The global shortage in talent supply is creating a golden opportunity for beginners, graduates, and software engineers to make a career transition to data science. Data science is all set to revolutionize careers, including yours. From social media posts to e-commerce purchase transactions, digital videos, and pictures in smartphones to sensors in shopping malls, the world today generates a whopping 2.5 quintillion bytes of data every day.
Enterprises, flooded with massive amounts of raw, unstructured data, or Big Data, are deploying a range of scientific methods to extract actionable insights from Big Data, which the growth of the Internet of Things (IoT) has accelerated.
The study of Big Data to gain business intelligence is known as data science. Insights from data science, processed via algorithms, processes, and systems, help organizations tap into new markets and demographics, measure effectiveness, and lower their costs.
No wonder why there is an ever-increasing demand for specialists in the data science field. Skilled and certified data science experts, or data scientists, according to Glassdoor, are one of the highest-paid IT professionals in the US, with an annual average salary of $119,235.
There is no doubt that data science, these days, has a vital role to play in the success of an organization, which, in fact, is driving the phenomenal growth in data science-related professions. Despite the booming demand, American management consulting firm, McKinsey, predicts a massive gap of 50 percent in the supply of skilled data science practitioners versus the growing need for data scientists.
For Software Engineers Transitioning to Data Science
A software engineer’s existing skills, such as their code deploying experience, and their ability to communicate with developer teams, are a tremendous asset in the data science field.
For a smooth career transition to data science, software engineers should stop getting pigeon-holed in their current position, and instead, try to enhance their skills in both frontend and backend-focused areas. More familiarity with both roles will transform you into a full stack developer, which is not only in high demand, but it can also create openings and kick-start your career as a data scientist.
The transition is, however, much easier for professionals with Machine Learning (ML) skills. Machine learning applications are closest to data science-related roles, and ML engineers can leverage their existing skill-set, including deploying models and incorporating them into apps, driving their career growth in data science.
There is one thing that needs consideration. A salary cut often accompanies the transition to data science, so software engineers, aspiring to change, should be ready to start their career in data science as junior-level professionals, with a pay cut.
Career Paths in Data Science for Graduates
If you are a mathematics, statistics, or computer science graduate, your chances of excelling in the evolving data science field are high.
But just being a graduate won’t help you much in landing a job as a data scientist, even if you have a solid foundation in mathematics and statistics, or have a basic-level coding experience during your computer science degree course.
To make an impression, and to boost your career growth in data science, you need to learn and have work-ready experience in a variety of programming languages, such as R, SAS, Python, Tableau, Hadoop and Spark.
Learning test-driven Python development and understanding SQL (Structured Query Language) is a must. You also need to grow your ability to collaborate with other developers on GitHub, learn about deploying models on the cloud, and know the use of Docker and docstrings.
A PG in Data Science, which offers a perfect blend of extensive case studies, theory, and hands-on practicum, is ideal for new statistics, computer science, and mathematics graduates.
Career Transition to Data Science for Beginners
If you are a beginner without a relevant degree or work experience, then your chances of cracking an interview for a data science job are lower than a computer science graduate or an employed software engineer.
Serious beginners, however, have a major competitive advantage. Many tech companies prefer self-taught, self-made data scientists, because of their natural ability to work hard and learn fast.
To start with, you need to do comprehensive research about the latest developments happening in the data science field. Study blogs, follow YouTube channels and connect with data science professionals on employment-oriented services like LinkedIn, to learn about the basics of data science, such as data wrangling and developing data pipelines.
The above information will help you develop an understanding of data science. If you think a career in data science is for you, enroll yourself in a course offered by an institute like Simplilearn, which has state-of-the-art blended learning modules for upskilling. Designed for all, no matter what your level of knowledge about statistics, analytics, or coding is, Simplilearn’s Data Analytics for beginners, is the launchpad that will catapult you into the domain of data science.
You can further accelerate your career journey to data science by pursuing our program like PG in Data Science that will make you industry-ready.
The applied approach to learning is super effective because it does not restrict students to physical classrooms. The blended learning module includes both traditional and virtual environments, which allow students to learn at their own pace, anytime, and from anywhere.
Using measurable programs, engaging games, and interactive apps to teach, Simplilearn’s proven and customized applied learning solutions are ideal for employees and beginners who are eager to upskill their data science know-how. Explore and enroll in our Advanced Certificate Program In Data Science in collaboration with Purdue University, and scale up your career today!