How To Start A Career In Data ScienceJune 10, 2021 No Comments
Featured article by Angel Evangelista
Have you been looking for an article or blog post that can provide you with sound advice on pursuing a data science-related career? Then, you’ve found the right page. As data science is one of the fastest-growing and most in-demand fields today, this is an excellent time to assess whether a job in the industry will fit you. One good thing to know is that data scientists don’t need to have any prior experience. The skill set required for data science can be acquired in various ways by yourself and with the help of multiple resources.
Identifying the position you wish to pursue should be the first step. As you may know, data science is a diverse industry that involves a wide range of job roles. You probably have questions like: what career opportunities are available to you when you learn data science? What’s the best path for you? Establishing a career in data science should start with answering such questions.
Although you might think you’ve already decided on your career choice and you’re ready to use data analyst resume keywords in your applications, it’s worth taking the time to dig deeper and consider all of the options available to you. So read on to gather more information on how to start your career in the data science industry:
1. Determine The Position You Want
As previously mentioned, there are many roles to be found in the realm of data science. Therefore, there are several career paths you can look into, including these:
- Data Scientist
- Machine Learning Engineer
- Machine Learning Scientist
- Data Engineer
- Applications Architect
- Data Statistician
- Data Analyst
If you already have a specific background or some work experience, you’ll have an easier time getting into roles than others. For instance, software developers are capable of transitioning into data engineering without much difficulty. Also, it could be confusing to choose the path to take and the skills to develop unless you’re clear about what you want to become. If it’s challenging for you to differentiate some jobs from positions you think are similar, apply these tips:
- Identify what each of the roles entails by speaking to professionals in the field.
- You can ask questions and advice from people you consider advisors or mentors.
- Choose the role that’s right for you based on what you want and what you can do well.
- Research your way around the job roles you find appealing.
Make sure you choose a role carefully before jumping into it. You must be clear about the requirements of the field and prepare for them accordingly. Companies get value from data science production, so you should be able to apply for a job related to it with ease.
2. Earn A Degree And Polish Your Skills
Once you’ve chosen a role, the next step is to devote yourself to understanding it. In other words, you have to look beyond the responsibilities that the job comes with. There are thousands of studies and courses out there that will help you become a data scientist, so you can choose whatever you want to learn about the field. Many course materials and educational programs can give you an advantage in landing a job in the data science industry. But while there’s no shortage of materials to study, learning may prove difficult if you don’t put in the necessary effort.
Massive Open Online Courses, otherwise known as MOOCs, are widely available these days. Some are free, while others charge a minimal fee. These will allow you to equip yourself with the skills needed for the data science industry.
3. Get Familiar With Some Programming Languages
If you compare data science with other fields, you’ll find that this industry focuses more on what you know and your ability to prove your relevant skills rather than your educational background. It won’t matter which prestigious school you attended as long as you have adequate skills for the job. When people from diverse backgrounds are assessed based on their skills, the playing field is leveled.
Given that you already have the right math background for data science, you should also hone your knowledge on some of the vital programming languages for data scientists. These four are among the most common programming languages used in the industry:
- Python: As a tool most often used by data scientists, Python offers various applications for web development, software development, machine learning, and deep learning.
- R: Data visualization is possible with this data science tool, and a large community of users supports the platform.
- SQL: Through this, you can query a variety of tables and databases at the same time.
- SAS: A statistical analysis, business intelligence, and predictive analytics tool, SAS is an expensive platform commonly used by large companies, and it’s not a standard tool used by small and medium-sized enterprises (SMEs).
There you have it—the essential steps and components you have to focus on in order to land a job in the data science industry. Employers invest a great deal of time and money into training data scientists due to the massive demand for data science. By following the tips above, you’ll be able to impress prospective employers and build a thriving career in data science in no time.
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