How to become a data scientist featured image

How to become a data scientist

When you enter into a career of a data scientist, you will be tasked with analyzing large sets of data both structured and unstructured. Data science combines computer science, mathematics, and statistics. If this sounds like a fun career for you, then you should pay attention to the helpful information below. Today, we are going to learn the steps it takes to become a professonal data scientist.

  1. Start With An Undergraduate Degree In Data Science: The first step in your journey to become a data scientist is to begin with an undergraduate degree in data science or a closely related field. Earning this degree will give you the foundation you need to build a successful career.
  2. Learn The Right Skills: While you are pursuing your undergraduate degree, you need to do some independent learning. Some of these skills will include research, cloud tools, risk analysis, python for data science, data mining and cleaning, effective communications, machine learning, and data warehousing just to name a few. Keep in mind, the skills you need to learn are always changing so you will never stop learning as a data scientist.
  3. Think About Specializing In Something: To earn the most from your career as a data scientist, you will need to specialize in something. For example, a entry data scientist that works in the tech field can earn around $85,000 a year. On the other hand, a senior data scientist working in the consulting industry can make as much as $158,000 or more per year. Once you are a senior data scientist, you will have the opportunity to go job about anywhere you choose. In fact, companies will be knocking on your door offering you a job with them.
  4. Land An Entry Level Job In Data Science: Once you have completed your undergraduate degree and you have specialized in a certain industry, it is time to look for a job. However, before you go out and look for a job, you will need to prepare yourself. Start by creating an online portfolio of projects that you have worked on while in school. This will give your potential employer an idea of what you are capable. Next, you will need to prepare yourself for your first interview. Remember to be confident and have all the right answers. Keep in mind that you will probably be asked a lot of technical questions related to your field. Last but not least, try to find a company that has room for you to grow. This will provide you with an opportunity to earn more money as the years go by. When you have the ability to move up in a company, it will eliminate the need for you to find gainful employment with another company. This will eliminate the stress caused by having to jump from one company to the next.
  5. Gain Post-Graduate Certifications: While you are working as an entry level data scientist, you can use your share time to gain post-graduate certifications. These certifications will allow you to learn more skills that you will need to grow in your field. Remember, the more certifications you have, the more money you can make! There are quite a few post-graduate certifications you can earn. While these will take some time to complete, they will be well worth it when you do!
  6. Earn Your Masters Degree: After a few years of working as a entry level data scientist, you will need to crack open the books and head back to school. Having a masters degree is more important than you think when it comes to data science. Sure you may be a master of python for data science but a masters degree can also help you climb your way to the top.

Now that you know what it takes to become a data scientist, why not begin your career path today? While it is a very challenging path, it is also highly rewarding. As a data scientist, you will have an endless potential to earn more money as long as you are willing to learn. If you feel this is the right career for you, make a step in the right direction by signing up for class today.

Starweaver Membership : How to become a data scientist

Business analysis : How to become a data scientist

Foundation of finance

microsoft azure