Machine learning has helped to empower huge strides in data science and vice versa. By applying ML, data scientists can analyze large amounts of data much more efficiently. Simultaneously, big data has helped to power many new applications of ML to achieve various practical results. If you are interested in data science and machine learning, the following tips will help you.
1. Start With the Basics
First and foremost, you need to invest time in learning the core software engineering and data science concepts behind machine learning. Due to the relatively short time that modern machine learning has been in place, a lot of people just want to jump straight in. However, you should never try to apply technology tools if you don’t really understand them (at least if you want to base your career on them). Instead, take the time you need to really deeply understand the concepts behind it all.
2. Don’t Skimp on Coding Knowledge
Machine learning and data engineering are not so much about technology and coding as they are about mathematics and modeling. However, that doesn’t mean that you should skimp on learning about coding. There is still a lot of programming, especially scripting, that goes into operating data science and ML solutions.
If you don’t invest some time in understanding the hard skills that help keep ML running, you will have a hard time applying the high-level concepts. You don’t need to be an industry-leading software engineer to be a data engineer, but you should know your way around code.
3. Make Sure To Stay Super Organized
Organization is essential in the data engineering space. While machine learning is intended to minimize a lot of the heavy lifting involved in data analysis, if you put data, unclean data into a system you will get bad results.
Data wrangler can help you use automation to ensure that your data is organized and clean. There are some great tools that should be implemented as part of your data processing pipeline.
4. Avoid All the Technology Hype
Machine learning and data science are rapidly growing fields. They are receiving a lot of attention because they are unlocking some new, futuristic possibilities such as self-driving vehicles. However, this can bring a lot of hype to the field. Sometimes technology hype can be a serious detriment to success.
The key issue that follows hype is a tendency to want to use the latest and greatest tools, regardless of whether they fit the current need. This leads to a lot of trying to fit a square peg into a round hole.
5. Make Sure To Practice Data Security
When you are collecting and analyzing a lot of data, you have a significant responsibility. If that data set gets compromised, your business could be liable. Furthermore, if your data gets compromised, stolen or corrupted, you may find your projects suddenly in a bad situation.
Therefore, it is important to practice good data security. Policies such as zero trust security can help to ensure that your business and your work are protected.
6. Know Your Data and Its Sources
Finally, make sure you know your data set and its sources. While big data cannot be managed manually by a person, you should at least have a strong understanding of what you are working with, even if you don’t look at every entry. There is a tendency in machine learning to want to let the automated pipeline do all the work. However, this can lead to a lot of errors.
It is always a good idea to understand what you are working with. This will help you to make better-informed decisions and to perform better data science.
Discover more about being a data engineer and using machine learning. Modern data science is an exciting field and being an engineer can be a great way to apply a computer science background in the industry.