How to Become a Data Scientist in 10 Steps
Data sciences and data analytics are fields that are booming in an uncertain job climate. The demand for people skilled in these areas is perpetually on the rise and that is why it can be a great option if you’re looking for a career change or to establish yourself professional. Not to mention, the pay is competitive and the high demand leaves employees in a good position to negotiate favourable working conditions and reliable benefits.
If this sounds appealing to you, there steps you can follow to build a new career as a data scientist. Here is how to become a data scientist in 10 simple steps:
1. Figure out what kind of data scientist you want to be
Before you learn how to become a data scientist, you should first determine whether you are the right fit for the job. It is important to understand that the definition of data scientist varies from company to company, so in order to be able to start working towards your goal, you need to know which industry you want to work in.
Generally speaking, a data scientist combines the skills of a software engineer and a statistician, but what will really allow you to stand out, is being able to offer niche knowledge of a specific industry.
2. Brush up on your math skills
As noted above, regardless of the industry you choose, you’re also going to require a solid foundation in math and statistics. In order to discern anything valuable from the data you’re analyzing, you’ll need basic linear algebra and a good understanding of algorithms. Be warned that more advanced mathematics might be required in some circumstances, but the basics are a good place to start.
3. Get familiar with machine learning
This is one of the most common buzz words being thrown around the world of data sciences, so you want to make sure you understand the concept and are comfortable using it. Linked to big data (another term you’re going to want to be familiar with), machine learning uses algorithms to turn data into value and support learning without being explicitly programmed.
4. Teach yourself how to code
Speaking of programming, in order to be a successful data scientist, you’re going to need to learn how to code. Having the ability to manipulate code will give you the ability to tell a computer how to analyze data. When starting out, you’d be well-advised to start with an open course language like Python and build your knowledge base from there.
5. Develop an understanding of data visualisation and reporting
Although your skills don’t need to as advanced as someone seeking employment in the realm of graphic design, you do need to be able to transform the data you collect into something the average person can understand. The ability to create comprehensive visualizations will go a long way to ensuring you’re valued as a data scientist.
6. Know the value of clean data
Raw data can only get you so far, so it is crucial that you learn basic data munging and data cleaning techniques in order to turn transform what you collect into something is easier to analyse and create meaning from. Standard data cleaning will eliminate issues like duplication and will ultimately save you a lot of time.
7. Become familiar with the various ways data is stored
In order to prove yourself as a data scientist, you’ll need to understand the similarities and differences between databases, data lakes, and distributed storage. These are all important data repositories and the framework they utilize largely dictates how they can be used. You always need to think and understand big data when building data storage, so it is important that you understand these differences from the get-go.
8. Join the community
One the most effective ways to quickly expand your knowledge of data sciences, is to become part of the community and talk to other people who share the same interests. You can start by joining social media groups dedicated to these topics and following leaders in the field. Especially when starting out, it is important you prove you that you have a keen interest for people to see you as relevant.
9. Practice, practice, practice,
It is turn in data sciences as with all things- practice makes perfect. There are lots of great resources that can help you build small projects to test the different techniques and theories that you’ve learned in order to fine tune your skills and ensure you’ll be ready to take advantage of any opportunities that present themselves.
10. Never stop learning
As you continue along your data scientist career path, keep in mind that you’re going to have to continually be brushing up your skills in order to stay relevant. Constantly be on the lookout for new tools that you can add to your toolbox and be curious about developments and innovation in your field. Never assume that you know it all and be humble if someone is willing to share their knowledge or expertise with you.