Organizations globally are increasingly relying on data to make their business processes more efficient, reach their customers more effectively, and make better decisions. Being a data scientist can be an extremely rewarding career, where you help these organizations gain insights from their data and make the decisions that really matter.
But people often want to know how they can become data scientists. Fortunately, we’re here to help, and in this post, we’ll look at the process of becoming a data scientist in more detail.
Qualifications
The first question people often have when they want to pursue a career in data science is: What qualifications do I need? In most cases, they want to know if they need a degree to be a data scientist.
Traditionally, data scientists have been highly qualified, with the majority having at least a Master’s degree. So, the traditional data science career started with a Bacherlor’s degree, typically in either mathematics, statistics, or computer science.
However, having a degree is not a strict requirement. This is even more so when you consider that the demand for data scientists currently outstrips supply. But keep in mind that you’ll still need the relevant data science, technical, and soft skills to be considered for a position.
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Develop the right skills
You’ve now seen that you don’t need a degree to be considered for a position as a data scientist, but you need to develop the relevant skills. The immediate question is: What skills do you need to develop?
Here, apart from skills in mathematics and statistics, you’ll also need to build skills in:
- Data. Data science’s foundation lies in data, so you’ll need to know how to collect, store, and work with data. Nowadays, it’s also important to have skills in working with big data and its related technologies.
- Programming. As a data scientist, you’ll use programming principles and languages to collect, sort, analyze, and manage data. As such, you’ll need to build these skills and learn the relevant programming languages. Some of these languages include Python, R, SQL, and others and some popular tools include TensorFlow, Jupyter Notebook, and more.
- Data visualization. Creating charts, graphs, and other data visualizations is an integral part of being a data scientist. As a result, you should build skills in using the tools to create these visualizations. Apart from programming languages, these tools also include Tableau, Excel, and Power BI.
- Machine learning. Machine learning is widely used in data science to improve the quality of data and the insights that can be gained from it. So, you should build some skills in machine and deep learning.
- Communication. Communication is often overlooked as a fundamental skill for data scientists. Yet, it’s crucial in that it allows you to effectively communicate your findings and share your ideas.
In a sense, you can see these skills as the fundamentals of data science. Now, to learn and improve these skills, a data science course is an excellent option. It teaches you fundamental data science concepts, the relevant programming languages, how to collect, analyze, and model data, and how to create data visualizations.
Once you’ve completed a course, you’ll be able to analyze, visualize and gain insights from data, make predictions based on it, and present your findings in ways that are easy and intuitive to understand.
Work on Data Science Projects
Once you’ve learned the fundamental skills and how to work with a range of programming languages and tools, you’ll need to build these skills even further. And this is where working on data science projects is invaluable. It helps you put the skills you’ve learned into practice.
There is a massive variety of projects you can work on based on your interests. Ideally, however, you should work on projects that incorporate a wide range of skills, including data analysis, predictive analytics, visualizations, and more. The projects you work on should also use different data types and use different tools. This will help you build a more complete set of data science skills.
As you progress, you can then take on more complex projects that will allow you to use more sophisticated techniques, and you can create your own algorithms to analyze and model data
Create a stellar portfolio
Apart from working on data science projects, you also need to demonstrate the skills you’ve acquired to potential employers. This is where your portfolio comes in. In fact, your portfolio could be one of the most critical components in landing your dream data science job.
When developing your portfolio, there are some things you should consider:
- Website. You can display your projects on GitHub or your personal website. Ideally, you should do so on both to make yourself stand out in the market.
- Projects. As mentioned earlier, you should work on projects that incorporate a wide range of skills. With your portfolio, the position is no different, and you should showcase projects that demonstrate your skills across the entire data science process.
- Narrative. It’s also important that you accompany your work with an engaging narrative that shows what you’re working on, what you want to achieve, and the results. This not only makes your projects easier to understand but also demonstrates your communication skills.
- Key projects. During the course of learning, you’ll likely complete several projects. So, you might want to highlight a few key projects that highlight the key skills relevant to a specific position.
Apply to relevant data science jobs
Once you’ve perfected your skills and crafted your portfolio, you can apply for relevant data science jobs. There are several positions you can fill as a data scientist including Data Engineer, Data Analyst, Business Analyst, Data Architect, and many others.
To find your job, you should look at the skills prospective employers prioritize, what projects they’re working on, and how these compare to your skills and expertise. Keep in mind, though, that entry-level positions might be your best option to gain entry into the industry. From these, you’ll build your skills and experience even further and move on to bigger and better things.
In closing
Hopefully, this post helped illustrate how you can become a data scientist. If you’re ready to take the first steps towards your new career, get in touch with Pierian Training. We’re trusted by over 2.8 million students and offer instructor-led training, taught by technical experts, in data science and cloud computing.
Our Python Data Scientist career path can take you from zero prior knowledge to an advanced understanding of data science and machine learning to enable you to build the data scientist career of your dreams.
