6 Simple Ways to Improve Your Data Science Workflow

Author | Feh Gonne

What matters most is that you get your job done as quickly and efficiently as possible, whether you have 10 years of related experience or 10 days. If you’re struggling with finding the right tools for your workflow, it might be time to change.

We’ve identified six simple ways to improve your data science workflow and add efficiency to your job. They’re not strictly related to analytics or coding skills (though you’ll likely improve with those!). Instead, these are just some general tips that we hope everyone in data science can benefit from.

1. Set the Right Objective

When starting a data project, it’s critical to set the right objective (or objective function). Don’t take these first steps lightly. You’re setting up a blueprint that will guide all future decisions.

· Categorize objectives as either exploratory or explanatory.

· The proper objective is one that everything you do is driven towards.

· A good objective is something that drives your project forward.

It’s not enough to say that your goal is to “understand the data” and scatterplot every possible feature combination. That’s just a recipe for producing lots of meaningless plots and graphs.

Instead, it would help if you started with a specific question that you want to answer and then worked backward to figure out what kinds of visualizations would help answer that question.

2.Get On the Same Page

Before diving into any analysis, spend some time checking in with your team and asking the following questions:

· What is most important to the business? What are we trying to achieve?

· What assumptions are we making about our users and their behavior? Are these accurate?

· Make sure everyone in the team uses the same tools and has access to the same data.

There are so many ways in which working collaboratively can go wrong, but it’s critical to get it right if you want your team to produce high-quality work. This means setting aside time to talk about how exactly you’re going to work together, who will take responsibility for each part of the analysis, and how you’ll store your code and datasets so that everyone can have easy access to them.

A good data science workflow includes:

i.Data Acquisition

How do we collect or acquire data? This includes deciding which data to collect and how to collect it to be used for analysis.

ii.Data Preparation

How do we clean and transform the raw data into a form that can be used for analysis? This includes removing duplicates, correcting errors, handling missing values, creating new variables, etc.

iii.Modeling

How do we analyze the data to make predictions or draw conclusions? This includes developing a statistical or machine learning model that can be used to make predictions or draw conclusions from the data.

3. Allow Room for Discovery

It’s hard to know what you don’t know and, therefore, impossible to plan for. Even if you’ve done similar work before, every project is different, so you should always allow room for uncertainty. The data science workflow is an iterative process that is not crafted in stone, and as you learn more about the data and the problem, your approach will change. It’s essential to leave flexibility in the schedule to accommodate this discovery process.

4.Talk to Your Consumer

Talking directly to your end-user can make a big difference in how you approach their problem. Together, you can come up with a better understanding of their goals, needs, and constraints that will inform your analysis.

You also have the opportunity to get them excited about what you are doing and make sure they are ready to take action when you deliver your results.

5.Clean up your Python Environment

As you work on data science projects, you will use more and more tools. With that comes more packages installed. But with great power comes great responsibility, and as a developer, you need to be able to manage your Python environment.

This is not only important for your sanity but also for reproducibility. If you want other people to be able to run your code, it’s best if you make sure that the environment is as close to identical as possible!

6.Optimal Solutions Tend To Be Suboptimal

We’re a society obsessed with optimization — our phones have gotten faster, we can fit more data on smaller drives, our cars have better gas mileage. The list goes on. And we are all for it! But when it comes down to writing actual code and building software, we are often too obsessed with the optimal solution.

How Numbersbright Is Your Best Choice?

We all have our unique ways of getting things done. We each have our system for organizing information and getting work done, whether you work in a factory or the field. This is especially true for those of us who are data scientists.

Data science is a diverse field, but like most fields, it has a few fundamental practices that can help you be more successful. You don’t necessarily have to follow these steps to the letter, but your data science workflow will improve if you do many things.

· Manage Your Files

· Keep Track of Your Work

· Document Your Work

· Store Your Data Securely

Here are some of the ways that Numbersbright makes data science better for everyone:

· No coding is required.

· Work with messy data without cleaning it up first.

· Automatically generate a simple report using your data set as input.

· Generate a wide range of insights from your data set in one go, rather than running a new analysis each time you want to test a new hypothesis.

· Visualize and share your results in seconds.