Data science after engineering is a strong career option that’s becoming popular day by day. The skills acquired in each domain complement each other well and directly impact the chances of getting a job. For instance, engineering graduates already possess analytical thinking, programming and mathematical skills, which, once coupled with math concepts like probability, linear algebra and calculus, domain knowledge, teamwork and project management, create a strong portfolio. With data science and engineering together, you can achieve roles in AI, analytics, big data, business consultancy, and so on.
Introduction
As an engineering student, what to do next is always a looming question. Should it be a master’s in tech? Or should it be job preparation? In recent times, a new vista has opened up for engineers – data science. It is an up-and-coming field that has consistently grown over the last few years and continues to expand. The job opportunities and pay ranges are also incredible. Thus, data science after engineering is becoming a popular career option for many.
To truly understand its benefits, skills and how to grow in this domain, continue reading our guide!
Why Is Data Science a Good Career Option?
Over the last few years, data science has acquired significant visibility. For active project management and smart progress, companies are relying on data science and its advantages. Here are some reasons why data science is a great career option:
- Data science is a constantly growing field. According to the Bureau of Labour, data science graduates can expect a 15% growth in job opportunities between 2019 and 2029.
- Since it is a skills-based career, anyone can learn and master these skills to grow their career. What’s important is staying on top of industry trends and keeping oneself on one’s toes.
- The skills acquired from data science are transferable. What you learn here can be applied to many other careers.
- It’s a dynamic field that interests candidates who dislike stagnant fields. Its ever-evolving nature is great for people who like challenges, analytics and problem-solving.
- Data Science holds the future. Information and data never expire. Even though teams are moving towards automation for organising and processing data, analysing it and drawing insights is heavily dependent on data scientists.
How Do Data Science and Engineering Complement Each Other?
Data science after engineering is becoming an increasingly viable field because the skills acquired for each domain complement each other very well. Engineers are already well-equipped with good analytical thinking, programming skills like C++, Python, and Java, mathematical foundations, statistics, practical learning, and technological flexibility.
Similarly, skills like programming principles, pure math concepts like probability, linear algebra and calculus, domain knowledge, teamwork and project management are honed by the study of data science. When beginning a career, these skills will come in handy to get a good job. Since data science and engineering together have a very high demand in technical fields, by acquiring skills in both, you’re looking at higher pay, a variety of domain options, future job security and global opportunities.
Popular Job Roles and Average Salary
As an engineer, there are many interesting data science-related jobs that you can target. These are well-suited to engineers who understand and enjoy developing models, computing data and dealing with real-world problems.
- Data Scientist
- Data Analyst
- ML/AI Engineer
- Business Analyst
- Data Science Consultant
- Big Data Engineer
A core engineering job from an accredited college typically offers an average starting salary of INR 2.5 to 5 LPA. But, when you couple that with data science, your average starting salary increases to INR 6 to 10 LPA. In fact, the potential for salary growth also increases significantly.
Advantages Of Data Science After Engineering
As discussed in the above section, great job opportunities and good salary packages are the biggest advantages of pursuing data science after engineering. In addition to that, here are some others:
- Exponential career growth due to added skills.
- High-demand job opportunities for data scientists.
- Increase in demand for new job roles, due to artificial intelligence, machine learning and natural language processing.
- Healthcare, manufacturing, finance and technology are also employing data scientists who have a strong technical foundation.
- Increased automation in companies creates job opportunities for high-end analytical positions.
- Data science after engineering generates a higher salary compared to simply core engineering.
- Flexible job options.
- Several opportunities to support continuous learning and career growth.
How to Get Started With Data Science After Engineering?
If you’re confused about how to get started with data science after engineering, here are some tips that will help you begin:
- Begin with the essentials of data science: Python, statistics, probability, machine learning, and data visualisation.
- Familiarise yourself with key frameworks and tools. These include R, SQL, Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Tableau, Power BI, Hadoop, and Spark.
- Get hands-on experience through real-world projects. Think about predictive models, data cleaning, preprocessing, and visualisation dashboards.
- Research the various specialisations available and see which resonates with you. Natural Language Processing, Computer Vision, Cloud AI, Deep Learning, and Big Data Engineering are all lucrative fields.
- Build a strong portfolio, either on GitHub or through online blogs, to showcase your abilities.
- Consider obtaining industry-recognised certifications.
- Finally, prepare for interviews by practising coding, data processing and analysis, and situational questions.
Mailam Engineering College: Learn with the Best
Established in 1998 in Tamil Nadu, Mailam Engineering College is a private, AICTE-approved institution affiliated with Anna University, Chennai. It is known for its strong academic foundation and industry-aligned programmes. At MEC, we offer a wide range of undergraduate and postgraduate engineering courses structured to meet the requirements of modern industries. Moreover, practical learning, career focus, placements, networking and recruiter connections are given extra focus.
If you wish to learn more about admissions in the engineering stream, read this blog here: Engineering College Admission: Process & Eligibility Criteria.
Conclusion: Data Science – A Viable Career after Engineering
After these discussions, it is evident that data science after engineering is a viable career option. The strong analytical, programming and problem-solving skills complement the core requirements of data science roles well. Moreover, data science after engineering offers better job roles, higher pay, more global opportunities and a strong career path. Candidates can combine the strengths of both to build an impressive portfolio. And by consistently learning more skills and keeping up with industry developments, candidates can grow their careers much faster.
FAQs
Q1. Can I become a data scientist after engineering?
Ans: Yes, becoming a data scientist after engineering is completely possible. Students need to begin by mastering fundamental skills, getting real-world exposure, and learning practical applications. Together, data science and engineering can get you a very good job with high pay.
Q2. What is the average salary of a data scientist after engineering?
Ans: Data science after engineering can get you an average starting salary of INR 6 to 10 LPA. In fact, an experienced professional may also earn up to INR 40 LPA.
Q3. Is Python or R better for data science?
Ans: R programming is better for data science, as it’s more suitable for statistical learning. But if you’re concerned with ML and large-scale applications, Python will be a better choice.
Q4. Is data science more math or coding?
Ans: Data science does not particularly involve extensive coding. It is more concerned with extracting insights from data using maths, statistics and programming.
Q5. What is the most used language for data science?
Ans: Python, C++ and Java are some of the most popular languages in data science.
