This is the era of Industry 4.0, and technology is developing further at a rapid rate. So, in this scenario, the skills required to work with these technologies also need to evolve - otherwise, they will become obsolete. Data Science, in particular, is growing rapidly in this decade; and honestly, you can expect it to become more popular in the near future.
In fact, Analytics Training suggests that about 30 million TB data is generated every single day by over 6 billion devices that are connected to the internet! So, with the rise of big data, comes the need for highly skilled specialists who can interpret this data and extract useful information out of it. This is exactly where a data scientist comes in.
In this article, we will discuss the top 6 tips to start a career as a data scientist. So let's begin:
What is a Data Scientist?
Before we move onto tips, let us first understand what a data scientist is exactly. A data scientist is basically an analytical data expert who is responsible for the collection, analysis, and interpretation of big data.
Although this field was not on the radar a few decades ago, but this sudden popularity reflects how businesses are now adopting big data. A data scientist is a sort of magician who uses all this unstructured information to boost revenue by extracting useful business insights. This job is an offshoot of various traditional technical roles, which include maths, computers, science, and statistics.
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Top 6 Tips to Start Career as a Data Scientist
Now, if you are interested in solving new, challenging problems in routine, data science is definitely the field you can make a career in. There is a huge demand for data science market - as research suggests - which the United States leads, requiring over 190,000 data scientists in the year 2020! Here are some tips that you can follow:
1. Choosing a Role:
Choosing the right role is very important in this field. You can be a machine learning expert, a data engineer, a data visualizer, or even a data engineer, etc. Depending on your work experience and study background, you can choose the role that suits you. For instance, if you have studied, software engineering, getting into data engineering wouldn't be difficult for you. In order to make the best choice, it is suggested that you talk to people who are already in the field.
2. Take Up a Course:
Now since you have chosen a role, the next thing is to put dedicated efforts into learning the work you will be required to do in the certain role. The awesome thing about data science is, you can learn a great deal about it online. In fact, According to a recent study by IBM, the demand for data scientists is expected to increase by 28% this year!
You can go for Udacity, IBM, Coursera, EdX, etc. and take certifications or courses to polish your skills. All these online universities bring coursework, assignments, quizzes, case studies, and comprehensively prepared study flow. So taking up two or three courses can bring a lot of value to your work.
3. Build your Portfolio:
Once you have completed your courses, the next logical step is to build your data science portfolio. It is important to bring a well-thought-out portfolio when you are working in this super-competitive field. So now, since you don't hold relevant industry experience, you can share personal data science projects which demonstrate your skillsets. You can also share the course projects created while completing your online certifications. and lastly, you can create some volunteer projects and showcase them in your portfolio.
4. Network with Relevant People:
Now that you are ready to jump in the race, it is essential to network with industry peers and even get support/ advice from them. In order to do so, the best option is to attend events and meetups in the field. It is important because not only will your peers keep you motivated but also help you overcome hurdles. We do have some meetups in Dublin, under the name of Dublin Data Science. So you can search for the ones in your area, and jump right in.
5. Master Soft Skills:
Now some of the skills that are required to establish a career in data science, are obvious like you need to be expert at coding in a certain language or have a sound understanding of how the technology works. But there are some lesser-known skills that you need to master in order to stand out of the crowd. Soft Skills such as creative thinking, time management, and innovation are important for working in this sector.
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You see, data scientists have to approach daily routine problems by fusing their creative thinking with logical concepts to build best-fit solutions. In fact, a recent research by RS News suggests that now employers use scales from 0-10 for each skill when you go for a job interview. Therefore, mastering soft skills like this are also very important to make you stand out.
6. Follow the Field Experts:
No matter at which point of learning/ expertise you are, it is important to engulf the right sources of knowledge. One of the most useful sources can be the informative blogs being run by data scientists. Interestingly, experts in this field are quite active online and therefore, they continue to update their followers with findings, and advancements in Data Science frequently. Some of the best data scientists to follow include:
So Are You Ready To Start Your Data Science Journey?
In this era, the demand for data science is huge and employers are actually investing both time and money for data scientists. Therefore, it is important for you to take the right steps in order to enjoy exponential growth. Before you step in, have a look at the tips we have shared above, and follow them in order to start off without making any costly mistakes.
If you would like to learn more about data science or data in general, please feel free to read some of the posts below!
What Is Dask, And How Can It Help Data Scientists?
Data Engineering 101: How To Develop Your First Data Pipeline
How Do Machine Learning Algorithms Learn Bias?
Personalization With Contextual Bandits
How To Survive Corporate Politics As A Data Scientist
What Is A Decision Tree
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