Data science is the use of statistical techniques to extract meaningful knowledge from large volumes of data. The majority of what a data scientist does is typically related to analysing or processing data in order to make inferences, predictions, or simply summarise it for presentation purposes.
Data engineers primarily tackle the bi-directional flow of both raw and processed datasets. They are in charge of the infrastructure that enables rapid access to stored information: designing databases, setting up clusters, optimising queries and providing tools for measuring performance. Data engineers ensure that the data is accessible to both developers and data scientists.
Data analysts also look at large datasets and attempt to draw conclusions. However, their primary focus is on looking at trends in the data and making predictions as to what will happen using historical information. They generally have less access to high-powered processing tools, but are more likely to use more descriptive statistical techniques.
All three job roles work in tandem with one another: a strong understanding of each field is invaluable when working with large amounts of data. It’s also worth noting that there are many opportunities outside the traditional boundaries of these roles; some of these include journalism, fraud detection, marketing and sales.
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Benefits
It’s important to understand that data scientists are in high demand and this means many companies and industries are willing to invest the time and energy into properly training them. This means you will likely have the opportunity for a higher salary when compared to other roles within the same industry. It’s also worth noting that there are many opportunities outside the traditional boundaries of these roles; some of these include journalism, fraud detection, marketing and sales. Taking a good online data science course will help.
What skills do you need?
Just about any skill set can be applicable for data science. This includes programming languages such as Python, R or even JavaScript, but also statistical software such as SPSS or SAS (which is often used by business analysts). However, a large number of data scientists come from non-traditional backgrounds: mathematics, physics, economics and even political science. The important thing is to know what you’re capable of learning. Data science has an incredibly steep learning curve, especially if you want to work on real-world problems that haven’t yet been solved by other people (and published in peer-reviewed papers).
How can you help me get my data science career off the ground?
There are a few ways you can start gaining exposure to data science. Most importantly, it’s not just about learning how to work with data; that’s only the tip of the iceberg.The greatest way to get your hands dirty is to participate in open source projects or even to create your own personal projects utilising available public datasets. Your prospects of landing a job in the sector are greater the more experience you have dealing with data.
Data scientists are in high demand and this means many companies and industries are willing to invest the time and energy into properly training them. This means you will likely have the opportunity for a higher salary when compared to other roles within the same industry. Thus, it is important to take the best data science courses online to boost your career goal in the right direction.
Features of a good data science course
1. Be practical:
The best way to become proficient in data science is through practice. The best way to practice is by doing a project and learning from the mistakes you make along the way. Any course should ideally give you the chance to do this by letting you work on a variety of engaging projects that will help you develop experience.
2. Allow variation:
Although programming skills are becoming more and more important, they’re only one aspect of what it means to be proficient in data science. Maths can also be very important if carrying out statistical analysis is part of your job description; so can statistics if you want to be a data analyst or scientist. It’s important that any course you take has a wide selection of resources, including videos, case studies and open source projects.
3. Be interactive:
It’s very easy to lose interest in the middle of a course if you aren’t able to relate to it or if all the information is simply being relayed to you without any opportunity for interaction. Interactive courses are more likely to keep your attention, so try looking for one that allows you to participate as much as possible. You should also be able to ask questions during lectures and be given answers quickly and honestly.
Conclusion
People working in the data sciences are currently enjoying an exciting period of rapid change. Armed with the right skills, knowledge and experience, it’s likely you can land an exciting new career that will allow you to work across many different industries and specialisations. If you have what it takes, then now is a great time to go after your dream data science job.