Data science is an in-demand career field with strong job prospects, and believe me, now is the best time to step into this field. And the best part if you don’t even need prior experience to become a data scientist.
There are plenty of ways to acquire the skills needed to become a data science on your own. But again, getting skills and then a decent job is a different thing. It’s not that easy to get a decent job be it in any field. Below you’ll find steps that will help you to break into data science without previous experience.
Step 1: Polish your math skills
For individuals coming from a quantitative background, data science should be an easy shift. But for individuals who don’t have a mathematical background should focus on improving their mathematical skills because mathematics plays a vital role in data science.
In the data science process, before analyzing data with high-tech tools, data scientists are required to get to the foundation of data analysis, which begins with plotting data points on graphs along the X and Y axes and finding trends and correlations between different variables.
To make sure you can draw accurate conclusions and write efficient codes, here are some suggested mathematics concepts you should master:
- Statistical modeling and fitting
- Data summaries and descriptive statistics
- Regression analysis
- Bayesian thinking and modeling
- Markov chains
- Statistical methods and probability theory
- Probability distributions
- Multivariable calculus
- Linear algebra
- Hypothesis testing
Step 2: Learn one or two programming languages
Once you have a solid foundation in mathematics, you can start to pick up a few of the must-know programming languages for data science i.e, Python, SQL, R, and SAS.
- Python is used for software development, web development, deep learning, and machine learning. Python is the most generally used language in the field of data science.
- R is useful for complicated statistical and mathematical calculations. It is also useful in data visualizations and has a great support community to help you get started.
Step 3: Take on side projects or internships
Companies will ask to see your professional practical experience. So, as you start building your skill base, make sure you are applying your skill set in real-world settings. For that, you can join internships. Internships are a great way to implement skills in the real world as well as a great way to learn new things right from the experts. It will also build your resume.
You can also go for freelancing but getting a freelance project isn’t that easy, especially when you are just getting started in a periocular field.
Before going for interviews, make sure you have something to show as an example of past work on your resume, it can be an internship or a freelance project.
Step 4- Put yourself out in the market
Once you’ve got the right skills and few projects on your portfolio, it’s time to hit the job portals and start looking for cool jobs out in the market. Along with it try to contact recruiters and build your network to get your breakthrough into the field.
Attend recruiting events, career fairs, and start searching for jobs on platforms like LinkedIn, or directly on company websites.
One of the most important things is shining up your resume. If your resume didn’t stand out in any way, it is going to be buried under the mountain of other applicants.
Step 5: Work hard—and network harder
Having connections with other peoples in the same field is the best way to learn more about different career opportunities. That way you will also have tons of advantages like what kind of company you’d like to work for, or what projects appeal to you.
If you’re in good standing, you can also start networking internally with higher-ups of the organization to explore more opportunities.
Warping it up:
First, polish your mathematical skills, and get a knack for one or two programming languages, then take on side projects or internships, and finally start your job hunting. And always remember that networking is the key and stay patient.
Related reading- Simple Linear Regression