Follow us on:    Twitter  |  Mastodon  |  Facebook  |  WhatsApp  |  Telegram  |  Google News

There is an increasing demand for data scientists as everyone realizes the importance of data and tries to find ways to take advantage of all the data they collect. Data science can seem intimidating to a lot of people, especially those who do not have any background in any data-related field.

 

While you may have a lot of questions, this article aims to take you through what is required to become a data scientist and hopefully answer all your questions in the process.

 

Starting a Career in Data Science: 7 Steps to Follow

 

Research Different Roles and Choose One

Before you can choose a career path in data science, you need to do proper research. This is because there are so many roles with each of these roles being different from the rest. Some of the more common roles include data engineering, machine learning, data scientist, data visualization expert, and many more.

 

Depending on previous experience and educational background, some of these roles may be easier for you to get into than others. If you are not sure whether a certain role would be a great fit, you can talk to people who already work in these fields or take mentorship from people in the fields you are interested in.

 

Choose the Right Program

Once you have decided on the career path you would like to follow as well as the roles that would be a great fit for you, you should find programs that cater to the specific area you would like to focus on. Although it is possible to earn certifications that prepare you for your preferred roles, earning a master’s degree not only arms you with the skills you need to succeed in a data-driven world but also improves your chances of landing high-demand roles. If you have chosen to follow the data scientist path, you can enroll in an online computer science master's program with a focus on data science. Alternatively, you can enroll for an online computer science master’s degree with a focus on machine learning or another area of data science that appeals to you.

 

 

For the best outcome, make sure that you engage with the program you choose actively. By going through the lectures, and completing your assignments and coursework, you will be better prepared to enter this field.

 

Gain Technical Skills

Once you have completed the course of your choice, it is time to work on your technical skills. Strictly speaking, your technical skills will be the tools and languages you use. A good rule to follow is to choose mainstream tools and languages that are used by lots of data scientists.

 

Python remains one of the most popular programming languages for data scientists. The main reasons for this include that it is easy to learn and, with its huge community, there are innumerable learning resources available to those looking to learn Python.

 

Because you might be required to use a different programming language at your job, you should learn the underlying programming concepts instead of memorizing syntax. These concepts will help with the application of your programming knowledge and help you come up with viable solutions to problems.

 

 

If you want to get started right away and do not have to learn a programming language, you should consider using GUI tools as you learn.

 

Work on Your Communication Skills

There is a misconception that data scientists need to be technically talented and that is enough to land them a job. This is actually wrong because communication skills are critical for data scientists. Great communication skills help you demonstrate your proficiencies during the interview process and can improve your chances. Using tools such as grammarly can even make you feel confident in terms of your communication skills. You might be wondering is grammarly premium worth it?. Check out Grammarly review to clear your confusion.

 

Communication skills are also critical once you land a job. A data scientist’s job involves a lot of explaining and presentation and you need good communication skills to break down data and explain it in a way that makes it easy for everyone to understand. Sharing your ideas effectively with colleagues will also depend on how well you can communicate and put your ideas across.

 

Network Actively

Although you should be focused on learning and gaining the skills that will make you a great data scientist, there is only so much you can learn before you have to go out in the world and network. Networking is important for several reasons. First, it exposes you to the world of data science, helping you meet peers who you can lean on for mentorship and advice. Second, networking is a great way to meet potential clients or employers. Third, networking can be an opportunity to learn about what is happening in the industry, where it is headed, and areas you need to focus on to stay on an upward trajectory.

 

 

Be a Team Player

Data scientists rarely work alone as they are usually members of a team. It is critical, therefore, that data scientists learn to work with others. Here, your communication skills will come to the fore and will enable you to explain yourself and work with others productively. Also, interpersonal skills will be important when working in a team, but this is something you can work on as you get to know your teammates and anyone else you will be working with.

 

Cultivate a Learning Habit

Data science and related fields are always evolving and this is why data scientists never stop learning. To ensure that you never fall behind, try to cultivate a learning habit. You should always be on the search for new learning resources. A great place to start is blogs that focus on data science, programming, statistics, and related topics. Blogs that are updated regularly are invaluable because they help you keep up with the latest advancements in this field.

 

The future is very bright for data scientists as we move to a world that is being increasingly governed and shaped by data. All industries need data scientists who can help them interpret and make use of big data. The good news is that starting a career as a data scientist does not require much, especially if you already have a bachelor's degree or a computer science background.