You can collect data on any topic in the world, from cats on the internet to politics to labor statistics. There are endless topics in which you can collect plenty of data points. Therefore, before you even begin studying to become a data scientist, you should narrow down your field of interest so that you find something that you’re passionate about.
You can even set yourself a goal to start a data collection project that you can carry out throughout your training — that way you’ll have data that you’re excited to analyze and questions that you can start asking
When you have some questions ready and a topic you’re ready to dive into, you’re ready to begin learning how to answer them. The first step here would be to learn a few programming languages, like Python, so that you can use computer programs to gather and analyze large sets of data. When it comes to choosing a programming language, you should focus less on choosing which language to learn and more on the process of analyzing data and building a project since this translates into all languages.
There are different paths you can take as a data scientist, so it’s a good idea to try and get a better idea of what area you’d like to specialize in early on.
Even if you’re an undergraduate getting your bachelor’s degree, you can still try and focus on certain concentrations, such as market research analysis, data visualization, management analysis, or more.
From this point, you can concentrate your studies to fit your goals, or you might even decide to continue on to a master’s degree to get even more training in your specialty.
Most companies will require that you show some sort of certification of your skills. Aside from a bachelor’s degree in a relevant field like information technology, computer science, or mathematics and perhaps a master’s degree depending on your specialty, you should also complete a certification.
There are many different types of data scientist certifications, so it’s important to choose one that’s most relevant to your concentration.
Never stop exploring the data. Once you’ve answered your main question, try and find more. Find new patterns and different directions to go in. A good data scientist tries their best to look at every possible angle on a set of data in order to gather a variety of different conclusions. By exploring different angles, you might run into issues you didn’t notice earlier and this will teach you how to become a much better problem solver.
It may seem like data scientists are solitary workers, but that’s not necessarily true. Since the quantities of data are huge, it’s so important to keep an open line of communication with your peers in order to get feedback and entertain different points of view.
You should be constantly sharing your work with others and even online in order to generate interest. This can help you find collaborators, fix issues you may be having, improve your skills, and may even lead you to find a job
The thing about data is that it never stops flowing. After you’ve completed one project, there’s always more to learn. Don’t be afraid to dip your toes into completely different projects or pose questions that may seem difficult to answer.
The more you test your own boundaries, the more you stand to learn, and companies are looking for data scientists who aren’t afraid to push the envelope.
