Whether you already work in the data science field or wish to get into it, there’s a lot of benefit in always expanding your bag of tricks. The field is grounded in statistics, and there’s also a rapidly growing trend toward automation. Being tech- and math-savvy is absolutely critical. Let’s take a look at 8 tips and tricks you’ll want to know as a data scientist.
#1: Learn to Program
With data science already heavily dependent on computing resources and machine learning quickly become the top way to derive insights, coding skills have never been more important. Fortunately, you don’t have to be a full-fledged application developer. Several programming languages are being increasingly tailored to serve those who need to build their own data analysis tools. Two of the biggest languages worth keeping up with are:
- Python
- R
If you’re looking to perform work using modern machine learning systems like TensorFlow, you’ll likely want to steer toward Python, as it has the largest set of supported libraries for ML. R, however, is very handy for quickly mocking up models and processing data. It’s also prudent to pick up some understanding of database queries.
#2: Develop a Rigid Workflow for Each Project
One of the biggest challenges in the world of data analytics is keeping your data as clean as possible. The best way to meet this challenge head on is to have a rigid workflow in place. Most folks in the field have set down these steps to follow:
- Gather and store data
- Verify integrity
- Clean the data and format it for processing
- Explore it briefly to get a sense of the dataset’s apparent strengths and weaknesses
- Run analysis
- Verify integrity again
- Confirm statistical relevance
- Build end products, such as visualizations and reports
#3: Find a Focus
The expanding nature of the data analytics world makes trying to know and explore it all as impossible as getting to the edge of the universe. It might be fun to explore machine vision to identify human faces, for example, but that skill likely isn’t going translate well if your life’s work is doing plagiarism detection.
In order to find a focus, you need to look at the real-world problems that interest you. This will then allow you to check out the data analysis tools that are commonly used to solve those problems.
#4: Always Think About Design
How you choose to analyze data will have a lot of bearing on how a project turns out. From a design standpoint, this means confronting questions like:
- What metrics will be used?
- Is this model appropriate for this job?
- Can the compute time be optimized more?
- Are the right formats being used for input and output?
#5: Make Data Scientist Friends with Github
Github is a wonderful source of code, and it can help you avoid needlessly reinventing the wheel. Register an account, and then learn the culture of Github and source code sharing. That means making a point of providing attribution in your work. Likewise, try to contribute to the community rather than just taking from it.
#6: Curate Data Well
One of the absolute keys to getting the most mileage out of data is to curate it competently. This means maintaining copies of original sources in order to allow others to track down issues later. You also need to provide and preserve unique identifiers for all your entries to permit tracking of data across database tables. This will ensure that you can distinguish duplicates from mere doppelgängers. When someone asks you to answer questions about oddities in the data or insights, you’ll be glad you left yourself a trail of breadcrumbs to follow.
#7: Know When to Cut Losses
Digging into a project can be fun, and there’s a lot to be said for grit and work ethic when confronting a problem. Spending forever fine-tuning a model that isn’t working, though, carries the risk of wasting a significant portion of the time you have available. Sometimes, the most you can learn from a particular approach is that it doesn’t work.
#8: Learn How to Delegate
Most great discoveries and innovations in the modern world are the final work products of teams. For example, STEM-related Nobel Prize are pretty much never awarded to individual winners anymore. While the media may enjoy telling the stories of single founders of companies, the reality is that all the successful startups of the internet age were team projects.
If you don’t have a team, find one. Recruit them in-house or go on the web and find people of similar interests. Don’t be afraid to use novel methods to find team members, too, such as holding contests or putting puzzles on websites.
