to specialize in certain fields like finance, economics, health care, or business.

Computer Science:

Many computer science programs involve classes that help students gain analytical and statistical skills. This point makes it attractive to data enthusiasts. This degree is available to you in many colleges. You must pick a program that has key concepts of data analytics included in it such as algorithm design, programming languages, and artificial intelligence, 

Applied Statistics or Mathematics:

Applied statistics and mathematics degrees are known to prepare students to deal with real-world applications, unlike traditional courses. This means learners like you can help businesses in making data-driven decisions. The curricula of the programs may include skills like statistical packages and programming languages. 

Finance/economics:

Many learners who love finance take up a degree in finance or economics as they include analysis and statistics as part of their curricula. Some institutes also provide finance programs that focus on business analytics along with financial subjects. Do you belong to this category? If so, follow both passions at once by joining a financial course. 

Psychology:

You may not be aware of the fact that many psychologists make use of data to explain, describe, and predict human behavior. It means applying to a bachelor’s program in psychology can expose you to statistical analysis and math along with domain-related topics.

Management Information Systems (MIS):

Obtaining a degree in this department will help you learn about databases and their usage in an organization. Choose this degree if you want to grasp in-depth knowledge of databases. The program curriculum usually includes data management, business theory, and database design. Some programs also let you get a glimpse of business intelligence, data management, or data analytics. 

Certifications:

Not all crash courses require you to have a degree in relevant fields. Some online programs accept applications from students with a bachelor’s degree in any domain. Certification programs usually have a duration of a minimum of 6 months. It means you do not have to worry about not having a degree in a particular field. You pick reliable institutes like IIM SKILLS and apply for the data analytics master course to become a data professional. 

List Of Professional Courses from IIM SKILLS

Qualifications to Become a Software Engineer

One way to understand Data Analytics vs Software engineering is by knowing about the qualifications required for each role. Let’s look at the eligibility criteria to apply for a software engineer role. 

Higher Secondary School Graduate:

The first thing you need to obtain before considering a software engineer job is a degree from higher secondary school. Because the syllabus covers crucial subjects like chemistry, physics, and mathematics. It is good if you can master basic coding and computer science concepts at school so that you can follow your passion in the future. This is recommended because it opens doors to national-level entrance exams such as JEE or any other entrance exam conducted by your country’s government to provide admissions into engineering colleges. 

Bachelor’s Degree:

Once you complete schooling, enter college to gain a degree in computer engineering, information technology, or computer science. Having a bachelor’s degree will make your life easier to become eligible for software engineering designations. Your degree is proof that you have knowledge about advanced software engineering. 

Entry-level Jobs:

The next step after acquiring essential knowledge from a bachelor’s degree is applying for jobs. It is important to remember that both non-IT and IT fields have software engineers. It means you can apply for entry-level positions post preparation of your resume and portfolio. One pro tip would be to customize your resume as per the company’s requirements to get shortlisted. 

Internship:

It is okay if you do not find a job after completion of graduation. There are many companies that offer internships to the budding software professionals. When you take up real-life projects, you get exposure to the working culture and can make new connections that can help you get an entry-level job. Some firms will offer you a full-time position if they are satisfied with your work. So, all you have to do is give your 100% and get noticed by the management. 

Master’s Degree:

The master’s program is usually chosen by undergraduate students to gain advanced skills in software engineering. It is not compulsory to do a master’s in software engineering, however having a master’s degree can help you attract higher salary packages. You can also pursue a master’s in software engineering while working as a software engineer in an organization. 

Diploma or Certifications:

If you want to get placed in well-reputed companies, then you must complete a certification course or a diploma from competent authorities. Doing so will let your potential employer know that you have expert knowledge and are proficient in the field, making them hire you. You may select one from “PG Diploma in Cyber Security, PG Diploma in Cloud Computing, or PG Diploma in Full-Stack Development.” You must consider yourself lucky as many ed-tech companies have come up with crash courses to help any person irrespective of their educational background to become a software professional. 

Attend Workshops and Conferences:

This point is like a tip to individuals who are already in the software field. It is being called a tip because individuals with knowledge in the field can easily grasp new software engineering concepts compared to those who do not have any knowledge. Learning about new changes and products in the industry can help you in applying for higher positions and get you hikes in your salary. There is no Data Analytics vs Software engineering when it comes to certifications and workshops as both fields allow students with certifications and knowledge.

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Skills for a Data Analyst

A data analyst is one who deals with data in organizations to promote informed decision-making. We have discussed that a data professional will use a variety of tools and techniques to complete the analysis. Now it is important to understand what skills they require to operate those technologies and tools. 

  • SQL
  • Statistical programming
  • Machine learning
  • Statistics and Probability
  • Data management
  • Statistical visualization
  • Econometrics

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SQL:

It is a programming language used for communicating with databases. When you are good at using SQL, you will find it easy to update, query, and organize data in relational databases. It also allows you to amend data structures. It is one of the most important languages as it is used by data analysts from all departments to access data from an organization’s database. This also sheds light on the technical screening at interviews. You will be asked a few questions on SQL. It is mandatory to be prepared to answer all the questions shot at you. 

Statistical Programming:

Statistical programming is one kind of programming language like Python and R. It is commonly used to perform functions that are not possible with Excel. Writing programs using this programming language will assist you in cleaning, analyzing, and visualizing large datasets with efficiency. 

R and Python are open-source languages. Learning them will benefit you and assist you to grow in the organization. Both languages can guide you to accomplish tasks related to data science. R is introduced to carry out analytics. Python is the most learned language for being easy and is very popular. 

Machine Learning:

It is a branch of AI that contributed to the success of various projects that involved data science. This tool is used to build algorithms and find patterns in large datasets to improve their accuracy. When a machine learning algorithm is used for handling data, it promotes smarter and more accurate predictions. Make sure to concentrate on machine learning classes as you will have to deal with it for critical tasks in the future. 

That being said, it is not mandatory to acquire machine learning skills as not all companies use or not all employers supervise data analysis that involves machine learning. Having skills related to machine learning is a plus to crack interviews with higher compensation. This is one skill that is not required for a software engineer and it clearly shows the difference between both streams (Data Analytics vs Software engineering)  in terms of the usage of technology. 

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Statistics and Probability:

Statistics is a subject that is related to the field of data science and math. Its relevance can be connected to the collection, analysis, and interpretation aspects of data science and math. When you are good at statistics and probability, you can detect trends and patterns in the selected data, Avoid fallacies, logical errors, and biases in your analysis, and contribute to the delivery of trustworthy and accurate results. 

Data Management:

Data management is a process that involves the collection, arrangement, and storage of data in a secure, cost-effective, and efficient way. Many organizations delegate this task to data architects, database administrators, IT security analysts, and data engineers. Data analysts also take part in managing data to some extent which is why it is important for you to learn data management. 

Statistical Visualization:

Obtaining insights from data is one task and presenting it in an understandable and appealing way is a crucial duty. When your presentation is clear and concise, the management will make better decisions. On the other hand, if it is clumsy and is not as per the set standards, it will lead to making wrong choices. This is why you must master the art of using graphs, maps, charts, and related visual elements for communicating your opinions and findings with the concerned teams. 

Learn to operate visualization tools such as Tableau and improve your data visualization skills. Using industry-standard software will give you the power to transform your analysis into data models, dashboards, business intelligence reports, and visualizations. 

Econometrics:

Econometrics is employed in companies for the application of mathematical and statistical data models to economics. It is done to predict trends as per the historical data. Having knowledge about econometrics can assist you in getting a job in the financial sector. Hedge funds and investment banks love to hire data analysts with expertise in econometrics. 

Skills for a Software Engineer

The main difference between data analytics and software engineering. i.e., Data Analytics vs Software engineering is the set of skills required for each role. We have learned about the faculties to become a data analyst. Now, let us look at essential technical skills for a software engineer. 

  • Computer programming and coding
  • Software Architecture
  • Software testing
  • DN (Software Defined Network)
  • Java
  • Python
  • C and C++
  • Scale

Computer Programming and Coding:

A software engineer will have almost all programming languages possessed by a web developer. Some of the languages known by a web developer are Python, Ruby, C.C++, and Jave. One common programming language for software engineers and data analysts is Python. A software engineer must also be familiar with programming fundamentals such as data algorithms and structures. If you are determined to become a software engineer, ensure to enroll yourself in coding bootcamps. This applies when you have limited knowledge of data science and want to expand your information base. 

Software Architecture:

As the name suggests, it is nothing but the organization in a system. This organization is known to have all elements, function settings, design principles, and their interactions. The designer of the software will begin the architecture with a goal in mind. The project must be completed without disturbing the goal. The behavior and structure of the software influence significant decisions. This is why you must build and use it properly to produce accurate outcomes. This skill definitely indirectly answers your question. . i.e., Data Analytics vs Software engineering?

Software Testing:

Though automation has taken over many mundane software-related tasks, it is crucial for you to know testing and debugging processes. Many activities in organizations are completed using AI to save time and money. However, testing is still a time-consuming and expensive component of a software development project. This means when you have testing skills, you can offer solutions to software projects. 

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DN (Software Defined Network)

Object-oriented Design (OOD):

The use of Object-oriented design for designing software has become popular in recent years. Its fundamental principles include encapsulation, abstraction, generalization, decomposition, and inheritance. It means when you acquire the skill to apply the principles of object-oriented design, you can create modular, reusable, and flexible software. 

Software Development:

Software engineers have the responsibility to lead a team that works on software development. You cannot lead a team without having knowledge about software development. This is why this skill is listed in the required skill set. As a software engineer, you will be expected to create software development programs and projects on computer systems.

Software Engineering Programming Skills and Languages

You have to be an expert in one programming language to fill the position of a software engineer who can thrive in software development and earn higher compensation. 

Java: Java is a language that will allow programmers to create applications that can be operated on different computer platforms. It is used to develop video games. Though C++ is also used for developing video games, Java is preferred because it is less complex. 

Python: Python is one of the trending programming languages that is used in many programs. As mentioned earlier, this one language connects software engineers and data analysts as it is also used for data analytics. 

C and C++: Many game developers prefer C/C++. That being said, C is still chosen by software developers for creating computer systems. Being proficient in C/C++ can get you placed in top companies. 

Scale: If there is one simple functional programming language to learn, that is Scale. No wonder, many software engineers are pros at using it for various purposes related to the software development process.

 

Conclusion on Data Analytics Vs Software Engineering

Are you still thinking about Data Analytics vs Software engineering to make a career choice? Okay, let me help you. If you are interested in software development, opt for software engineering because it deals with software development projects. On the other hand, if you are passionate about using data analytics tools and technologies, jump into data analytics.

If you decide to become a software engineer, ensure to have the below skills.

  • Computer programming and coding
  • Software Architecture
  • Software testing
  • DN (Software Defined Network)
  • Java
  • Python
  • C and C++
  • Scale

If you have made up your mind to join the data analytics field, gain the below skills from reliable training centers.

  • SQL
  • Statistical programming
  • Machine learning
  • Statistics and Probability
  • Data management
  • Statistical visualization
  • Econometrics

One major difference between these two streams. .i.e., Data Analytics vs Software engineering is the mode of entry. You have to have relevant degrees or certifications to serve the companies through your dream role. 

 

FAQs on Data Analytics Vs Software Engineering

Q. Data Analytics vs Software engineering? Which is best for me?

You must select a software engineering field if you are into software development and architecture. On the other hand, if you love the application of data analytics tools and techniques to challenging organizational tasks, prefer data analytics. 

Q. What skills should I acquire to become a software engineer?

You must gain the below set of skills to thrive in the software field.

  • Computer programming and coding
  • Software Architecture
  • Software testing
  • DN (Software Defined Network)
  • Java
  • Python
  • C and C++
  • Scale

Q. What skills should I acquire to become a data analyst?

You must gain the below set of skills to thrive in the data analytics field.

  • SQL
  • Statistical programming
  • Machine learning
  • Statistics and Probability
  • Data management
  • Statistical visualization
  • Econometrics

 

 

 

Anuja Maniyala is a intern at IIM SKILLS. She worked as a creative content writer for AADOX and Quoteslyfe in the past. She has identified her passion for writing after working as a banker in some well-known companies like Wipro and HSBC. Her current target is to become an author of a unique and creative self-help book. Her enthusiasm and curiosity to learn about the human mind and behavior makes her different from the rest of the world.

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