The Most Important Data Science Project
What is the project every data scientist must do?
Recently, I have been plagued with one particular question from various people — “What shall I do after completing a MOOC in Data Science?”. Of-course, the most obvious answer is to use the skills you’ve just learnt and the best way to do that is with a project, but even that can be complicated because the next question would probably be “what type of projects should I do?”
Hence, I took some time to think about this before answering and would like to say a massive thank you for those that have waited for me to think this through. In my place of solitude I had an epiphany. The most important Data Science project is allowing the world know what you can do—how you can add more value. Ergo, after completion of any Data Science MOOC, start your journey on showing the world what you are capable of, you may begin with your newly crafted skills.https://www.kdnuggets.com/wp-admin/edit.php?post_type=acf
“Our lives is a constant sales pitch, we should be deliberate in allowing everyone else know what we can do to add value to them.“
This may sound quite broad, and in truth, it is. Like in any Data Science project, you would need to constantly iterate on how you communicate as there are so many ways to communicate your value to the marketplace in which simply stating “Do Projects” does not capture.
Additionally, We can never know the optimal solution from the beginning of a Data Science project, nor should we expect to know the best way to communicate our skills unless we try. Yet, with each solution, we discover more about ourselves and we can build solutions that focus on correcting the errors we are making. Besides, whether it is arriving where we picture ourselves, or being of mass value to the marketplace, it is most likely going to require the outputs from former solutions to ensemble all of the lessons you learned along the way, just like most winning solutions for Data Science competitions.
It simply falls down your preferred medium of communication — Make it fun.
In my opinion, I believe this way of seeing things alleviates stress from the mind of the aspiring Data Scientist because the focus is on your own development. The incentive is to become better so that you may offer more to the market place rather than chasing job opportunities, which by the way would eventually begin to come to you as you showcase what you have to offer to the marketplace.
Note: Remember that the healthiest relationships suit both parties. Both sides win! You want a large salary, the world needs competent problem solvers. If you want a major breakthrough, be of major use.
Showcasing your Data Science Skills
As stated above, there are many ways to showcase your Data Science skills, possibly more than I can think of. Your job is to select 2–3 and begin working at each. Without further ado…
Journal writing is an excellent method to keep tabs on your experience and thoughts. In the long run journal writing can be used to identify future goals and aims. In a Data Science environment, we can use journal writing to track our:
- Thought Processes
Yes, all of this is valuable information and if it allows you to track your progress and project yourself into the future, it also does wonders as a tool to showcase your ability. People in front of you can see where you are going and provide assistance to help you reach the next point in your career faster, as well as lay off some of their responsibilities to you while they focus on task that will take them forward. Additionally, those behind you can learn and be inspired from what you’ve left behind. Regardless of how you look at it, in both scenarios you are useful!
Not only do projects allow you to learn new technologies and reach outside of your comfort zone, it also gives you an experience of what the workflow will be like in a real project environment if done properly. Furthermore, you are directly displaying what you can do and if it suits what someones needs, they’ll come and get you.
I have 2 things to say about doing this method. Firstly, leave the Titanic challenge alone… Please. Secondly, maintain the project. Don’t just do it until “it works” and stop. Continue to build and develop your project, add functionality, make it come to life. If you’ve created a Dog Classifier, build something (i.e. an app, website, etc) that allows the project to come out of your terminal.
This can go hand in hand with your projects in a sense that you may share your trail of thought behind your solution. Another good example would be to summarize other people’s work, It’s good for engagement and you may (more than likely) learn a thing or two. For instance, taking the summary of other people’s work idea; there are many research papers released each week and you can decide to summarize 2 or 3 as they are released.
For those unfamiliar with case studies, it can be described as “an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables.” (Source: Big Data Made Simple).
Simply, start with a problem, outline the various solutions that are available then offer proven results that showcase your solution as optimal. Isn’t optimal solutions what all businesses need? Case Studies are effective in business and are effective for you — some examples below.
An Example Case study
How we scaled data science to all sides of Airbnb over 5 years of hypergrowth
Five years ago, I joined Airbnb as its first data scientist. At that time, the few people who'd even heard of the...
A presentation is the process of presenting a topic to an audience. It is typically a demonstration, introduction, lecture, or speech meant to inform, persuade, inspire, motivate, or to build good will or to present a new idea or product (Source: Wikipedia).
Why is this useful?
You get to demonstrate your understanding of a topic to others and it also is a display of soft skills which are very much as useful as technical abilities for a Data Scientist.
Well if you’re reading this story, you can probably guess one of the options I’ve chosen. Blogging has been absolutely amazing for me (especially during lockdown). Thinking of what to write about drives you to want to learn more and when you have to flesh out a technical subject, you’re a little bit more attentive to the fine details which allows you to learn quicker — I can testify to that. Also, you can build up your reputation as a subject matter expert — Oh and did I add that you can earn extra income?
“If you can’t explain it simply, you don’t understand it well enough”. — Richard Feynman
There is absolutely no reason why you cannot start today. Platforms like Youtube and Medium has made this market extremely accessible and if you’ve learnt something new, it’s likely that someone else may want to know. Don’t ever feel that because someone else has done something already that you cannot — it’s also a good idea to build on work of others.
I’ve heard this one so many times with Kaggle being the first name that comes to mind whenever Data Science competitions are mentioned. There have been so many debates about whether is useful for real-world Data Science of which I really do not want to get into. The important factor is that to do well you’d have to apply techniques (some of which are applied in the real-world) and this serves as a form of practical learning and if you do well it speaks volumes of your character.
Yes, it is true that their are some differences between Competitions and real-world Data Science workflows, but when we talk about approaching the problem then this serves as the closest thing that you’ll get to the real world (Unless you decide to do your own project of-course).
Showcasing your value is a life project that every human must part-take in, instead of doing a project to merely bag your next job after completing a MOOC, decide to commit to something that in the long-term will continuously allow you to add value to yourself. Rather than blindly going through life hoping that the next thing you do will open all the doors to you, be deliberate and consistent in showcasing your capabilities to increase your odds of getting a major break through.
There are many ways to showcase the skills you’ve learnt. If you feel that I have missed something out, leave a response of what it is and provide an explanation of how it’s useful.
Kurtis Pykes - AI Writer - Towards Data Science | LinkedIn
View Kurtis Pykes' profile on LinkedIn, the world's largest professional community. Kurtis has 1 job listed on their...
If you like this story you may also like:
- How to Secure a Data Science Role You Actually Want
The must do’s that aren’t spoken of enough when taking on roles relating to Artificial Intelligence
- Staying Motivated For Your Data Science Career
We Can Stay Motivated for the Long Run.
- How to Learn Faster for Data Scientist
An effective strategy for Learning to Learn
- The Reason You’re Frustrated when Trying to Become a Data Scientist
The hidden skill that separates the best from the rest
Bio: Kurtis Pykes is a Machine Learning Engineer Intern at Codehouse. He is passionate about harnessing the power of machine learning and data science to help people become more productive and effective. Follow his Medium blog: https://link.medium.com/2tFtAhN7d7.
Original. Reposted with permission.
- How to Optimize Your CV for a Data Scientist Career
- Here is What I’ve Learned in 2 Years as a Data Scientist
- The Uncommon Data Science Job Guide
Top Stories Past 30 Days