Are you thinking of ways to switch to a Data Science career? If yes, then you’re at the right place. As you might be aware, organizations are investing a good amount in Data Science roles, ultimately increasing the demand for it. And, to take advantage of this trend, this is the best time to get started with preparing for the career switch into Data Scientist because it is the most in-demand skill that top companies are looking for.
In this blog of career switch guide from Non-Tech to Data Scientist, we’ll see problems faced and skills required for individuals from Non-Tech backgrounds, and how to become data analysts for non-IT people.
Why Switch To Data Science?
If you’re willing to switch from Non-Tech to Data Scientist, you might have a question, why there’s a need to switch to Data Science? Data consumption and production have increased in the past few years and will continue to increase so. The data generated should be utilized to extract insights from it. And, therefore, as said before, organizations are investing huge resources to hire Data Science professionals.
The Harvard Business Review declared Data Science as the most in-demand job of the 21st Century. Not just this, the monetary benefits of the field are high due to high demand. Let’s see some of the facts regarding the same. According to Naukri.com, the average salary of a data scientist with only 1 year of experience is INR 6.7 Lakh per year.
From these facts, we can therefore conclude that data scientist positions offer a bunch of amazing options to aspirants and that employers will offer a competitive data scientist wage package. Hence, many are in this field. But, it is not at all easy to enter this job profile. Despite the huge demand, candidates fail to crack the data science interview. The reason behind this is the difference between what skills are taught in Data Science and what is actually needed in a real-world job. It is important to work and practice practical aspects before working as a Data Scientist.
Non-tech To Data scientist: A Step-By-Step Guide To Becoming Data Scientist
Despite the possibility for any individual to enter the field, it’s important to gain the appropriate skills and knowledge for one to get success in the domain. Let’s see a step-by-step guide on the journey of a data analyst for non-it or any other position.
Step 1: Start From The Basics
For one to become an expert in any field, it is important to get cleared with the basics of the concept. An aspirant needs to start learning from scratch.
Data Science is the technical domain, and for people from the Non-Tech field, it is important to get familiar with some basic technical concepts. These concepts are but are not limited to:
- Programming languages like Python, R
- Mathematical concepts like statistics, probability
- Excel – Advance level
- Basics of Machine Learning
- After clearing the basics, the individual can move to the second step.
Step 2: Get Started With Learning Through A Data Science Course
Data Science is a complex field, especially for a person from a Non-tech bg. After an individual is familiar with the technical concepts, he/she should get enrolled in the Data Science course.
The course must cover the following concepts:
- Programming languages
- Data visualization
- Big Data
- Machine learning
To select the best course, consider questioning the below-mentioned questions:
- Are all the important topics included in the course curriculum?
- Is practical training included in the course structure?
- Who and how many hiring partners are associated with the institute?
- What are the reviews of students for the institute?
Step 3: Learn The Difference Between Concepts Like AI, ML, DL, Etc
Data Science has various interrelated and correlated topics such as:
- Statistics: a field of mathematics that deals with gathering, analyzing, interpreting, and presenting vast amounts of numerical data
- Machine Learning: the usage of and research into computer systems that can adapt and learn without being given detailed instructions
- Deep learning: a form of artificial neural network-based machine learning wherein the data is processed through multiple layers to extract increasingly complex properties
- Artificial Intelligence: systems or devices that can accomplish tasks and can continually refine themselves depending on the data they gather by mimicking human intelligence.
Step 4: Practice On Real-World Projects
The key to landing a data science position at one of the top, well-known firms are gaining practice, training, and experience. To do this, one must concentrate on developing a collection of projects that are aimed at resolving obstacles and errors in the actual world.
It goes without saying that there will be a lot of applicants competing for the exact same data scientist employment. Therefore, choosing more specialized project learning over academic learning is a guaranteed method to stand out from the crowd.
These projects also demonstrate one's capacity to apply theoretical knowledge to the development of data modeling that affects society and business. These were the career guide for individuals to switch from Non-tech to Data scientists. Now, let’s see the skills to become a Data Science expert:
Non-tech to Data scientist: Skills Required To Become Data Scientist
In the field of Data Science, there are two types of important skills:
- Technical skills
- Non-Technical skills
Technical Skills Required
Skill 1: Programming
Programming is fundamentally what data science is. Programming Knowledge for Data Science gathers all the core abilities required to convert unstructured data into useful insights. Although there isn't a set rule for choosing a programming language, Python and R are the most recommended ones. Data scientists pick a programming language based on the requirements of the situation at hand. However, Python appears to have gotten the closest to being the most preferred language of data science.
Skill 2: Working With Unstructured Data
Data is obtained from multiple sources. And, thus, it is not always possible to get hands-on well-organized data. Data scientists need to have prior expertise in working with unorganized data from many sources and channels. For instance, a data scientist should be skilled in monitoring social media if they are working on a project to assist the marketing team in providing insightful research.
Skill 3: Data Wrangling
The data that a firm obtains or gets is frequently unfit for analysis. Therefore, it is crucial to comprehend and be knowledgeable about how to handle data flaws. The process of changing and translating unstructured data from one format to another in order to prepare the data for analysis is known as "data wrangling." Basically, data wrangling involves gathering data, combining important fields, and cleaning the data.
Skill 4: Data Visualization
One of the most important skills is data visualization because it goes beyond simply displaying the findings to include understanding the data as well as its limitations. Visual representation of concepts is always preferable because they are more well-known and understood. Data scientists always get useful data when they make a visualization, which is unexpected given that it can affect the system.
Skill 5: Machine Learning
Data Scientists are required to work on a huge amount of data sets and manage the same. This data is used for the decision-making and finalizing process. The skill required here is machine learning. Machine learning is a component of data science that works on data and works on modeling data and getting the results.
Non-Technical Skills Required
If you’re the one trying to switch from non-tech to data science, then it is possible you already have the below-mentioned skills.
Skill 1: Critical Thinking
Critical thinking is a skill that is important and applicable to any career. For data scientists, it's even more crucial because, in addition to discovering insights, one also needs to be able to frame questions correctly, comprehend how the results connect to the business, or suggest actionable next steps. In the discipline of data science, critical thinking implies all potential solutions to an issue and maintains an ongoing sense of curiosity for every answer.
Skill 2: Communication skill
Communication is more than simply face-to-face interaction; it also includes writing correctly and confidently presenting technical concepts to peers, co-workers, and superiors. Data Science is a jargon-heavy profession, thus it's important to fully comprehend the concept before attempting to communicate it to a non-Data Science audience.
Skill 3: Problem-solving/ proactive nature
Being a data scientist requires problem-solving talent and motivation. Data science focuses on achieving that. But in order to tackle a problem effectively, one must have both the ability and the willingness to go deep into the cause of the problem. The top problem solvers are capable of rapidly pinpointing challenging problems that are often left unseen and suggesting the best approaches for handling them.
Skill 4: Teamwork
Teamwork is equally important to communication because an individual's tech competence is also evaluated based on how effectively it fits into a team. Even though they are judged on their own merit, a person's capacity to collaborate with other people is a clear sign that their tech skills are on the same level. As a result, new original thoughts emerge and flow to enhance productivity.
Skill 5: Organization skills
Organizing is one topic that is typically never brought up explicitly. The ability to be organized demonstrates a candidate's aptitude for setting priorities, working through problems methodically, and completing activities on schedule.
They can control their time better as a result, better meeting work deadlines.
Entry into the world of data science for those without technical backgrounds is not the simplest task, but it is also not impossible. Because there is a lot of learning, unlearning, and relearning involved, it is a challenging route to follow.
Prioritizing mastering the fundamentals before advancing to more complex applications is a wise strategy, as is establishing a connection with a mentor or professionals to gain insight into the subject.
Engaging with the greater community for data science and staying updated with industry trends will enhance your resume just as much as practical experience and academic credentials!
Q1. Can a non-tech person do data science?
A non-tech person can definitely shift to the Data science field. Although many data scientists have backgrounds in technology or mathematics, you shouldn't let that stop you from wanting to work in the field. The journey can be a bit challenging but fun as well.
Q2. Is data science hard for non-technical students?
Not just for non-tech people, but data science is a complex field. The reason behind this is that the field requires a huge set of abilities and skills. Well, with good training and practice one can easily learn Data science.
Q3. Does data science require technical background?
Data science is a technical field but many hold the misconception of the compulsion of the technical field. Any individual from a non-tech background can decide and pursue the field of Data science.
Q4. Can I move from non-IT to IT?
Switching career from non-IT to IT is possible. One can decide to switch from non-tech to Data scientist or data analyst for non-it people. The person must work on clearing the basics first and gain practical exposure to grow in the corporate world.