Data science is all about making sense of the data you collect. Data scientists sift through reams of numbers to figure out how to make the world a better place.
The field of data science is still in its infancy, but it has fast-grown into one of the hottest fields in tech.
We’ve put together a step-by-step guide to help you get started on your data science journey. We’ll teach you the skills you need to apply to any project you’re working on, including data science fundamentals and the different types of data scientists. And, we’ll teach you how to get the most out of your data science endeavors.
What Steps Do I Need To Take To Be A Data Scientist?
There is more to becoming a data scientist than just going to university and getting a degree. You need to acquire a range of skills and experience in order to qualify as a data scientist.
This evidently means that the more skills and experience that you build up, the better your CV will look which will make you stand out from the crowd when applying for jobs.
To break it down, there are five main steps that need to be taken to become a qualified data scientist.
- Reinforce your foundations
It is pretty clear that whichever branch of data science you want to get into, you’ll need some sort of background in the subject area. A great starting point is having a degree or qualification in the subject area that interests you. For example, if becoming a financial data scientist takes your fancy, then a solid background in mathematics (i.e., an undergraduate degree) would be extremely beneficial.
Generally speaking, you will need an undergrad degree to become a data scientist. Employers are looking for someone who is qualified, has great planning and organization skills, and is able to deliver under pressure, and having a degree shows just that.
There is no specific degree that you need to have, as a wide range of subject areas give you the freedom to venture into the data science path. The list of degrees below are all considered acceptable foundations when building up your data scientist career:
- Physics
- Brain and cognitive science
- Biology
- Computer science
- Engineering
- Statistics
- Learn the basics
There are a few essential technical skills that you need to have experience in to become a data scientist. You will need to become proficient in using programming tools which are considered the ‘basics’ in the data science world.
Hadoop, SQL, and Python are a few of the database interrogation and analysis tools that you need to be familiar with.
SQL
SQL is a domain specific language used in data handling for the extraction, addition, and deletion of data. Due to the fact that it isn’t as complex as other programming tools, knowing how to use it proficiently is considered a must-have skill by most employers. Learning SQL will help you to better understand relational databases and boost your profile as a data scientist.
Hadoop
Hadoop is an open-source software framework that provides massive database storage and holds an enormous processing power which gives you endless opportunities when working on tasks. Having experience with this programming tool is not essential, but heavily preferred in most cases. This is because when working as a data scientist, you will have to deal with so much data that it may exceed the memory capacity of your system or server. That’s why Hadoop is so useful for data storage, as well as data exploration, data filtration, and data sampling.
Python
Python is one of the most common coding tools available. It is a great programming option for data scientists, especially beginners, as the syntax is very similar to the English language and therefore easy to read and understand. Python is an extremely versatile tool that can be used for almost all processes and responsibilities you will hold as a data scientist. Compatible for use on different platforms including Windows, Mac, and Linux, it runs on an interpreter system which means that code can be quickly executed as soon as it is written.
- Study machine learning
By following this step and gaining experience in machine learning and algorithms, you are sure to stand out from other data scientist applicants.
Machine learning is essentially using patterns hidden inside of data to build predictive models for the future. A data scientist works with large numbers of data sets, and so having the skills and experience to solve different data science problems that are based on predictions of major organizational outcomes is very beneficial.
To build up your machine learning skills, it is best to start small with simple linear and regression models. Once you feel that you have grasped those concepts well, you can gradually increase the complexity and move onto K-means clustering and Classification and regression trees (CART) models.
- Gain Experience
As with any job or career type, having experience is a great way to show potential employers that you have the skills and knowledge they are looking for.
With data science continuously growing in popularity, gaining work experience is not as hard as you may think.
Big companies, especially those in the retail, finance, and travel industries, offer internships which provide you with invaluable experience. Bear in mind that these programmes can be extremely competitive, and places are limited, so anchoring down your CV with basic skills and having an ‘above and beyond’ working attitude can help your application go further.
Medium and small companies may not have as many internship programmes but may offer shadowing opportunities. These can be a great way to watch an experienced data scientist at work, so you can soak up all the techniques and skills they use in their daily job.
Also, large firms such as Kaggle and Topcoder frequently host competitions which aim to spot new and emerging talent. By entering and competing in these events you can show potential employers your eagerness and flexibility towards becoming a data scientist.
If you are only just starting out in your career as a data scientist, you can always rely on conferences and seminars as a starting point. These events can give you a greater insight on what becoming a data scientist means, as well offering you with great networking opportunities with potential employers.
- Keep up to date
As stated before, data science is an exciting and every-changing career field with so many new opportunities becoming available every day. It’s really important to be confident in your skills and abilities so you know you are making a difference in your data science career.
Having said so, even the most skilled data scientists sometimes need to top-up their knowledge. There are various online courses and bootcamps available that you can attend which keep you up to date with the latest algorithms and techniques.
Also, a postgraduate qualification such as a Masters or a PHD is very common among data scientists. By gaining extra certifications, you are giving yourself an edge over other candidates which makes you more likely to be chosen for promotions in the future.
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