How to Switch to Data Science for Non-Computer Background Techies
You can get into data science for the non-computer background without the hassle by following these steps
By 2022, global revenues for data science and business analytics are projected to reach US$210 billion. Not surprisingly, as demand grows, there is a shortage of skilled data science professionals, and companies are embracing the industry and looking for professionals who are willing to open their arms and enter the workforce. However, the gap between existing and required skills is very large, and there is no doubt that the gap between supply and demand is widening. It’s a lucrative employment opportunity for enthusiasts of data science for non-computer background domains in areas that once grew and penetrated only into other industries, no matter how far away they were. If you want to close the gap from being a data science expert from a noncomputer background to a data science expert and related disciplines, here are a few simple steps.
Step 1: Identify your dream job
Data science is a very broad discipline, allowing you to set goals and eliminate skills that you do not need at this point as you streamline and work towards your ideal job.
In technical work, most roles require these skills
- Mathematics
- Statistics
- Programming
- Business knowledge
This is because data science professionals and analysts play a role in providing insights to stakeholders and professionals in other disciplines. The long-term goal of data science in the business context is to create business planning and insights that can drive future goals such as increasing sales, driving sales, and hiring talented people. However, the technical skills that will help you in your dream job are not determined until you understand the path you want to follow. Another important step is to assess the current knowledge applicable to this area, even if it is from another area. This is especially true for graduates of economics, mathematics, statistics, or business administration. The facets of these streams are well integrated into data science, so take advantage of them.
Step 2: Learn new skills through a data science course
Data science is a complex area that is intertwined with aspects of different industries. To get a good start as a data science expert from a non-computer background with little or no knowledge, candidates must continue their education by enrolling in a well-reviewed course at a leading institution or course provider.
The ideal curriculum should cover the following topics:
- Programming basics (Java, R, Python)
- Deep learning
- Data visualization
- Statistics and probabilities
- Handling of big data
These are the easiest and most organized way of steps to become a data science professional
because if you’re going on a research journey alone, it takes time to gather relevant resources and figure out where to start. Moreover, it is nearly impossible to set yourself on a mission to learn all the skills within the umbrella of data science. Data science professional courses are the best place to get a good start, as certain skills are also based on experience and human interaction. Curated by veterans in this field, these courses usually offer the additional benefits of career counseling, internships, and mentoring programs by industry professionals.
Step 3: Business problems and how badly you need to automate decision-making
If you’re lucky, you may be able to find a free course or open-source program that offers only the jump start you need. Once you’ve gone through it and understood what you’re looking for in a course, you can decide which paid certification you want to opt for.
Today, almost every industry is better organized according to best practices and is beginning to adopt redundant process automation. You will find common practical processes that can be automated using data science. You can also formulate business problems, work towards business outcomes, and initiate a proof of concept [POC].
Step 4: Big data and statistical techniques
Data science and its hybrids rely on vast numbers and statistics. Understand how big data and statistical methods need to be mastered in order to understand data science for non-computer background techies.
Big data: A set of data that traditional software/algorithms cannot manage, process, or analyze in a reasonable amount of time.
Statistics: Significant events have triggered the current rapid growth in the use of analytical decision-making, and statistics are at the heart of all of them.
Step 5: Find mentors in the field
There are many certified data science professionals from large universities and educational technology companies. Before deciding which one to use, you need to analyze the topics and techniques of the course used by the mentor.
No matter what area you decide to start with, finding a foothold is always difficult. The same applies to the professionals in data science for non-computer backgrounds, but finding a mentor can benefit those who want to enter into IT courses for non-IT background techies.
