Canva This article will cover the five steps you need to take to become a data scientist, including how to get the right skills for this new career path.
1. Figure Out What You Like to Do
To become a data scientist, first, figure out what you like to do. Consider your hobbies, interests, and strengths if you are unsure where to start. For example, maybe you enjoy making art or playing sports. On the other hand, perhaps you have experience in business or finance and would like to use that knowledge in your career. Or maybe there isn’t any one thing that comes immediately to mind—in which case, it might be worth taking some time off work and trying different things until something catches fire for you (and don’t worry too much if nothing does). Canva It can also help if we consider our weaknesses and strengths: what are the areas we struggle with? Are there specific tasks that are challenging for us? Then, it could be worth looking into training courses that would improve these skills to become easier over time. For example, if writing reports causes anxiety because of spelling errors or other mistakes (a common problem among data scientists), consider signing up for a proofreading course at Udemy or Coursera, which will help improve those skills before starting full-time employment as a data scientist.
2. Get Some Basic Math and Statistics Knowledge
A grasp of basic statistics and probability is integral to becoming a data scientist. Data scientists are expected to have more than just an understanding of math—they’re also likely to know how to apply it. Statistics teach you how to interpret numerical information to make conclusions about the world around you. The probability component teaches how likely certain events will happen given certain circumstances or variables. Canva The first step in understanding these concepts is learning about the different types of variables, such as discrete, continuous, categorical, ordinal, etc. Once you have this background knowledge, start exploring math courses that teach probability theory (i.e., classes on Bayes Theorem). You’ll often see why these skills help make accurate predictions about real-world situations.
3. Understand the Type of Data Scientist You Want to Be
There are many types of Data Scientists, and it’s important to know what level of experience and education you need for each type. An essential step in becoming a Data Scientist is understanding precisely what the job entails and what skills are required. There are three main types of Data Scientists: Canva Analytics Engineer: This role uses statistics and machine learning algorithms to derive insights from data. They use their findings to support and improve business processes or products. Analytics engineers typically need an undergraduate degree in mathematics, computer science, engineering, or another technical field. Data Architect: The data architect ensures that all data is stored correctly in databases, is appropriately secured and accessible by authorized users only, and can be easily retrieved when needed. This role requires a bachelor’s degree in computer science or a related field. It also may require 3+ years of experience in data architecture design and implementation projects, such as a master’s degree in business administration (MBA) with an emphasis on information systems management or a similar advanced degree program focused on information technology (IT). Data Scientist: Data scientists use statistical techniques like predictive modeling and machine learning to analyze data and make predictions. They also design algorithms that software applications can use for business purposes, such as fraud detection or predicting customer behavior. Data scientists typically have a bachelor’s degree in computer science or mathematics with a minor in statistics or business administration. In addition, they may also require 3+ years of experience using statistical analysis tools like R and Python.
4. Pick a Programming Language and Learn It
A programming language allows you to write computer programs, which are sets of instructions that tell computers what to do. There are many different programming languages, but R and Python are the most popular ones for data science. Suppose you have no programming or coding experience and want to become a data scientist as soon as possible. In that case, I highly recommend learning Python first because it’s easier than R. Canva Once you’ve learned one programming language, it’ll be much easier to understand another one if necessary. For example, once you’ve learned the basics of Python, picking up R will be much less intimidating because they’re similar but not exactly alike (in other words: they share some syntax). On the other hand, if you have some coding experience or already know R, it’s up to you which one you want to learn first. I recommend starting with Python in both cases because more resources are available for beginners.
5. Build a Portfolio and Network With Others in the Industry
Building your portfolio can be one of the most important things you do as a data scientist. You need to show potential employers that you have more than just theoretical knowledge and can put it into practice by solving real problems. A portfolio to showcase your abilities is also essential when applying for jobs, as it allows potential employers to see what kind of work you can do. Canva An excellent way to start building your portfolio is by volunteering with an organization or company that serves an essential purpose (like non-profit organizations). This gives you hands-on experience working on projects involving data science skills while helping out an organization or business in need. Not only will this give you some great exposure and experience in the field, but it will also look great on paper.
Final Thoughts
It takes serious effort to become a data scientist. It’s one thing to go online and find a course or resource that says they can teach you the basics, but it’s another thing when you start putting everything into practice. The most important thing about this process is that you keep going even if things don’t seem perfect at first! It’s important to remember that you’re not alone in this. There are many online resources; you can always ask questions if something doesn’t make sense or you don’t understand something. Additionally, if you put in the effort and practice daily, you will soon be able to call yourself a data scientist!
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This content is accurate and true to the best of the author’s knowledge and is not meant to substitute for formal and individualized advice from a qualified professional. © 2022 Hassan
