Data science is constantly growing and evolving, and a new application for data science is often found every day. From the healthcare field to the financial field, career options in Data Science are numerous, and it’s a profitable industry to enter as it offers scope to individuals in the form of raising aptitude and analytical skills.
But while it’s clear that data science is an emerging industry, entering the field isn’t as easy as it seems, requiring varied skills. Becoming an expert in the field of data science isn’t as simple as landing another job with a six-figure salary, like a lawyer, CA, or doctor. Many experts in this field suggest learning data science skills to get over this field and achieve better results than what you have not thought about.
The recent U.S. Bureau of Labor Statistics Survey suggests that opportunities in data science with data science skills will drive a hike of around 28% in employment through 2026. There is a massive demand for skilled data engineers and a need for more qualified data scientists and related professionals.
That’s why we’ve written this guide. Below, we’ll discuss some of the most common data science opportunities that an individual can eye for
Data Analyst
The demand for data science is gaining popularity and will be long-lasting. All the data that comes to the servers must be sorted accordingly to be correctly analyzed. This is the domain of a Data Analyst. To become a Data Analyst, you need to earn a bachelor’s degree in computer science and an additional degree in Data analysis. This will help you secure a worthy position in the organization you want to actually work in. You can also check the career clarity service.
Data Scientist
The work of a Data Scientist and Data Analyst lies in the same domain, but Data scientists pay more attention to building new machine models. These models help construct newer teaching methods and elaborate on the specifics of a program, topic, or concept. As a Data Scientist, you can often choose and pursue your ideas. The path is the same—a bachelor’s degree in Computer science and a master’s degree in the respective field. Infield experience counts a lot if you aim to become a Data Scientist.
Data Engineer
Their work revolves around building and storing the system that aids a company in storing immense and impeding data. The skills one needs to harbour while working under this title is the ability to meticulously derive software that will decrease a company's burden while analyzing a data sheet. Along with the basic bachelor's and a diploma-certified course, one must have in-depth knowledge of working with software like Phyton, SPS and many more. All these qualities emphasize the need for a dedicated person to do this job.
Quantitative Analyst
The people who opt for a Quantitative Analyst job should have a master’s degree in the Data science field. It is, to a particular degree, gaining immense popularity in the present era. Some companies ask for a postgraduate degree in data science if you aim for a super senior post. You can suggest core business insights to the organization or company in this job. It is not necessarily pure computer science, but it offers the advantage of being a more versatile job. They also help the company assess the risk and guide it in predicting future fluctuations in the field.
System Analyst
As a system analyst, you must keep track of the systems that are most used in your company. It also implies that one must have in-depth knowledge about the workings of various systems. They oversee every work to be done by the system and rectify if there has been any mishap. Easier said than done; a system analyst must always be glued to the desktop screen during working hours. The chance of a system crashing is always high, making the job even more challenging. To be a system analyst, you must also have an additional degree in the system working, their malfunctions, and the basic qualifications.
Conclusion
These are just some fantastic job opportunities that await you after graduation. With the world relying increasingly on big data, the demand for data professionals will undoubtedly skyrocket!
The comprehensive subjects offered in our online Master of Data Science program are packed with knowledge and skills-training coursework that will prepare you for extensive data management. Whether you’re interested in becoming a data scientist, a database developer, or even a role not listed above, you can rest assured that Chandigarh University Online, among other options, provides an online M.Sc in Data Science that can help you achieve your dream job.
Frequently Asked Questions
1. What core steps help you become a data scientist?
Here is a process for becoming a data scientist.
Take online courses and enhance your skills in machine learning, math, and statistics
Learn how to take advantage of Python and R for coding.
Achieve an undergraduate degree in data science or something related to this field.
Understand the core handling of the database so you can effectively retrieve information from it.
Explore the field of big data and its career scopes.
Attend competitions and network with data scientists to learn more about the field.
Take up an internship to gain industrial exposure and gain experience in the field.
2. Which is the best language for data science?
The programmers are using two types of programming languages – low-level and high-level. Beginners in the field use low-level languages like machine language and assembly language. Many data scientists use high-level programming languages to increase efficiency and stability while learning something new about their subject. Some of the widely used programming languages for this data science field include:
1. Python
2. JavaScript
3. Scala
4. R
5. SQL
6. Julia
Programmers use any programming language based on their requirements and level of understanding.
3. Do data scientists take on the use of C++ programming language?
Programmers actively placed in this computing language discuss the pros and cons of C++ and related languages. While Python and R are considered the most popular languages for data science, certain data science professionals see C and C++ as efficient and effective choices.
Due to their numerous opportunities, developers have found the traditional C and C++ languages helpful for data science development. Not to deny that numerous individuals have found C++ to be the best programming language that they prefer using this field with a better understanding of the subjects.
