There’s one thing you can say about data science — it’s a lot of things. Data science isn’t just one single discipline, skill set, or methodology. Due to this, data science is considered an ‘interdisciplinary branch’ of science because it incorporates mathematics, human behavior analysis, and workflow studies, logic systems, and algorithms as a core part.
Data scientists need to understand business and be flexible super-performers. More than just data analytics, more than just big data insight, more than just handling raw unstructured data streams, and more than just being able to drive a database blindfolded, they also need to be able to handle new streams of raw unstructured data. In what ways can a data scientist be successful?
Table of Contents
1. Stay up to date with technology
Almost all of today’s data science technologies emerged in the last decade or so.
If you go into data science with the intention of taking it seriously, you’ve relegated yourself to a lifetime of constant learning. Try Python if you’ve worked with MATLAB all your life, or try Plotly for something new if you’ve used Matplotlib for visualizations.
Find out which technologies are relevant by reading blog posts, and pick a couple you want to add to your stack every week. Experiment with new technologies for an hour every week (or as much time as you can spare). Create some personal projects to learn how to use the new technologies to the best of your ability.
2. Maintain documentation
All the good programmers are the ones who provide clear and concise documentation to support their work and fill their programs with useful comments to describe what certain lines of code do. This is especially important for data scientists who use complex algorithms and machine learning models to solve problems.
Take the time to read good code documentation or articles on how to write good code documentation. To practice, write documentation for your old personal projects or spend some time reviewing the documentation for your current project. Since most of the data science world runs on Python, check out this well-written article on how to document your Python code.
3. Become a part of data science communities
The stereotype that developers are social outcasts to write code aimed at world domination is an outdated generalization that doesn’t reflect the modern complexity of the tech industry. So much has changed.
The complexity of data science means that a large support network of specialists within and outside the data science community is required to solve the many different problems that bring different data scientists together.
However, the importance of a community is not limited to the professional sphere. As the field of data science grows, the aid paves the way for future analysts and engineers to get influenced and prepare them to create an impact in the years to come.
The only way to bring about the necessary change is to create a community-wide movement that catalyzes change for the betterment of the industry.
Become a mentor, create blog posts, join online data science forums and help solve problems, start a YouTube channel, share your experiences, participate in hackathons, or develop online courses to help future generations data scientists to learn the in and out of data science.
4. Make it a habit to refactor your code
Refactoring is the process of cleaning up code without changing its original functionality. Although refactoring is a process born out of necessity in software development situations, refactoring can be a useful habit for data scientists.
Examine the old code and ask if the same code could be written more efficiently. If so, spend some time learning coding best practices and finding ways to shorten, improve, and clean up your code. Check out this great article that describes code refactoring best practices.
5. Understand business problems
Data science consists of 75% understanding business problems and 25% writing models to solve them.
Developing algorithms, coding, and mathematics is easy. Implementing them so they solve specific business problems is more challenging. The rest of the process will be much smoother if you take the time to understand the business problem and the objectives you hope to achieve.
In order to gain a deeper understanding of the problems facing the industry in which you work, you need to conduct some research and gather some context with which to support your knowledge of the problems. An engineering firm, for example, needs to know what makes its customers tick, or what its specific goals are.
Do research on the specific goals of the organization and any challenges it may be facing in its current market. Include in your cheat sheet what algorithms might help to solve existing challenges, or machine learning models that you think will be useful in the future. Add ideas to your cheat sheet as you think of them, and soon enough you’ll have a treasure trove of information custom-tailored to your unique situation.
6. Optimize your workspace, tools and workflow
Despite the availability of many productivity-enhancing extensions for IDEs, some people have not yet optimized their workflows.
It really comes down to which tools, workspaces, and workflows make you the most efficient and effective data scientist.
Determine where you could improve your effectiveness and efficiency once a year (or more often if that works better for you). It might mean working on your machine learning algorithms in the morning, sitting on an exercise ball instead of a chair, or adding a linting extension to your IDE. You should experiment with different workspaces, tools, and workflows until you find your ideal one.
7. Be a minimalist
Be a minimalist in your code and your workflow. The best data scientists use the simplest algorithms, the smallest amount of code, and the least amount of data.
Sometimes when people discuss minimalism in code, they try to come up with outrageous solutions that use only a few lines of code.
Once you become familiar with data science concepts, look for ways to make your code simpler, cleaner, and shorter. Use simple algorithms and create reusable functions to reduce redundancy.
The more you advance as a data scientist, the more you will push yourself to write more efficient solutions, write fewer lines of code, and use simpler algorithms and models. Leave plenty of comments to explain how contracted versions of code work, and learn how to shorten your code without reducing its effectiveness.
8. Self-development should be your priority
Today, everyone is obsessed with upskilling, and for good reason. Data scientists should not be exempt from this trend.
Create an inventory of your skills and see how you match up with the requirements of job postings. Do you have experience with Python-related libraries, such as Keras, NumPy, Pandas, PyTorch, TensorFlow, Matplotlib, Seaborn, and Plotly? Have you written memos detailing what you’ve learned? Are you comfortable working in teams to complete tasks? If not, are there online courses or resources that will help you improve in those areas?
9. Apply functions to eliminate complexity and redundancy
The best developers tend to be lazy developers because they figure out how to create solutions that don’t require much effort. Don’t forget the importance of functions when writing code. Go back and bundle redundant or complex code into functions after you’ve written a solution to simplify and organize it.
10. Opt for test-driven methods
In test-driven development (TDD), code is written with incremental improvements that are constantly tested as a part of the development process. It uses the “Red, Green, Refactor” system to encourage developers to build a test suite, write implementation code, and then optimize the codebase.
Data scientists can use TDD to produce analytics pipelines, develop proof-of-concepts, work with subsets of data, and ensure that working code is not broken during development.
Determine whether or not test-driven development can be useful to your workflow. TDD isn’t the perfect solution to every problem, but it can be useful when implemented thoughtfully. This article describes TDD in great detail and gives an example of how to implement it into data science projects.
11. Try small, frequent commits
Have you ever made a pull request and had your computer blow up with errors and issues?
Take a breath when you feel like introducing the person who made such a big commitment to your fist and remember that this person didn’t practice good habits growing up.
Making small, frequent commits is the golden rule of team-based software development.
Get into the practice of frequently committing your code changes and making pull requests to get the latest code. Since every change you or someone else makes could break the whole project, it’s best to make small changes that can be reverted easily and likely only affect one part or layer.
12. Start a project with an end in mind
To effectively relate this to data science projects, you need to ask yourself in the planning phase of a project what the desired outcome of the project is. By doing so, you will be able to plan the path of the project and get a roadmap of the outcomes you need to accomplish. Also, you will be able to determine if the project is feasible and sustainable based on its outcome.
Start each project with a planning session that examines what you hope to gain from development. Determine which problem you will be trying to solve or which piece of evidence you are trying to gather. Then, answer feasibility and sustainability questions that will shape your milestones and outcomes. After that, you can develop code and machine learning models with a clear outline in place to guide you the end goal of your project.
13. Become a storyteller
As a storyteller, you must understand the story you are trying to tell in order to be successful. That is, it is your responsibility to understand so that you can be understood. Developing this habit early on of understanding what you’re trying to accomplish, so that you can share it with someone else to a fair level of comprehension, will make you the most effective data scientist in the room.
The Feynman Technique helps you gain a comprehensive understanding of the concepts and problems you’re seeking to solve. In short, this method aligns with the data science process of analyzing data and explaining the results to non-data science stakeholders. In short, you refine your explanation of the topic to such a point that you can explain it in simple, non-jargon terms that anyone can understand.
14. Read more research papers
Industry news and insights are often shared through research papers.
We can learn from research papers how others solve problems, broaden our perspectives, and keep up with the latest trends by reading them.
Decide on one topic to study in depth every week. It’s helpful to let your colleagues know what you’re working on so they can give you feedback or support. You’ll retain more information if you take lecture-style notes while reading the research paper, and if you do not understand a concept, it is good to assume you don’t understand it and go back and re-read that section of the text until you do. If at all possible, try implementing something from the reading in a personal project or work meeting.
15. Embrace change
Don’t be a data scientist who isn’t willing to change, because the world of data science is changing rapidly.
Being open to change not only forces you to improve as a professional but also keeps you relevant in an industry that changes rapidly.
When you find a new technology or process making headlines, give it a try. Even if it’s just to read the documentation – you can keep up-to-date on changing trends in your industry. Acting as someone who is in the know can help guide your company through technological changes and advances, while also helping you stay ahead of the curve.
If you develop good habits at any stage of your data science career, you will be able to achieve your potential as an effective data scientist who can make a significant contribution to solving any problem you encounter. If you want to set yourself up for future success, there is no better time than now.
[mailerlite_form form_id=1]
