The trending field of data science offers a unique blend of demanding and exciting job profiles. The fact that data science does not discriminate is more important. If one is proficient in statistics and programming, they can join whether they come from a scientific, commercial, or artistic background. There will undoubtedly be obstacles and rejections along the way, but if one persists and develops their programming skills, they can expect a lucrative salary and career path. Although engineering is still a popular bachelor’s degree programme, a degree in engineering or any other stream is not absolutely necessary to pursue a career in data science.
Data science recruiters always look for skilled personnel and those who can process the massive data that is out there.
What do Data Scientist do?
As we just established, data scientists gather, purge, and interpret data using mathematics and programming. Their primary responsibility is to modify the mathematical and statistical models used to gather data. In other words, they find new data.
The role of data scientists is more crucial than ever in a society where every activity is recorded. Data scientists can focus on one of the following areas when used to inform business decisions:
Formal business problems are transformed by data scientists into data questions, which then have data-driven solutions.
They use data visualisation to convey information to stakeholders and team members who are less technically savvy.
Data architects, data engineers, machine learning engineers, and analytics managers are common job titles held by data scientists.
Yet the ability to code is what sets data scientists apart from the competition. Python, R, SQL, and other programming languages are all areas of expertise for data scientists.
Do data scientists differ from statisticians and data analysts?
Although their work is similar, data scientists and data analysts and statisticians differ in the following key ways:
The watchdogs of a company’s data, data analysts assist team members in comprehending and utilising that data to create data-driven, strategic decisions.
● Business analysts have a strategic role, leveraging the data they hold to identify issues and create solutions.
● On the other hand, data scientists use that data by sorting through it and categorising it to uncover flaws and trends, then use that data to build prediction models.
● Salary is one of, if not the greatest, distinction between analysts and data scientists. To put it simply, data scientists make significantly more money on average due to their technological expertise.
While dealing with data is the foundation of both professions, a data analyst functions more like a translator and has fewer technical requirements. A data scientist, on the other hand, serves in a hybrid role, assisting businesses in using coding to transform data into something useful and consumable. Data scientists and analysts are frequently confused, in part because analysts and researchers have existed for a very long time before “data” existed.
Data Science Without an Engineering Degree
Consider someone who does not have an engineering background who, after hearing or reading about the data science stream, gradually develops interest in it. One may decide to change careers after learning more about the available jobs and salaries.
Except in the case of computer science, the degree has been less significant in the data science field.
Narrow it down
Learning the fundamentals of data science and the job roles it offers is the first step. There are many other job profiles in the data science stream besides the data scientist role, some of which include:
Business analyst, data analyst, MIS reporting executive, data engineer
One must be aware of every job role associated with the data science field before attempting to select the most alluring role. Having a clear idea of the industry one wants to work in is preferable because, as someone without a technical background or an engineering degree, it may be better to focus on fewer options and become more specialised.
The Math and programming languages
Learning statistics and programming languages are the first things to learn for any data science role when one has decided to change career paths.
Learning a programming language may initially seem intimidating for someone without an engineering degree, but it is not difficult to learn. When compared to C and C++ or even Java, programming languages like Python and R, which are popular in data science, are thought to be easier to learn and practise.
The best programming languages to learn currently available are Python and R, with Python being the most preferred choice.
Be aware that learning programming is not the only requirement for a career in data science; in order to make the transition easier, one must also be familiar with statistics. It gets better the more one is familiar with it.
Get a few projects finished.
One needs to start working on practical projects once they have enough programming and math skills to demonstrate that the knowledge they have acquired is not just theoretical. This not only deepens understanding but also aids in portfolio development.
One must carefully construct a portfolio to avoid making mistakes. One can start practising their programming skills by creating a portfolio site using WordPress and a Github account.
Data Science Bootcamp
One should undoubtedly enroll in a data science boot camp if they have the time. One must go through demanding military-style scenarios in boot camp to hone their skill set. By attending boot camps, one can learn a variety of skills, including Python, R, and Hadoop, and gain the knowledge necessary to solve practical data science problems.
The Data Science Community
Always keep an eye out for data science summits and conferences. Such events offer the chance to network in addition to providing opportunities to learn. Even local data science meetups would offer a great chance for networking and community involvement.
Social media apps can also be used to join existing online communities or to create new ones.
Prepare for Data Science Interview
Once one has a solid understanding of data science and its job profiles, it is time to update and strengthen one’s resume. Begin contacting the network you have established and highlighting all the personal projects you are working on without using too many buzzwords.
Having a mentor is beneficial for fully understanding data science, and it’s always a good idea to look for ways to advance your skills.
Can I become data scientist without engineering degree: Conclusion
Apart from having a strong interest in data science and regardless of having an engineering degree, a person’s mathematical abilities are heavily emphasised in this field. Mathematics, statistics, and probability knowledge are helpful in identifying the issues and supporting algorithm development. It is a very exciting job profile. There are more job opportunities such as data scientist, business analyst, MIS reporting executive and data engineer. Find more exciting job profile.
Programming’s function could be added later. In addition to honing one’s skills through bootcamps and Github, one can benefit from the various MOOCs that are offered online.