Fret not, A career in Data Science for non-techies too!
Data is everywhere, each and every organization is generating data in large amounts and are in need of data scientists/ analysts who can help them in extracting useful insights. Choosing data science as your career is an excellent option as you would only grow higher in achieving better opportunities.
We see many articles talking about the technical aspects of switching to a data science career such as python, machine learning, domain expertise, etc., But what about a person from a non-technical background?
Relax, all you need is a little more effort to put in and a little more time to make your footmark. The three basic checkpoints to keep in mind while stepping into this career are:
Decide the job you desire to work for: Strong determination is enough to enter in this field is a good start. But “where and how to start?” is the question to be looked into. Knowing about few job titles in data science field would help gain a brief idea about the roles available.
INFOGRAPHIC
- Data Analyst
- Data Scientist
- Data Engineer
- Machine Learning Engineer
- Quantitative Analyst
- Data Warehouse Architect
- Business Intelligence Analyst
- Statistician
- Business Analyst
- Systems Analyst
- Marketing Analyst
- Operations Analyst
You can go through the job profile of each role online and gain comprehensive knowledge about the job duties. Understand, analyse, and compare it against your skill set/choices, and decide the job role.
Discover your dream company: Now, make a list of best companies in the industry, and pick your choice. Get the minute details about company, job profile, and how the company can help you in offering opportunities and contribute to your overall growth.
Develop your network for the job: You might still have questions to be answered. It will be a better choice if you get a chance to meet experienced delegates in the related field, But how to approach them?
Search for them on LinkedIn and approach them with a simple message. It helps to gain a better understanding of your strengths and to find out specific areas that need extra concentration.
Let us look into the common requirements for individuals with technical and non-technical backgrounds.
Technical Skills
- Python Coding- Learn Python! It is the most versatile and comprehensible programming language.
- Mathematics- All the mathematics that you learned from your school to graduation is not needed. The topics like linear algebra, matrices, scalars, vectors, and probability are some of the topics to start with.
- Statistics – You could start by brushing up on few fundamental topics of Statistics such as mean, median, and mode and then subjects like correlation, sampling, and distribution to get overall command over the domain.
- Domain Expertise– We have mentioned domain expertise under technical skills as the business problem could be technical. And without the domain knowledge, it is difficult to relate to the business requirement or the end goal.
- Data Visualization– Simply, it is a representation of the data in a pictorial form like charts, graphs, etc.
Non-Technical Skills:
- Intellectual curiosity– The fundamental approach is to find the root cause of any business problem. Intellectual curiosity leads us to predictive analysis that allows us to peep into the future by showing us the possibilities.
- Communication Skills– To be successful in your role, you should be able to articulate your understanding with someone who is also a non-technical user. Therefore, you need to have strong communication skills as a data scientist.
- Teamwork– You need to liaise with multiple teams as a data science professional. Each team would be dealing with certain parts of the data at different stages.
Now you know what all you need to do, invest your time and effort! Do not give up even if you are derailed a few times in your path. Mastering all the mentioned areas and being focused would lead you to achieve your goal. It is okay, even if you spend a few months on a qualitative study. Keep mastering, and you are already halfway in your switch as a Data Science professional.
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